Alan Turing's Legacy: Info-Computational Philosophy of Nature

Abstract: Alan Turing's pioneering work on computability, and his ideas on morphological computing support Andrew Hodges' view of Turing as a natural philosopher. Turing's natural philosophy differs importantly from Galileo's view that the book of nature is written in the language of mathematics (The Assayer, 1623). Computing is more than a language of nature as computation produces real time physical behaviors. This article presents the framework of Natural Info-computationalism as a contemporary natural philosophy that builds on the legacy of Turing's computationalism. Info-computationalism is a synthesis of Informational Structural Realism (the view that nature is a web of informational structures) and Natural Computationalism (the view that nature physically computes its own time development). It presents a framework for the development of a unified approach to nature, with common interpretation of inanimate nature as well as living organisms and their social networks. Computing is understood as information processing that drives all the changes on different levels of organization of information and can be modeled as morphological computing on data sets pertinent to informational structures. The use of infocomputational conceptualizations, models and tools makes possible for the first time in history the study of complex selforganizing adaptive systems, including basic characteristics and functions of living systems, intelligence, and cognition.


Axiomatic Tools versus Constructive approach to Unconventional Algorithms

Abstract: In this paper, we analyze axiomatic issues of unconventional computations from a methodological and philosophical point of view. We explain how the new models of algorithms changed the algorithmic universe, making it open and allowing increased flexibility and creativity. However, the greater power of new types of algorithms also brought the greater complexity of the algorithmic universe, demanding new tools for its study. That is why we analyze new powerful tools brought forth by the axiomatic theory of algorithms, automata and computation.


TCP Congestion Control Scheme for Wireless Networks based on TCP Reserved Field and SNR Ratio

Abstract: Currently, TCP is the most popular and widely used network transmission protocol. In actual fact, about 90% of connections on the internet use TCP to communicate. Through several upgrades and improvements, TCP became well optimized for the very reliable wired networks. As a result, TCP considers all packet timeouts in wired networks as due to network congestion and not to bit errors. However, with networking becoming more heterogeneous, providing wired as well as wireless topologies, TCP suffers from performance degradation over error-prone wireless links as it has no mechanism to differentiate error losses from congestion losses. It therefore considers all packet losses as due to congestion and consequently reduces the burst of packet, diminishing at the same time the network throughput. This paper proposes a new TCP congestion control scheme appropriate for wireless as well as wired networks and is capable of distinguishing congestion losses from error losses. The proposed scheme is based on using the reserved field of the TCP header to indicate whether the established connection is over a wired or a wireless link. Additionally, the proposed scheme leverages the SNR ratio to detect the reliability of the link and decide whether to reduce packet burst or retransmit a timed-out packet. Experiments conducted, revealed that the proposed scheme proved to behave correctly in situations where timeouts were due to error and not to congestion. Future work can improve upon the proposed scheme so much so that it can leverage CRC and HEC errors so as to better determine the cause of transmission timeouts in wireless networks.


A Fast Projected Fixed-Point Algorithm for Large Graph Matching

Abstract: We propose a fast approximate algorithm for large graph matching. A new projected fixed-point method is defined and a new doubly stochastic projection is adopted to derive the algorithm. Previous graph matching algorithms suffer from high computational complexity and therefore do not have good scalability with respect to graph size. For matching two weighted graphs of $n$ nodes, our algorithm has time complexity only $O(n^3)$ per iteration and space complexity $O(n^2)$. In addition to its scalability, our algorithm is easy to implement, robust, and able to match undirected weighted attributed graphs of different sizes. While the convergence rate of previous iterative graph matching algorithms is unknown, our algorithm is theoretically guaranteed to converge at a linear rate. Extensive experiments on large synthetic and real graphs (more than 1,000 nodes) were conducted to evaluate the performance of various algorithms. Results show that in most cases our proposed algorithm achieves better performance than previous state-of-the-art algorithms in terms of both speed and accuracy in large graph matching. In particular, with high accuracy, our algorithm takes only a few seconds (in a PC) to match two graphs of 1,000 nodes.


Sparse Suffix Tree Construction with Small Space

Abstract: We consider the problem of constructing a sparse suffix tree (or suffix array) for $b$ suffixes of a given text $T$ of size $n$, using only $O(b)$ words of space during construction time. Breaking the naive bound of $\Omega(nb)$ time for this problem has occupied many algorithmic researchers since a different structure, the (evenly spaced) sparse suffix tree, was introduced by K{\"a}rkk{\"a}inen and Ukkonen in 1996. While in the evenly spaced sparse suffix tree the suffixes considered must be evenly spaced in $T$, here there is no constraint on the locations of the suffixes. We show that the sparse suffix tree can be constructed in $O(n\log^2b)$ time. To achieve this we develop a technique, which may be of independent interest, that allows to efficiently answer $b$ longest common prefix queries on suffixes of $T$, using only $O(b)$ space. We expect that this technique will prove useful in many other applications in which space usage is a concern. Furthermore, additional tradeoffs between the space usage and the construction time are given.


Background Subtraction for Online Calibration of Baseline RSS in RF Sensing Networks

Abstract: Radio frequency (RF) sensing networks are a class of wireless sensor networks (WSNs) which use RF signals to accomplish tasks such as passive device-free localization and tracking. The algorithms used for these tasks usually require access to measurements of baseline received signal strength (RSS) on each link. However, it is often impossible to collect this calibration data (measurements collected during an offline calibration period when the region of interest is empty of targets). We propose adapting background subtraction methods from the field of computer vision to estimate baseline RSS values from measurements taken while the system is online and obstructions may be present. This is done by forming an analogy between the intensity of a background pixel in an image and the baseline RSS value of a WSN link and then translating the concepts of temporal similarity, spatial similarity and spatial ergodicity which underlie specific background subtraction algorithms to WSNs. Using experimental data, we show that these techniques are capable of estimating baseline RSS values with enough accuracy that RF tomographic tracking can be carried out in a variety of different environments without the need for a calibration period.


The Longest Queue Drop Policy for Shared-Memory Switches is 1.5-competitive

Abstract: We consider the Longest Queue Drop memory management policy in shared-memory switches consisting of $N$ output ports. The shared memory of size $M\geq N$ may have an arbitrary number of input ports. Each packet may be admitted by any incoming port, but must be destined to a specific output port and each output port may be used by only one queue. The Longest Queue Drop policy is a natural online strategy used in directing the packet flow in buffering problems. According to this policy and assuming unit packet values and cost of transmission, every incoming packet is accepted, whereas if the shared memory becomes full, one or more packets belonging to the longest queue are preempted, in order to make space for the newly arrived packets. It was proved in 2001 [Hahne et al., SPAA '01] that the Longest Queue Drop policy is 2-competitive and at least $\sqrt{2}$-competitive. It remained an open question whether a (2-\epsilon) upper bound for the competitive ratio of this policy could be shown, for any positive constant \epsilon. We show that the Longest Queue Drop online policy is 1.5-competitive.


Modular Arithmetic Expressions and Primality Testing via DNA Self-Assembly

Abstract: Self-assembly is a fundamental process by which supramolecular species form spontaneously from their components. This process is ubiquitous throughout the life chemistry and is central to biological information processing. Algorithms for solving many mathematical and computational problems via tile self assembly have been proposed by many researchers in the last decade. In particular tile set for doing basic arithmetic of two inputs have been given. In this work we give tile set for doing basic arithmetic (addition, subtraction, multiplication) of n inputs and subsequently computing its modulo. We also present a tile set for primality testing. Finally we present a software 'xtilemod' for doing modular arithmetic. This simplifies the task of creating the input files to xgrow simulator for doing basic (addition, subtraction, multiplication and division) as well as modular arithmetic of n inputs. Similar software for creating tile set for primality testing is also given.


A Comprehensive Model to achieve Service Reusability for Multi level stakeholders using Non-Functional attributes of Service Oriented Architecture

Abstract: SOA is a prominent paradigm for accomplishing reuse of services. Service reusability is one dominant factor which has a greater influence on achieving quality in SOA systems. There exists sufficient research in this area and researchers have contributed many works towards achieving quality in SOA systems but much emphasis was not provided on service reusability [1] [2] [3]. Few authors have addressed reusability factor with limited non-functional attributes. Our study focuses on identifying the non-functional attributes which have major or greater influence towards obtaining reusability in SOA systems. The objective of this study goes into the next level, to categorize the non-functional attributes on multi stakeholder's perspective i.e. Service Consumer, Service Provider and Service Developer which paves the way to build a comprehensive quality model for achieving Service Reusability


Dynamic Priority Queue: An SDRAM Arbiter With Bounded Access Latencies for Tight WCET Calculation

Abstract: This report introduces a shared resource arbitration scheme "DPQ - Dynamic Priority Queue" which provides bandwidth guarantees and low worst case latency to each master in an MPSoC. Being a non-trivial candidate for timing analysis, SDRAM has been chosen as a showcase, but the approach is valid for any shared resource arbitration. Due to its significant cost, data rate and physical size advantages, SDRAM is a potential candidate for cost sensitive, safety critical and space conserving systems. The variable access latency is a major drawback of SDRAM that induces largely over estimated Worst Case Execution Time (WCET) bounds of applications. In this report we present the DPQ together with an algorithm to predict the shared SDRAM's worst case latencies. We use the approach to calculate WCET bounds of six hardware tasks executing on an Altera Cyclone III FPGA with shared DDR2 memory. The results show that the DPQ is a fair arbitration scheme and produces low WCET bounds.


On the toggling-branching recurrence of Computability Logic

Abstract: We introduce a new, substantially simplified version of the toggling-branching recurrence operation of Computability Logic, prove its equivalence to Japaridze's old, "canonical" version, and also prove that both versions preserve the static property of their arguments.


Higher-Order Partial Least Squares (HOPLS): A Generalized Multi-Linear Regression Method

Abstract: A new generalized multilinear regression model, termed the Higher-Order Partial Least Squares (HOPLS), is introduced with the aim to predict a tensor (multiway array) $\tensor{Y}$ from a tensor $\tensor{X}$ through projecting the data onto the latent space and performing regression on the corresponding latent variables. HOPLS differs substantially from other regression models in that it explains the data by a sum of orthogonal Tucker tensors, while the number of orthogonal loadings serves as a parameter to control model complexity and prevent overfitting. The low dimensional latent space is optimized sequentially via a deflation operation, yielding the best joint subspace approximation for both $\tensor{X}$ and $\tensor{Y}$. Instead of decomposing $\tensor{X}$ and $\tensor{Y}$ individually, higher order singular value decomposition on a newly defined generalized cross-covariance tensor is employed to optimize the orthogonal loadings. A systematic comparison on both synthetic data and real-world decoding of 3D movement trajectories from electrocorticogram (ECoG) signals demonstrate the advantages of HOPLS over the existing methods in terms of better predictive ability, suitability to handle small sample sizes, and robustness to noise.


Generalizing Redundancy in Propositional Logic: Foundations and Hitting Sets Duality

Abstract: Detection and elimination of redundant clauses from propositional formulas in Conjunctive Normal Form (CNF) is a fundamental problem with numerous application domains, including AI, and has been the subject of extensive research. Moreover, a number of recent applications motivated various extensions of this problem. For example, unsatisfiable formulas partitioned into disjoint subsets of clauses (so-called groups) often need to be simplified by removing redundant groups, or may contain redundant variables, rather than clauses. In this report we present a generalized theoretical framework of labelled CNF formulas that unifies various extensions of the redundancy detection and removal problem and allows to derive a number of results that subsume and extend previous work. The follow-up reports contain a number of additional theoretical results and algorithms for various computational problems in the context of the proposed framework.


Efficient computation of exact solutions for quantitative model checking

Abstract: Quantitative model checkers for Markov Decision Processes typically use finite-precision arithmetic. If all the coefficients in the process are rational numbers, then the model checking results are rational, and so they can be computed exactly. However, exact techniques are generally too expensive or limited in scalability. In this paper we propose a method for obtaining exact results starting from an approximated solution in finite-precision arithmetic. The input of the method is a description of a scheduler, which can be obtained by a model checker using finite precision. Given a scheduler, we show how to obtain a corresponding basis in a linear-programming problem, in such a way that the basis is optimal whenever the scheduler attains the worst-case probability. This correspondence is already known for discounted MDPs, we show how to apply it in the undiscounted case provided that some preprocessing is done. Using the correspondence, the linear-programming problem can be solved in exact arithmetic starting from the basis obtained. As a consequence, the method finds the worst-case probability even if the scheduler provided by the model checker was not optimal. In our experiments, the calculation of exact solutions from a candidate scheduler is significantly faster than the calculation using the simplex method under exact arithmetic starting from a default basis.


Locally Stable Marriage with Strict Preferences

Abstract: We study stable matching problems with locality of information and control. In our model, each agent is a node in a fixed network and strives to be matched to another agent. An agent has a complete preference list over all other agents it can be matched with. Agents can match arbitrarily, and they learn about possible partners dynamically based on their current neighborhood. We consider convergence of dynamics to locally stable matchings -- states that are stable with respect to their imposed information structure in the network. In the two-sided case of stable marriage in which existence is guaranteed, we show that the existence of a path to stability becomes NP-hard to decide. This holds even when the network exists only among one partition of agents. In contrast, if one partition has no network and agents remember a previous match every round, a path to stability is guaranteed and random dynamics converge with probability 1. We characterize this positive result in various ways. For instance, it holds for random memory and for cache memory with the most recent partner, but not for cache memory with the best partner. Also, it is crucial which partition of the agents has memory. Finally, we present results for centralized computation of locally stable matchings, i.e., computing maximum locally stable matchings in the two-sided case and deciding existence in the roommates case.


The number of distinct distances from a vertex of a convex polygon

Abstract: Erd\H{o}s conjectured in 1946 that every n-point set P in convex position in the plane contains a point that determines at least floor(n/2) distinct distances to the other points of P. The best known lower bound due to Dumitrescu (2006) is 13n/36 - O(1). In the present note, we slightly improve on this result to (13/36 + eps)n - O(1) for eps ~= 1/23000. Our main ingredient is an improved bound on the maximum number of isosceles triangles determined by P.


Synthesizing Robust Systems with RATSY

Abstract: Specifications for reactive systems often consist of environment assumptions and system guarantees. An implementation should not only be correct, but also robust in the sense that it behaves reasonably even when the assumptions are (temporarily) violated. We present an extension of the requirements analysis and synthesis tool RATSY that is able to synthesize robust systems from GR(1) specifications, i.e., system in which a finite number of safety assumption violations is guaranteed to induce only a finite number of safety guarantee violations. We show how the specification can be turned into a two-pair Streett game, and how a winning strategy corresponding to a correct and robust implementation can be computed. Finally, we provide some experimental results.


UPPAAL-SMC: Statistical Model Checking for Priced Timed Automata

Abstract: This paper offers a survey of uppaalsmc, a major extension of the real-time verification tool uppaal. uppaalsmc allows for the efficient analysis of performance properties of networks of priced timed automata under a natural stochastic semantics. In particular, uppaalsmc relies on a series of extensions of the statistical model checking approach generalized to handle real-time systems and estimate undecidable problems. uppaalsmc comes together with a friendly user interface that allows a user to specify complex problems in an efficient manner as well as to get feedback in the form of probability distributions and compare probabilities to analyze performance aspects of systems. The focus of the survey is on the evolution of the tool - including modeling and specification formalisms as well as techniques applied - together with applications of the tool to case studies.


Controllers with Minimal Observation Power (Application to Timed Systems)

Abstract: We consider the problem of controller synthesis under imperfect information in a setting where there is a set of available observable predicates equipped with a cost function. The problem that we address is the computation of a subset of predicates sufficient for control and whose cost is minimal. Our solution avoids a full exploration of all possible subsets of predicates and reuses some information between different iterations. We apply our approach to timed systems. We have developed a tool prototype and analyze the performance of our optimization algorithm on two case studies.


Simple Deterministic Algorithms for Fully Dynamic Maximal Matching

Abstract: A maximal matching can be maintained in fully dynamic (supporting both addition and deletion of edges) $n$-vertex graphs using a trivial deterministic algorithm with a worst-case update time of O(n). No deterministic algorithm that outperforms the na\"{\i}ve O(n) one was reported up to this date. The only progress in this direction is due to Ivkovi\'{c} and Lloyd \cite{IL93}, who in 1993 devised a deterministic algorithm with an \emph{amortized} update time of $O((n+m)^{\sqrt{2}/2})$, where $m$ is the number of edges. In this paper we show the first deterministic fully dynamic algorithm that outperforms the trivial one. Specifically, we provide a deterministic \emph{worst-case} update time of $O(\sqrt{m})$. Moreover, our algorithm maintains a matching which is in fact a 3/2-approximate maximum cardinality matching (MCM). We remark that no fully dynamic algorithm for maintaining $(2-\eps)$-approximate MCM improving upon the na\"{\i}ve O(n) was known prior to this work, even allowing amortized time bounds and \emph{randomization}. For low arboricity graphs (e.g., planar graphs and graphs excluding fixed minors), we devise another simple deterministic algorithm with \emph{sub-logarithmic update time}. Specifically, it maintains a fully dynamic maximal matching with amortized update time of $O(\log n/\log \log n)$. This result addresses an open question of Onak and Rubinfeld \cite{OR10}. We also show a deterministic algorithm with optimal space usage, that for arbitrary graphs maintains a maximal matching in amortized $O(\sqrt{m})$ time, and uses only $O(n+m)$ space.


Probabilistically Safe Control of Noisy Dubins Vehicles

Abstract: We address the problem of controlling a stochastic version of a Dubins vehicle such that the probability of satisfying a temporal logic specification over a set of properties at the regions in a partitioned environment is maximized. We assume that the vehicle can determine its precise initial position in a known map of the environment. However, inspired by practical limitations, we assume that the vehicle is equipped with noisy actuators and, during its motion in the environment, it can only measure its angular velocity using a limited accuracy gyroscope. Through quantization and discretization, we construct a finite approximation for the motion of the vehicle in the form of a Markov Decision Process (MDP). We allow for task specifications given as temporal logic statements over the environmental properties, and use tools in Probabilistic Computation Tree Logic (PCTL) to generate an MDP control policy that maximizes the probability of satisfaction. We translate this policy to a vehicle feedback control strategy and show that the probability that the vehicle satisfies the specification in the original environment is bounded from below by the maximum probability of satisfying the specification on the MDP.


Identifying all abelian periods of a string in quadratic time and relevant problems

Abstract: Abelian periodicity of strings has been studied extensively over the last years. In 2006 Constantinescu and Ilie defined the abelian period of a string and several algorithms for the computation of all abelian periods of a string were given. In contrast to the classical period of a word, its abelian version is more flexible, factors of the word are considered the same under any internal permutation of their letters. We show two O(|y|^2) algorithms for the computation of all abelian periods of a string y. The first one maps each letter to a suitable number such that each factor of the string can be identified by the unique sum of the numbers corresponding to its letters and hence abelian periods can be identified easily. The other one maps each letter to a prime number such that each factor of the string can be identified by the unique product of the numbers corresponding to its letters and so abelian periods can be identified easily. We also define weak abelian periods on strings and give an O(|y|log(|y|)) algorithm for their computation, together with some other algorithms for more basic problems.


Advances on Matroid Secretary Problems: Free Order Model and Laminar Case

Abstract: The most well-known conjecture in the context of matroid secretary problems claims the existence of a constant-factor approximation applicable to any matroid. Whereas this conjecture remains open, modified forms of it were shown to be true, when assuming that the assignment of weights to the secretaries is not adversarial but uniformly random (Soto [SODA 2011], Oveis Gharan and Vondr\'ak [ESA 2011]). However, so far, there was no variant of the matroid secretary problem with adversarial weight assignment for which a constant-factor approximation was found. We address this point by presenting a 9-approximation for the \emph{free order model}, a model suggested shortly after the introduction of the matroid secretary problem, and for which no constant-factor approximation was known so far. The free order model is a relaxed version of the original matroid secretary problem, with the only difference that one can choose the order in which secretaries are interviewed. Furthermore, we consider the classical matroid secretary problem for the special case of laminar matroids. Only recently, a constant-factor approximation has been found for this case, using a clever but rather involved method and analysis (Im and Wang, [SODA 2011]) that leads to a 16000/3-approximation. This is arguably the most involved special case of the matroid secretary problem for which a constant-factor approximation is known. We present a considerably simpler and stronger $3\sqrt{3}e\approx 14.12$-approximation, based on reducing the problem to a matroid secretary problem on a partition matroid.


Optimization in a Self-Stabilizing Service Discovery Framework for Large Scale Systems

Abstract: Ability to find and get services is a key requirement in the development of large-scale distributed sys- tems. We consider dynamic and unstable environments, namely Peer-to-Peer (P2P) systems. In previous work, we designed a service discovery solution called Distributed Lexicographic Placement Table (DLPT), based on a hierar- chical overlay structure. A self-stabilizing version was given using the Propagation of Information with Feedback (PIF) paradigm. In this paper, we introduce the self-stabilizing COPIF (for Collaborative PIF) scheme. An algo- rithm is provided with its correctness proof. We use this approach to improve a distributed P2P framework designed for the services discovery. Significantly efficient experimental results are presented.


Cost Sensitive Reachability Heuristics for Handling State Uncertainty

Abstract: While POMDPs provide a general platform for non-deterministic conditional planning under a variety of quality metrics they have limited scalability. On the other hand, non-deterministic conditional planners scale very well, but many lack the ability to optimize plan quality metrics. We present a novel generalization of planning graph based heuristics that helps conditional planners both scale and generate high quality plans when using actions with nonuniform costs. We make empirical comparisons with two state of the art planners to show the benefit of our techniques.


Stable Independence in Perfect Maps

Abstract: With the aid of the concept of stable independence we can construct, in an efficient way, a compact representation of a semi-graphoid independence relation. We show that this representation provides a new necessary condition for the existence of a directed perfect map for the relation. The test for this condition is based to a large extent on the transitivity property of a special form of d-separation. The complexity of the test is linear in the size of the representation. The test, moreover, brings the additional benefit that it can be used to guide the early stages of network construction.


'Say EM' for Selecting Probabilistic Models for Logical Sequences

Abstract: Many real world sequences such as protein secondary structures or shell logs exhibit a rich internal structures. Traditional probabilistic models of sequences, however, consider sequences of flat symbols only. Logical hidden Markov models have been proposed as one solution. They deal with logical sequences, i.e., sequences over an alphabet of logical atoms. This comes at the expense of a more complex model selection problem. Indeed, different abstraction levels have to be explored. In this paper, we propose a novel method for selecting logical hidden Markov models from data called SAGEM. SAGEM combines generalized expectation maximization, which optimizes parameters, with structure search for model selection using inductive logic programming refinement operators. We provide convergence and experimental results that show SAGEM's effectiveness.


Of Starships and Klingons: Bayesian Logic for the 23rd Century

Abstract: Intelligent systems in an open world must reason about many interacting entities related to each other in diverse ways and having uncertain features and relationships. Traditional probabilistic languages lack the expressive power to handle relational domains. Classical first-order logic is sufficiently expressive, but lacks a coherent plausible reasoning capability. Recent years have seen the emergence of a variety of approaches to integrating first-order logic, probability, and machine learning. This paper presents Multi-entity Bayesian networks (MEBN), a formal system that integrates First Order Logic (FOL) with Bayesian probability theory. MEBN extends ordinary Bayesian networks to allow representation of graphical models with repeated sub-structures, and can express a probability distribution over models of any consistent, finitely axiomatizable first-order theory. We present the logic using an example inspired by the Paramount Series StarTrek.


A Differential Semantics of Lazy AR Propagation

Abstract: In this paper we present a differential semantics of Lazy AR Propagation (LARP) in discrete Bayesian networks. We describe how both single and multi dimensional partial derivatives of the evidence may easily be calculated from a junction tree in LARP equilibrium. We show that the simplicity of the calculations stems from the nature of LARP. Based on the differential semantics we describe how variable propagation in the LARP architecture may give access to additional partial derivatives. The cautious LARP (cLARP) scheme is derived to produce a flexible cLARP equilibrium that offers additional opportunities for calculating single and multidimensional partial derivatives of the evidence and subsets of the evidence from a single propagation. The results of an empirical evaluation illustrates how the access to a largely increased number of partial derivatives comes at a low computational cost.


Modifying Bayesian Networks by Probability Constraints

Abstract: This paper deals with the following problem: modify a Bayesian network to satisfy a given set of probability constraints by only change its conditional probability tables, and the probability distribution of the resulting network should be as close as possible to that of the original network. We propose to solve this problem by extending IPFP (iterative proportional fitting procedure) to probability distributions represented by Bayesian networks. The resulting algorithm E-IPFP is further developed to D-IPFP, which reduces the computational cost by decomposing a global EIPFP into a set of smaller local E-IPFP problems. Limited analysis is provided, including the convergence proofs of the two algorithms. Computer experiments were conducted to validate the algorithms. The results are consistent with the theoretical analysis.


Exploiting Evidence-dependent Sensitivity Bounds

Abstract: Studying the effects of one-way variation of any number of parameters on any number of output probabilities quickly becomes infeasible in practice, especially if various evidence profiles are to be taken into consideration. To provide for identifying the parameters that have a potentially large effect prior to actually performing the analysis, we need properties of sensitivity functions that are independent of the network under study, of the available evidence, or of both. In this paper, we study properties that depend upon just the probability of the entered evidence. We demonstrate that these properties provide for establishing an upper bound on the sensitivity value for a parameter; they further provide for establishing the region in which the vertex of the sensitivity function resides, thereby serving to identify parameters with a low sensitivity value that may still have a large impact on the probability of interest for relatively small parameter variations.


MAA*: A Heuristic Search Algorithm for Solving Decentralized POMDPs

Abstract: We present multi-agent A* (MAA*), the first complete and optimal heuristic search algorithm for solving decentralized partially-observable Markov decision problems (DEC-POMDPs) with finite horizon. The algorithm is suitable for computing optimal plans for a cooperative group of agents that operate in a stochastic environment such as multirobot coordination, network traffic control, `or distributed resource allocation. Solving such problems efiectively is a major challenge in the area of planning under uncertainty. Our solution is based on a synthesis of classical heuristic search and decentralized control theory. Experimental results show that MAA* has significant advantages. We introduce an anytime variant of MAA* and conclude with a discussion of promising extensions such as an approach to solving infinite horizon problems.


Models for Truthful Online Double Auctions

Abstract: Online double auctions (DAs) model a dynamic two-sided matching problem with private information and self-interest, and are relevant for dynamic resource and task allocation problems. We present a general method to design truthful DAs, such that no agent can benefit from misreporting its arrival time, duration, or value. The family of DAs is parameterized by a pricing rule, and includes a generalization of McAfee's truthful DA to this dynamic setting. We present an empirical study, in which we study the allocative-surplus and agent surplus for a number of different DAs. Our results illustrate that dynamic pricing rules are important to provide good market efficiency for markets with high volatility or low volume.


Local Utility Elicitation in GAI Models

Abstract: Structured utility models are essential for the effective representation and elicitation of complex multiattribute utility functions. Generalized additive independence (GAI) models provide an attractive structural model of user preferences, offering a balanced tradeoff between simplicity and applicability. While representation and inference with such models is reasonably well understood, elicitation of the parameters of such models has been studied less from a practical perspective. We propose a procedure to elicit GAI model parameters using only "local" utility queries rather than "global" queries over full outcomes. Our local queries take full advantage of GAI structure and provide a sound framework for extending the elicitation procedure to settings where the uncertainty over utility parameters is represented probabilistically. We describe experiments using a myopic value-of-information approach to elicitation in a large GAI model.


On the Value of Correlation

Abstract: Correlated equilibrium (Aumann, 1974) generalizes Nash equilibrium to allow correlation devices. Aumann showed an example of a game, and of a correlated equilibrium in this game, in which the agents' surplus (expected sum of payo s) is greater than their surplus in all mixed-strategy equilibria. Following the idea initiated by the price of anarchy literature (Koutsoupias & Papadimitriou, 1999;Papadimitriou, 2001) this suggests the study of two major measures for the value of correlation in a game with non-negative payoffs: 1. The ratio between the maximal surplus obtained in a correlated equilibrium to the maximal surplus obtained in a mixed-strategy equilibrium. We refer to this ratio as the mediation value. 2. The ratio between the maximal surplus to the maximal surplus obtained in a correlated equilibrium. We refer to this ratio as the enforcement value. In this work we initiate the study of the mediation and enforcement values, providing several general results on the value of correlation as captured by these concepts. We also present a set of results for the more specialized case of congestion games (Rosenthal,1973), a class of games that received a lot of attention in the recent literature.


A unified setting for inference and decision: An argumentation-based approach

Abstract: Inferring from inconsistency and making decisions are two problems which have always been treated separately by researchers in Artificial Intelligence. Consequently, different models have been proposed for each category. Different argumentation systems [2, 7, 10, 11] have been developed for handling inconsistency in knowledge bases. Recently, other argumentation systems [3, 4, 8] have been defined for making decisions under uncertainty. The aim of this paper is to present a general argumentation framework in which both inferring from inconsistency and decision making are captured. The proposed framework can be used for decision under uncertainty, multiple criteria decision, rule-based decision and finally case-based decision. Moreover, works on classical decision suppose that the information about environment is coherent, and this no longer required by this general framework.


Common Voting Rules as Maximum Likelihood Estimators

Abstract: Voting is a very general method of preference aggregation. A voting rule takes as input every voter's vote (typically, a ranking of the alternatives), and produces as output either just the winning alternative or a ranking of the alternatives. One potential view of voting is the following. There exists a 'correct' outcome (winner/ranking), and each voter's vote corresponds to a noisy perception of this correct outcome. If we are given the noise model, then for any vector of votes, we can


Hybrid Bayesian Networks with Linear Deterministic Variables

Abstract: When a hybrid Bayesian network has conditionally deterministic variables with continuous parents, the joint density function for the continuous variables does not exist. Conditional linear Gaussian distributions can handle such cases when the continuous variables have a multi-variate normal distribution and the discrete variables do not have continuous parents. In this paper, operations required for performing inference with conditionally deterministic variables in hybrid Bayesian networks are developed. These methods allow inference in networks with deterministic variables where continuous variables may be non-Gaussian, and their density functions can be approximated by mixtures of truncated exponentials. There are no constraints on the placement of continuous and discrete nodes in the network.


On Bayesian Network Approximation by Edge Deletion

Abstract: We consider the problem of deleting edges from a Bayesian network for the purpose of simplifying models in probabilistic inference. In particular, we propose a new method for deleting network edges, which is based on the evidence at hand. We provide some interesting bounds on the KL-divergence between original and approximate networks, which highlight the impact of given evidence on the quality of approximation and shed some light on good and bad candidates for edge deletion. We finally demonstrate empirically the promise of the proposed edge deletion technique as a basis for approximate inference.


On Privacy-Preserving Histograms

Abstract: We advance the approach initiated by Chawla et al. for sanitizing (census) data so as to preserve the privacy of respondents while simultaneously extracting "useful" statistical information. First, we extend the scope of their techniques to a broad and rich class of distributions, specifically, mixtures of highdimensional balls, spheres, Gaussians, and other "nice" distributions. Second, we randomize the histogram constructions to preserve spatial characteristics of the data, allowing us to approximate various quantities of interest, e.g., cost of the minimum spanning tree on the data, in a privacy-preserving fashion.


Exploiting Evidence in Probabilistic Inference

Abstract: We define the notion of compiling a Bayesian network with evidence and provide a specific approach for evidence-based compilation, which makes use of logical processing. The approach is practical and advantageous in a number of application areas-including maximum likelihood estimation, sensitivity analysis, and MAP computations-and we provide specific empirical results in the domain of genetic linkage analysis. We also show that the approach is applicable for networks that do not contain determinism, and show that it empirically subsumes the performance of the quickscore algorithm when applied to noisy-or networks.


Counterexample-guided Planning

Abstract: Planning in adversarial and uncertain environments can be modeled as the problem of devising strategies in stochastic perfect information games. These games are generalizations of Markov decision processes (MDPs): there are two (adversarial) players, and a source of randomness. The main practical obstacle to computing winning strategies in such games is the size of the state space. In practice therefore, one typically works with abstractions of the model. The diffculty is to come up with an abstraction that is neither too coarse to remove all winning strategies (plans), nor too fine to be intractable. In verification, the paradigm of counterexample-guided abstraction refinement has been successful to construct useful but parsimonious abstractions automatically. We extend this paradigm to probabilistic models (namely, perfect information games and, as a special case, MDPs). This allows us to apply the counterexample-guided abstraction paradigm to the AI planning problem. As special cases, we get planning algorithms for MDPs and deterministic systems that automatically construct system abstractions.


Use of Dempster-Shafer Conflict Metric to Detect Interpretation Inconsistency

Abstract: A model of the world built from sensor data may be incorrect even if the sensors are functioning correctly. Possible causes include the use of inappropriate sensors (e.g. a laser looking through glass walls), sensor inaccuracies accumulate (e.g. localization errors), the a priori models are wrong, or the internal representation does not match the world (e.g. a static occupancy grid used with dynamically moving objects). We are interested in the case where the constructed model of the world is flawed, but there is no access to the ground truth that would allow the system to see the discrepancy, such as a robot entering an unknown environment. This paper considers the problem of determining when something is wrong using only the sensor data used to construct the world model. It proposes 11 interpretation inconsistency indicators based on the Dempster-Shafer conflict metric, Con, and evaluates these indicators according to three criteria: ability to distinguish true inconsistency from sensor noise (classification), estimate the magnitude of discrepancies (estimation), and determine the source(s) (if any) of sensing problems in the environment (isolation). The evaluation is conducted using data from a mobile robot with sonar and laser range sensors navigating indoor environments under controlled conditions. The evaluation shows that the Gambino indicator performed best in terms of estimation (at best 0.77 correlation), isolation, and classification of the sensing situation as degraded (7% false negative rate) or normal (0% false positive rate).


Nonparametric Bayesian Logic

Abstract: The Bayesian Logic (BLOG) language was recently developed for defining first-order probability models over worlds with unknown numbers of objects. It handles important problems in AI, including data association and population estimation. This paper extends BLOG by adopting generative processes over function spaces - known as nonparametrics in the Bayesian literature. We introduce syntax for reasoning about arbitrary collections of objects, and their properties, in an intuitive manner. By exploiting exchangeability, distributions over unknown objects and their attributes are cast as Dirichlet processes, which resolve difficulties in model selection and inference caused by varying numbers of objects. We demonstrate these concepts with application to citation matching.


Efficient algorithm for estimation of qualitative expected utility in possibilistic case-based reasoning

Abstract: We propose an efficient algorithm for estimation of possibility based qualitative expected utility. It is useful for decision making mechanisms where each possible decision is assigned a multi-attribute possibility distribution. The computational complexity of ordinary methods calculating the expected utility based on discretization is growing exponentially with the number of attributes, and may become infeasible with a high number of these attributes. We present series of theorems and lemmas proving the correctness of our algorithm that exibits a linear computational complexity. Our algorithm has been applied in the context of selecting the most prospective partners in multi-party multi-attribute negotiation, and can also be used in making decisions about potential offers during the negotiation as other similar problems.


Local Markov Property for Models Satisfying Composition Axiom

Abstract: The local Markov condition for a DAG to be an independence map of a probability distribution is well known. For DAGs with latent variables, represented as bi-directed edges in the graph, the local Markov property may invoke exponential number of conditional independencies. This paper shows that the number of conditional independence relations required may be reduced if the probability distributions satisfy the composition axiom. In certain types of graphs, only linear number of conditional independencies are required. The result has applications in testing linear structural equation models with correlated errors.


Unsupervised Activity Discovery and Characterization From Event-Streams

Abstract: We present a framework to discover and characterize different classes of everyday activities from event-streams. We begin by representing activities as bags of event n-grams. This allows us to analyze the global structural information of activities, using their local event statistics. We demonstrate how maximal cliques in an undirected edge-weighted graph of activities, can be used for activity-class discovery in an unsupervised manner. We show how modeling an activity as a variable length Markov process, can be used to discover recurrent event-motifs to characterize the discovered activity-classes. We present results over extensive data-sets, collected from multiple active environments, to show the competence and generalizability of our proposed framework.


Bounding the Uncertainty of Graphical Games: The Complexity of Simple Requirements, Pareto and Strong Nash Equilibria

Abstract: We investigate the complexity of bounding the uncertainty of graphical games, and we provide new insight into the intrinsic difficulty of computing Nash equilibria. In particular, we show that, if one adds very simple and natural additional requirements to a graphical game, the existence of Nash equilibria is no longer guaranteed, and computing an equilibrium is an intractable problem. Moreover, if stronger equilibrium conditions are required for the game, we get hardness results for the second level of the polynomial hierarchy. Our results offer a clear picture of the complexity of mixed Nash equilibria in graphical games, and answer some open research questions posed by Conitzer and Sandholm (2003).


Modeling Transportation Routines using Hybrid Dynamic Mixed Networks

Abstract: This paper describes a general framework called Hybrid Dynamic Mixed Networks (HDMNs) which are Hybrid Dynamic Bayesian Networks that allow representation of discrete deterministic information in the form of constraints. We propose approximate inference algorithms that integrate and adjust well known algorithmic principles such as Generalized Belief Propagation, Rao-Blackwellised Particle Filtering and Constraint Propagation to address the complexity of modeling and reasoning in HDMNs. We use this framework to model a person's travel activity over time and to predict destination and routes given the current location. We present a preliminary empirical evaluation demonstrating the effectiveness of our modeling framework and algorithms using several variants of the activity model.


Approximate Inference Algorithms for Hybrid Bayesian Networks with Discrete Constraints

Abstract: In this paper, we consider Hybrid Mixed Networks (HMN) which are Hybrid Bayesian Networks that allow discrete deterministic information to be modeled explicitly in the form of constraints. We present two approximate inference algorithms for HMNs that integrate and adjust well known algorithmic principles such as Generalized Belief Propagation, Rao-Blackwellised Importance Sampling and Constraint Propagation to address the complexity of modeling and reasoning in HMNs. We demonstrate the performance of our approximate inference algorithms on randomly generated HMNs.


Metrics for Markov Decision Processes with Infinite State Spaces

Abstract: We present metrics for measuring state similarity in Markov decision processes (MDPs) with infinitely many states, including MDPs with continuous state spaces. Such metrics provide a stable quantitative analogue of the notion of bisimulation for MDPs, and are suitable for use in MDP approximation. We show that the optimal value function associated with a discounted infinite horizon planning task varies continuously with respect to our metric distances.


Planning in POMDPs Using Multiplicity Automata

Abstract: Planning and learning in Partially Observable MDPs (POMDPs) are among the most challenging tasks in both the AI and Operation Research communities. Although solutions to these problems are intractable in general, there might be special cases, such as structured POMDPs, which can be solved efficiently. A natural and possibly efficient way to represent a POMDP is through the predictive state representation (PSR) - a representation which recently has been receiving increasing attention. In this work, we relate POMDPs to multiplicity automata- showing that POMDPs can be represented by multiplicity automata with no increase in the representation size. Furthermore, we show that the size of the multiplicity automaton is equal to the rank of the predictive state representation. Therefore, we relate both the predictive state representation and POMDPs to the well-founded multiplicity automata literature. Based on the multiplicity automata representation, we provide a planning algorithm which is exponential only in the multiplicity automata rank rather than the number of states of the POMDP. As a result, whenever the predictive state representation is logarithmic in the standard POMDP representation, our planning algorithm is efficient.


Unstructuring User Preferences: Efficient Non-Parametric Utility Revelation

Abstract: Tackling the problem of ordinal preference revelation and reasoning, we propose a novel methodology for generating an ordinal utility function from a set of qualitative preference statements. To the best of our knowledge, our proposal constitutes the first nonparametric solution for this problem that is both efficient and semantically sound. Our initial experiments provide strong evidence for practical effectiveness of our approach.


Existence and Finiteness Conditions for Risk-Sensitive Planning: Results and Conjectures

Abstract: Decision-theoretic planning with risk-sensitive planning objectives is important for building autonomous agents or decision-support systems for real-world applications. However, this line of research has been largely ignored in the artificial intelligence and operations research communities since planning with risk-sensitive planning objectives is more complicated than planning with risk-neutral planning objectives. To remedy this situation, we derive conditions that guarantee that the optimal expected utilities of the total plan-execution reward exist and are finite for fully observable Markov decision process models with non-linear utility functions. In case of Markov decision process models with both positive and negative rewards, most of our results hold for stationary policies only, but we conjecture that they can be generalized to non stationary policies.


Near-optimal Nonmyopic Value of Information in Graphical Models

Abstract: A fundamental issue in real-world systems, such as sensor networks, is the selection of observations which most effectively reduce uncertainty. More specifically, we address the long standing problem of nonmyopically selecting the most informative subset of variables in a graphical model. We present the first efficient randomized algorithm providing a constant factor (1-1/e-epsilon) approximation guarantee for any epsilon > 0 with high confidence. The algorithm leverages the theory of submodular functions, in combination with a polynomial bound on sample complexity. We furthermore prove that no polynomial time algorithm can provide a constant factor approximation better than (1 - 1/e) unless P = NP. Finally, we provide extensive evidence of the effectiveness of our method on two complex real-world datasets.


On the optimality of tree-reweighted max-product message-passing

Abstract: Tree-reweighted max-product (TRW) message passing is a modified form of the ordinary max-product algorithm for attempting to find minimal energy configurations in Markov random field with cycles. For a TRW fixed point satisfying the strong tree agreement condition, the algorithm outputs a configuration that is provably optimal. In this paper, we focus on the case of binary variables with pairwise couplings, and establish stronger properties of TRW fixed points that satisfy only the milder condition of weak tree agreement (WTA). First, we demonstrate how it is possible to identify part of the optimal solution|i.e., a provably optimal solution for a subset of nodes| without knowing a complete solution. Second, we show that for submodular functions, a WTA fixed point always yields a globally optimal solution. We establish that for binary variables, any WTA fixed point always achieves the global maximum of the linear programming relaxation underlying the TRW method.


A Revision-Based Approach to Resolving Conflicting Information

Abstract: In this paper, we propose a revision-based approach for conflict resolution by generalizing the Disjunctive Maxi-Adjustment (DMA) approach (Benferhat et al. 2004). Revision operators can be classified into two different families: the model-based ones and the formula-based ones. So the revision-based approach has two different versions according to which family of revision operators is chosen. Two particular revision operators are considered, one is the Dalal's revision operator, which is a model-based revision operator, and the other is the cardinality-maximal based revision operator, which is a formulabased revision operator. When the Dalal's revision operator is chosen, the revision-based approach is independent of the syntactic form in each stratum and it captures some notion of minimal change. When the cardinalitymaximal based revision operator is chosen, the revision-based approach is equivalent to the DMA approach. We also show that both approaches are computationally easier than the DMA approach.


Asynchronous Dynamic Bayesian Networks

Abstract: Systems such as sensor networks and teams of autonomous robots consist of multiple autonomous entities that interact with each other in a distributed, asynchronous manner. These entities need to keep track of the state of the system as it evolves. Asynchronous systems lead to special challenges for monitoring, as nodes must update their beliefs independently of each other and no central coordination is possible. Furthermore, the state of the system continues to change as beliefs are being updated. Previous approaches to developing distributed asynchronous probabilistic reasoning systems have used static models. We present an approach using dynamic models, that take into account the way the system changes state over time. Our approach, which is based on belief propagation, is fully distributed and asynchronous, and allows the world to keep on changing as messages are being sent around. Experimental results show that our approach compares favorably to the factored frontier algorithm.


Robotic Mapping with Polygonal Random Fields

Abstract: Two types of probabilistic maps are popular in the mobile robotics literature: occupancy grids and geometric maps. Occupancy grids have the advantages of simplicity and speed, but they represent only a restricted class of maps and they make incorrect independence assumptions. On the other hand, current geometric approaches, which characterize the environment by features such as line segments, can represent complex environments compactly. However, they do not reason explicitly about occupancy, a necessity for motion planning; and, they lack a complete probability model over environmental structures. In this paper we present a probabilistic mapping technique based on polygonal random fields (PRF), which combines the advantages of both approaches. Our approach explicitly represents occupancy using a geometric representation, and it is based upon a consistent probability distribution over environments which avoids the incorrect independence assumptions made by occupancy grids. We show how sampling techniques for PRFs can be applied to localized laser and sonar data, and we demonstrate significant improvements in mapping performance over occupancy grids.


Self-Confirming Price Prediction for Bidding in Simultaneous Ascending Auctions

Abstract: Simultaneous ascending auctions present agents with the exposure problem: bidding to acquire a bundle risks the possibility of obtaining an undesired subset of the goods. Auction theory provides little guidance for dealing with this problem. We present a new family of decisiontheoretic bidding strategies that use probabilistic predictions of final prices. We focus on selfconfirming price distribution predictions, which by definition turn out to be correct when all agents bid decision-theoretically based on them. Bidding based on these is provably not optimal in general, but our experimental evidence indicates the strategy can be quite effective compared to other known methods.


Expectation Propagation for Continuous Time Bayesian Networks

Abstract: Continuous time Bayesian networks (CTBNs) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cyclic) dependency graph over a set of variables, each of which represents a finite state continuous time Markov process whose transition model is a function of its parents. As shown previously, exact inference in CTBNs is intractable. We address the problem of approximate inference, allowing for general queries conditioned on evidence over continuous time intervals and at discrete time points. We show how CTBNs can be parameterized within the exponential family, and use that insight to develop a message passing scheme in cluster graphs and allows us to apply expectation propagation to CTBNs. The clusters in our cluster graph do not contain distributions over the cluster variables at individual time points, but distributions over trajectories of the variables throughout a duration. Thus, unlike discrete time temporal models such as dynamic Bayesian networks, we can adapt the time granularity at which we reason for different variables and in different conditions.


Expectation Maximization and Complex Duration Distributions for Continuous Time Bayesian Networks

Abstract: Continuous time Bayesian networks (CTBNs) describe structured stochastic processes with finitely many states that evolve over continuous time. A CTBN is a directed (possibly cyclic) dependency graph over a set of variables, each of which represents a finite state continuous time Markov process whose transition model is a function of its parents. We address the problem of learning the parameters and structure of a CTBN from partially observed data. We show how to apply expectation maximization (EM) and structural expectation maximization (SEM) to CTBNs. The availability of the EM algorithm allows us to extend the representation of CTBNs to allow a much richer class of transition durations distributions, known as phase distributions. This class is a highly expressive semi-parametric representation, which can approximate any duration distribution arbitrarily closely. This extension to the CTBN framework addresses one of the main limitations of both CTBNs and DBNs - the restriction to exponentially / geometrically distributed duration. We present experimental results on a real data set of people's life spans, showing that our algorithm learns reasonable models - structure and parameters - from partially observed data, and, with the use of phase distributions, achieves better performance than DBNs.


Sufficient conditions for convergence of Loopy Belief Propagation

Abstract: We derive novel sufficient conditions for convergence of Loopy Belief Propagation (also known as the Sum-Product algorithm) to a unique fixed point. Our results improve upon previously known conditions. For binary variables with (anti-)ferromagnetic interactions, our conditions seem to be sharp.


A Conditional Random Field for Discriminatively-trained Finite-state String Edit Distance

Abstract: The need to measure sequence similarity arises in information extraction, object identity, data mining, biological sequence analysis, and other domains. This paper presents discriminative string-edit CRFs, a finitestate conditional random field model for edit sequences between strings. Conditional random fields have advantages over generative approaches to this problem, such as pair HMMs or the work of Ristad and Yianilos, because as conditionally-trained methods, they enable the use of complex, arbitrary actions and features of the input strings. As in generative models, the training data does not have to specify the edit sequences between the given string pairs. Unlike generative models, however, our model is trained on both positive and negative instances of string pairs. We present positive experimental results on several data sets.


The Relationship Between AND/OR Search and Variable Elimination

Abstract: In this paper we compare search and inference in graphical models through the new framework of AND/OR search. Specifically, we compare Variable Elimination (VE) and memoryintensive AND/OR Search (AO) and place algorithms such as graph-based backjumping and no-good and good learning, as well as Recursive Conditioning [7] and Value Elimination [2] within the AND/OR search framework.


Representation Policy Iteration

Abstract: This paper addresses a fundamental issue central to approximation methods for solving large Markov decision processes (MDPs): how to automatically learn the underlying representation for value function approximation? A novel theoretically rigorous framework is proposed that automatically generates geometrically customized orthonormal sets of basis functions, which can be used with any approximate MDP solver like least squares policy iteration (LSPI). The key innovation is a coordinate-free representation of value functions, using the theory of smooth functions on a Riemannian manifold. Hodge theory yields a constructive method for generating basis functions for approximating value functions based on the eigenfunctions of the self-adjoint (Laplace-Beltrami) operator on manifolds. In effect, this approach performs a global Fourier analysis on the state space graph to approximate value functions, where the basis functions reflect the largescale topology of the underlying state space. A new class of algorithms called Representation Policy Iteration (RPI) are presented that automatically learn both basis functions and approximately optimal policies. Illustrative experiments compare the performance of RPI with that of LSPI using two handcoded basis functions (RBF and polynomial state encodings).


Description Logics with Fuzzy Concrete Domains

Abstract: We present a fuzzy version of description logics with concrete domains. Main features are: (i) concept constructors are based on t-norm, t-conorm, negation and implication; (ii) concrete domains are fuzzy sets; (iii) fuzzy modifiers are allowed; and (iv) the reasoning algorithm is based on a mixture of completion rules and bounded mixed integer programming.


Bayes' Bluff: Opponent Modelling in Poker

Abstract: Poker is a challenging problem for artificial intelligence, with non-deterministic dynamics, partial observability, and the added difficulty of unknown adversaries. Modelling all of the uncertainties in this domain is not an easy task. In this paper we present a Bayesian probabilistic model for a broad class of poker games, separating the uncertainty in the game dynamics from the uncertainty of the opponent's strategy. We then describe approaches to two key subproblems: (i) inferring a posterior over opponent strategies given a prior distribution and observations of their play, and (ii) playing an appropriate response to that distribution. We demonstrate the overall approach on a reduced version of poker using Dirichlet priors and then on the full game of Texas hold'em using a more informed prior. We demonstrate methods for playing effective responses to the opponent, based on the posterior.


Point-Based POMDP Algorithms: Improved Analysis and Implementation

Abstract: Existing complexity bounds for point-based POMDP value iteration algorithms focus either on the curse of dimensionality or the curse of history. We derive a new bound that relies on both and uses the concept of discounted reachability; our conclusions may help guide future algorithm design. We also discuss recent improvements to our (point-based) heuristic search value iteration algorithm. Our new implementation calculates tighter initial bounds, avoids solving linear programs, and makes more effective use of sparsity.


Approximate Linear Programming for First-order MDPs

Abstract: We introduce a new approximate solution technique for first-order Markov decision processes (FOMDPs). Representing the value function linearly w.r.t. a set of first-order basis functions, we compute suitable weights by casting the corresponding optimization as a first-order linear program and show how off-the-shelf theorem prover and LP software can be effectively used. This technique allows one to solve FOMDPs independent of a specific domain instantiation; furthermore, it allows one to determine bounds on approximation error that apply equally to all domain instantiations. We apply this solution technique to the task of elevator scheduling with a rich feature space and multi-criteria additive reward, and demonstrate that it outperforms a number of intuitive, heuristicallyguided policies.


Predictive Linear-Gaussian Models of Stochastic Dynamical Systems

Abstract: Models of dynamical systems based on predictive state representations (PSRs) are defined strictly in terms of observable quantities, in contrast with traditional models (such as Hidden Markov Models) that use latent variables or statespace representations. In addition, PSRs have an effectively infinite memory, allowing them to model some systems that finite memory-based models cannot. Thus far, PSR models have primarily been developed for domains with discrete observations. Here, we develop the Predictive Linear-Gaussian (PLG) model, a class of PSR models for domains with continuous observations. We show that PLG models subsume Linear Dynamical System models (also called Kalman filter models or state-space models) while using fewer parameters. We also introduce an algorithm to estimate PLG parameters from data, and contrast it with standard Expectation Maximization (EM) algorithms used to estimate Kalman filter parameters. We show that our algorithm is a consistent estimation procedure and present preliminary empirical results suggesting that our algorithm outperforms EM, particularly as the model dimension increases.


Efficient Test Selection in Active Diagnosis via Entropy Approximation

Abstract: We consider the problem of diagnosing faults in a system represented by a Bayesian network, where diagnosis corresponds to recovering the most likely state of unobserved nodes given the outcomes of tests (observed nodes). Finding an optimal subset of tests in this setting is intractable in general. We show that it is difficult even to compute the next most-informative test using greedy test selection, as it involves several entropy terms whose exact computation is intractable. We propose an approximate approach that utilizes the loopy belief propagation infrastructure to simultaneously compute approximations of marginal and conditional entropies on multiple subsets of nodes. We apply our method to fault diagnosis in computer networks, and show the algorithm to be very effective on realistic Internet-like topologies. We also provide theoretical justification for the greedy test selection approach, along with some performance guarantees.


Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars

Abstract: This paper addresses the problem of mapping natural language sentences to lambda-calculus encodings of their meaning. We describe a learning algorithm that takes as input a training set of sentences labeled with expressions in the lambda calculus. The algorithm induces a grammar for the problem, along with a log-linear model that represents a distribution over syntactic and semantic analyses conditioned on the input sentence. We apply the method to the task of learning natural language interfaces to databases and show that the learned parsers outperform previous methods in two benchmark database domains.


Importance Sampling in Bayesian Networks: An Influence-Based Approximation Strategy for Importance Functions

Abstract: One of the main problems of importance sampling in Bayesian networks is representation of the importance function, which should ideally be as close as possible to the posterior joint distribution. Typically, we represent an importance function as a factorization, i.e., product of conditional probability tables (CPTs). Given diagnostic evidence, we do not have explicit forms for the CPTs in the networks. We first derive the exact form for the CPTs of the optimal importance function. Since the calculation is hard, we usually only use their approximations. We review several popular strategies and point out their limitations. Based on an analysis of the influence of evidence, we propose a method for approximating the exact form of importance function by explicitly modeling the most important additional dependence relations introduced by evidence. Our experimental results show that the new approximation strategy offers an immediate improvement in the quality of the importance function.


An Algorithm for Computing Stochastically Stable Distributions with Applications to Multiagent Learning in Repeated Games

Abstract: One of the proposed solutions to the equilibrium selection problem for agents learning in repeated games is obtained via the notion of stochastic stability. Learning algorithms are perturbed so that the Markov chain underlying the learning dynamics is necessarily irreducible and yields a unique stable distribution. The stochastically stable distribution is the limit of these stable distributions as the perturbation rate tends to zero. We present the first exact algorithm for computing the stochastically stable distribution of a Markov chain. We use our algorithm to predict the long-term dynamics of simple learning algorithms in sample repeated games.


Qualitative Decision Making Under Possibilistic Uncertainty: Toward more discriminating criteria

Abstract: The aim of this paper is to propose a generalization of previous approaches in qualitative decision making. Our work is based on the binary possibilistic utility (PU), which is a possibilistic counterpart of Expected Utility (EU).We first provide a new axiomatization of PU and study its relation with the lexicographic aggregation of pessimistic and optimistic utilities. Then we explain the reasons of the coarseness of qualitative decision criteria. Finally, thanks to a redefinition of possibilistic lotteries and mixtures, we present the refined binary possibilistic utility, which is more discriminating than previously proposed criteria.


Structured Region Graphs: Morphing EP into GBP

Abstract: GBP and EP are two successful algorithms for approximate probabilistic inference, which are based on different approximation strategies. An open problem in both algorithms has been how to choose an appropriate approximation structure. We introduce 'structured region graphs', a formalism which marries these two strategies, reveals a deep connection between them, and suggests how to choose good approximation structures. In this formalism, each region has an internal structure which defines an exponential family, whose sufficient statistics must be matched by the parent region. Reduction operators on these structures allow conversion between EP and GBP free energies. Thus it is revealed that all EP approximations on discrete variables are special cases of GBP, and conversely that some wellknown GBP approximations, such as overlapping squares, are special cases of EP. Furthermore, region graphs derived from EP have a number of good structural properties, including maxent-normality and overall counting number of one. The result is a convenient framework for producing high-quality approximations with a user-adjustable level of complexity


A Model for Reasoning with Uncertain Rules in Event Composition Systems

Abstract: In recent years, there has been an increased need for the use of active systems - systems required to act automatically based on events, or changes in the environment. Such systems span many areas, from active databases to applications that drive the core business processes of today's enterprises. However, in many cases, the events to which the system must respond are not generated by monitoring tools, but must be inferred from other events based on complex temporal predicates. In addition, in many applications, such inference is inherently uncertain. In this paper, we introduce a formal framework for knowledge representation and reasoning enabling such event inference. Based on probability theory, we define the representation of the associated uncertainty. In addition, we formally define the probability space, and show how the relevant probabilities can be calculated by dynamically constructing a Bayesian network. To the best of our knowledge, this is the first work that enables taking such uncertainty into account in the context of active systems. herefore, our contribution is twofold: We formally define the representation and semantics of event composition for probabilistic settings, and show how to apply these extensions to the quantification of the occurrence probability of events. These results enable any active system to handle such uncertainty.


Flexible Dynamic Information Flow Control in the Presence of Exceptions

Abstract: We describe a new, dynamic, floating-label approach to language-based information flow control. A labeled IO monad, LIO, keeps track of a current label and permits restricted access to IO functionality. The current label floats to exceed the labels of all data observed and restricts what can be modified. Unlike other language-based work, LIO also bounds the current label with a current clearance that provides a form of discretionary access control. Computations may encapsulate and pass around the results of computations with different labels. In addition, the LIO monad offers a simple form of labeled mutable references and exception handling. We give precise semantics and prove confidentiality and integrity properties of a call-by-name \lambda-calculus and provide an implementation in Haskell.


Single-window Integrated Development Environment

Abstract: This paper addresses the problem of IDE interface complexity by introducing single-window graphical user interface. This approach lies in removing additional child windows from IDE, thus allowing a user to keep only text editor window open. We describe an abstract model of IDE GUI that is based on most popular modern integrated environments and has generalized user interface parts. Then this abstract model is reorganized into single windowed interface model: access to common IDE functions is provided from the code editing window while utility windows are removed without loss of IDE functionality. After that the implementation of single-window GUI on KDevelop 4 is described. And finally tool views and usability of several well- known IDEs are surveyed.


Super-Mixed Multiple Attribute Group Decision Making Method Based on Hybrid Fuzzy Grey Relation Approach Degree

Abstract: The feature of our method different from other fuzzy grey relation method for supermixed multiple attribute group decision-making is that all of the subjective and objective weights are obtained by interval grey number and that the group decisionmaking is performed based on the relative approach degree of grey TOPSIS, the relative approach degree of grey incidence and the relative membership degree of grey incidence using 4-dimensional Euclidean distance. The weighted Borda method is used to obtain final rank by using the results of four methods. An example shows the applicability of the proposed approach.


On the Generalized Delay-Capacity Tradeoff of Mobile Networks with L\'evy Flight Mobility

Abstract: In the literature, scaling laws for wireless mobile networks have been characterized under various models of node mobility and several assumptions on how communication occurs between nodes. To improve the realism in the analysis of scaling laws, we propose a new analytical framework. The framework is the first to consider a L\'{e}vy flight mobility pattern, which is known to closely mimic human mobility patterns. Also, this is the first work that allows nodes to communicate while being mobile. Under this framework, delays ($\bar{D}$) to obtain various levels of per-node throughput $(\lambda)$ for L\'evy flight are suggested as $\bar{D}(\lambda) = O(\sqrt{\min (n^{1+\alpha} \lambda, n^2)})$, where L\'evy flight is a random walk of a power-law flight distribution with an exponent $\alpha \in (0,2]$. The same framework presents a new tighter tradeoff $\bar{D}(\lambda) = O(\sqrt{\max (1,n\lambda^3)})$ for \textit{i.i.d.} mobility, whose delays are lower than existing results for the same levels of per-node throughput.


Algorithms for High-Performance Networking in the Presence of Obstacles

Abstract: This work develops novel algorithms for high-performance networking in the presence of obstacles based on a method for communicating via ultrasonic rays reflected at the obstacles. The rays are curves determined by the variable speed of sound and initial conditions and we develop ultrasonic ray models based on a system of differential equations. We present new parallel algorithms and software for shape and trajectory reconstruction of moving obstacles and show how the reconstructed reflection point of a ray at an obstacle is a natural router for messages between the ray's transmitter and receiver and discuss the advantages of the new architecture. We discuss how the new algorithms and software improve the performance and properties of the network architecture.


Multimodal similarity-preserving hashing

Abstract: We introduce an efficient computational framework for hashing data belonging to multiple modalities into a single representation space where they become mutually comparable. The proposed approach is based on a novel coupled siamese neural network architecture and allows unified treatment of intra- and inter-modality similarity learning. Unlike existing cross-modality similarity learning approaches, our hashing functions are not limited to binarized linear projections and can assume arbitrarily complex forms. We show experimentally that our method significantly outperforms state-of-the-art hashing approaches on multimedia retrieval tasks.


Generalized Hybrid Grey Relation Method for Multiple Attribute Mixed Type Decision Making

Abstract: The multiple attribute mixed type decision making is performed by four methods, that is, the relative approach degree of grey TOPSIS method, the relative approach degree of grey incidence, the relative membership degree of grey incidence and the grey relation relative approach degree method using the maximum entropy estimation, respectively. In these decision making methods, the grey incidence degree in four-dimensional Euclidean space is used. The final arrangement result is obtained by weighted Borda method. An example illustrates the applicability of the proposed approach.


Data Mining on Educational Domain

Abstract: Educational data mining (EDM) is defined as the area of scientific inquiry centered around the development of methods for making discoveries within the unique kinds of data that come from educational settings, and using those methods to better understand students and the settings which they learn in. Data mining enables organizations to use their current reporting capabilities to uncover and understand hidden patterns in vast databases. As a result of this insight, institutions are able to allocate resources and staff more effectively. In this paper, we present a real-world experiment conducted in Shree Rayeshwar Institute of Engineering and Information Technology (SRIEIT) in Goa, India. Here we found the relevant subjects in an undergraduate syllabus and the strength of their relationship. We have also focused on classification of students into different categories such as good, average, poor depending on their marks scored by them by obtaining a decision tree which will predict the performance of the students and accordingly help the weaker section of students to improve in their academics. We have also found clusters of students for helping in analyzing student's performance and also improvising the subject teaching in that particular subject.


Framework of SQL Injection Attack

Abstract: With the changing demographics of globalization, the emergence and prevalence of web application have acquired a central and pivotal role in the domains of technology and advancements. It thus becomes imperative to probe deeply into the architecture, significance and different facets of usages. Web applications enclose the functioning between a user and the services provided by the server, which contains a database as its backend. The user can access the required information through sending a request in the form of text to the web server, which is interpreted by the server side script to construct an SQL. The query is sent to the database which responds in order to generate an HTML page that is sent back to the user. Since the functioning of web application is a dynamic and complicated matter, certain threats to the database security have been registered. One such alarming threat is the prevalence of SQL Injection Attack. Hence a dynamic algorithm is given in this paper for preventing SQL Injection Attacks which is based on context free grammars and compiler parsing techniques. The paper attempts to present the notation of a SQLI Prevent Parser for the prevention of SQL Injection Attacks. This Parser determines the structure of queries and compares whether the queries are functionally equivalent or not. This parser has been used on a sample web application and the results have come out to be positive majors to prevent SQL Injection Attacks.


Hybrid Forecasting of Exchange Rate by Using Chaos Wavelet SVM-Markov Model and Grey Relation Degree

Abstract: This paper proposes an exchange rate forecasting method by using the grey relative combination approach of chaos wavelet SVM-Markov model. The problem of short-term forecast of exchange rate by using the comprehensive method of the phase space reconstitution and SVM method has been researched. We have suggested a wavelet-SVR-Markov forecasting model to predict the finance time series and demonstrated that can more improve the forecasting performance by the rational combination of the forecast results through various combinational tests. Our test result has been showed that the two-stage combination model is more excellent than the normal combination model. Also we have comprehensively estimated the combination forecast methods according to the forecasting performance indicators.The estimated result have been shown that the combination forecast methods on the basic of the degree of grey relation and the optimal grey relation combination have fine forecast performance.


Velocity/Position Integration Formula (I): Application to In-flight Coarse Alignment

Abstract: The in-flight alignment is a critical stage for airborne INS/GPS applications. The alignment task is usually carried out by the Kalman filtering technique that necessitates a good initial attitude to obtain satisfying performance. Due to the airborne dynamics, the in-flight alignment is much difficult than alignment on the ground. This paper proposes an optimization-based coarse alignment approach using GPS position/velocity as input, founded on the newly-derived velocity/position integration formulae. Simulation and flight test results show that, with the GPS lever arm well handled, it is potentially able to yield the initial heading up to one degree accuracy in ten seconds. It can serve as a nice coarse in-flight alignment without any prior attitude information for the subsequent fine Kalman alignment. The approach can also be applied to other applications that require aligning the INS on the run.


An Innovative Skin Detection Approach Using Color Based Image Retrieval Technique

Abstract: From The late 90th, "Skin Detection" becomes one of the major problems in image processing. If "Skin Detection" will be done in high accuracy, it can be used in many cases as face recognition, Human Tracking and etc. Until now so many methods were presented for solving this problem. In most of these methods, color space was used to extract feature vector for classifying pixels, but the most of them have not good accuracy in detecting types of skin. The proposed approach in this paper is based on "Color based image retrieval" (CBIR) technique. In this method, first by means of CBIR method and image tiling and considering the relation between pixel and its neighbors, a feature vector would be defined and then with using a training step, detecting the skin in the test stage. The result shows that the presenting approach, in addition to its high accuracy in detecting type of skin, has no sensitivity to illumination intensity and moving face orientation.


Velocity/Position Integration Formula (II): Application to Inertial Navigation Computation

Abstract: Inertial navigation applications are usually referenced to a rotating frame. Consideration of the navigation reference frame rotation in the inertial navigation algorithm design is an important but so far less seriously treated issue, especially for ultra-high-speed flying aircraft or the future ultra-precision navigation system of several meters per hour. This paper proposes a rigorous approach to tackle the issue of navigation frame rotation in velocity/position computation by use of the newly-devised velocity/position integration formulae in the Part I companion paper. The two integration formulae set a well-founded cornerstone for the velocity/position algorithms design that makes the comprehension of the inertial navigation computation principle more accessible to practitioners, and different approximations to the integrals involved will give birth to various velocity/position update algorithms. Two-sample velocity and position algorithms are derived to exemplify the design process. In the context of level-flight airplane examples, the derived algorithm is analytically and numerically compared to the typical algorithms existing in the literature. The results throw light on the problems in existing algorithms and the potential benefits of the derived algorithm.


Complete PISO and SIMPLE solvers on Graphics Processing Units

Abstract: We implemented the pressure-implicit with splitting of operators (PISO) and semi-implicit method for pressure-linked equations (SIMPLE) solvers of the Navier-Stokes equations on Fermi-class graphics processing units (GPUs) using the CUDA technology. We also introduced a new format of sparse matrices optimized for performing elementary CFD operations, like gradient or divergence discretization, on GPUs. We verified the validity of the implementation on several standard, steady and unsteady problems. Computational effciency of the GPU implementation was examined by comparing its double precision run times with those of essentially the same algorithms implemented in OpenFOAM. The results show that a GPU (Tesla C2070) can outperform a server-class 6-core, 12-thread CPU (Intel Xeon X5670) by a factor of 4.2.


A Secure Dynamic Job Scheduling on Smart Grid using RSA Algorithm

Abstract: Grid computing is a computation methodology using group of clusters connected over high-speed networks that involves coordinating and sharing computational power, data storage and network resources. Integrating a set of clusters of workstations into one large computing environment can improve the availability of computing power. The goal of scheduling is to achieve highest possible system throughput and to match the application need with the available computing resources. A secure scheduling model is presented, that performs job grouping activity at runtime. In a Grid environment, security is necessary because grid is a dynamic environment and participates are independent bodies with different policies, objectives and requirements. Authentication should be verified for Grid resource owners as well as resource requesters before they are allowed to join in scheduling activities. In order to achieve secure resource and job scheduling including minimum processing time and maximum resource utilization, A Secure Resource by using RSA algorithm on Networking and Job Scheduling model with Job Grouping strategy(JGS) in Grid Computing has been proposed. The result shows significant improvement in the processing time of jobs and resource utilization as compared to dynamic job grouping (DJG) based scheduling on smart grids (SG).


Syntactic vs. Semantic Locality: How Good Is a Cheap Approximation?

Abstract: Extracting a subset of a given OWL ontology that captures all the ontology's knowledge about a specified set of terms is a well-understood task. This task can be based, for instance, on locality-based modules (LBMs). These come in two flavours, syntactic and semantic, and a syntactic LBM is known to contain the corresponding semantic LBM. For syntactic LBMs, polynomial extraction algorithms are known, implemented in the OWL API, and being used. In contrast, extracting semantic LBMs involves reasoning, which is intractable for OWL 2 DL, and these algorithms had not been implemented yet for expressive ontology languages. We present the first implementation of semantic LBMs and report on experiments that compare them with syntactic LBMs extracted from real-life ontologies. Our study reveals whether semantic LBMs are worth the additional extraction effort, compared with syntactic LBMs.


Analysis of Multi-Scale Fractal Dimension to Classify Human Motion

Abstract: In recent years there has been considerable interest in human action recognition. Several approaches have been developed in order to enhance the automatic video analysis. Although some developments have been achieved by the computer vision community, the properly classification of human motion is still a hard and challenging task. The objective of this study is to investigate the use of 3D multi-scale fractal dimension to recognize motion patterns in videos. In order to develop a robust strategy for human motion classification, we proposed a method where the Fourier transform is used to calculate the derivative in which all data points are deemed. Our results shown that different accuracy rates can be found for different databases. We believe that in specific applications our results are the first step to develop an automatic monitoring system, which can be applied in security systems, traffic monitoring, biology, physical therapy, cardiovascular disease among many others.


Fast Constructions of Light-Weight Spanners for General Graphs

Abstract: To our knowledge, there are only two known algorithms for constructing sparse and light spanners for general graphs. One of them is the greedy algorithm of Alth$\ddot{o}$fer et al. \cite{ADDJS93}, analyzed by Chandra et al. in SoCG'92. The greedy algorithm consructs, for every \emph{weighted} undirected $n$-vertex $m$-edge graph $G = (V,E)$ and any integer $k \ge 1$, a $(2k-1)$-spanner with $O(n^{1 + 1/k})$ edges and weight $O(k \cdot n^{(1+\eps)/k}) \cdot \omega(MST(G))$, for any $\eps > 0$. The drawback of the greedy algorithm is that it requires $O(m \cdot (n^{1 + 1/k} + n \cdot \log n))$ time. The other algorithm is due to Awerbuch et al. \cite{ABP91}. It constructs $O(k)$-spanners with $O(k \cdot n^{1 + 1/k} \cdot \Lambda)$ edges, weight $O(k^2 \cdot n^{1/k} \cdot \Lambda) \cdot \omega(MST(G))$, within time $O(m \cdot k \cdot n^{1/k} \cdot \Lambda)$, where $\Lambda$ is the logarithm of the aspect ratio of the graph. The running time of both these algorithms is unsatisfactory. Moreover, the usually faster algorithm of \cite{ABP91} pays for the speedup by significantly increasing both the stretch, the sparsity, and the weight of the resulting spanner. In this paper we devise an efficient algorithm for constructing sparse and light spanners. Specifically, our algorithm constructs $((2k-1) \cdot (1+\eps))$-spanners with $O(k \cdot n^{1 + 1/k})$ edges and weight $O(k \cdot n^{1/k}) \cdot \omega(MST(G))$, where $\eps > 0$ is an arbitrarily small constant. The running time of our algorithm is $O(k \cdot m + \min\{n \cdot \log n,m \cdot \alpha(n)\})$. Moreover, by slightly increasing the running time we can reduce the other parameters. These results address an open problem from the ESA'04 paper by Roditty and Zwick \cite{RZ04}.


Design and Development of Low Cost Multi-Channel USB Data

Abstract: This paper describes the design and development of low cost USB Data Acquisition System (DAS) for the measurement of physical parameters. Physical parameters such as temperature, humidity, light intensity etc., which are generally slowly varying signals are sensed by respective sensors or integrated sensors and converted into voltages. The DAS is designed using PIC18F4550 microcontroller, communicating with Personal Computer (PC) through USB (Universal Serial Bus). The designed DAS has been tested with the application program developed in Visual Basic, which allows online monitoring in graphical as well as numerical display.


Performance Evaluation of Widely used Portknoking Algorithms

Abstract: Port knocking is a technique by which only a single packet or special sequence will permit the firewall to open a port on a machine where all ports are blocked by default. It is a passive authorization technique which offers firewall-level authentication to ensure authorized access to potentially vulnerable network services. In this paper, we present performance evaluation and analytical comparison of three widely used port knocking (PK) algorithms, Aldaba, FWKNOP and SIG-2. Comparative analysis is based upon ten selected parameters; Platforms (Supported OS), Implementation (PK, SPA or both), Protocols (UDP, TCP, ICMP), Out of Order packet delivery, NAT (Network Address Translation), Encryption Algorithms, Root privileges (For installation and operation), Weak Passwords, Replay Attacks and IPv6 compatibility. Based upon these parameters, relative performance score has been given to each algorithm. Finally, we deduce that FWKNOP due to compatibility with windows client is the most efficient among chosen PK implementations.


Security Architecture for Cluster based Ad Hoc Networks

Abstract: Mobile Ad hoc Networks (MANETs) are subject to various kinds of attacks. Deploying security mechanisms is difficult due to inherent properties of ad hoc networks, such as the high dynamics of their topology, restricted bandwidth, and limited resources in end device. With such dynamicity in connectivity and limited resources it is not possible to deploy centralized security solution but distribution solution. The paper proposes architectural security concept in distributed manner where network is divided into clusters with one cluster head node each. This cluster head node also act as a router providing proactive hidden routing by using Steganographic methods for inter-cluster security. Besides cipher method is used to provide intra-cluster security. The proposed secure architecture specifies operational view of cluster head as a router that provides trust, anonymity and confidentiality through Steganography and Cryptography respectively.