The Life and Death of Unwanted Bits: Towards Proactive Waste Data Management in Digital Ecosystems

Abstract: Our everyday data processing activities create massive amounts of data. Like physical waste and trash, unwanted and unused data also pollutes the digital environment by degrading the performance and capacity of storage systems and requiring costly disposal. In this paper, we propose using the lessons from real life waste management in handling waste data. We show the impact of waste data on the performance and operational costs of our computing systems. To allow better waste data management, we define a waste hierarchy for digital objects and provide insights into how to identify and categorize waste data. Finally, we introduce novel ways of reusing, reducing, and recycling data and software to minimize the impact of data wastage


Topological Fidelity in Sensor Networks

Abstract: Sensor Networks are inherently complex networks, and many of their associated problems require analysis of some of their global characteristics. These are primarily affected by the topology of the network. We present in this paper, a general framework for a topological analysis of a network, and develop distributed algorithms in a generalized combinatorial setting in order to solve two seemingly unrelated problems, 1) Coverage hole detection and Localization and 2) Worm hole attack detection and Localization. We also note these solutions remain coordinate free as no priori localization information of the nodes is assumed. For the coverage hole problem, we follow a "divide and conquer approach", by strategically dissecting the network so that the overall topology is preserved, while efficiently pursuing the detection and localization of failures. The detection of holes, is enabled by first attributing a combinatorial object called a "Rips Complex" to each network segment, and by subsequently checking the existence/non-existence of holes by way of triviality of the first homology class of this complex. Our estimate exponentially approaches the location of potential holes with each iteration, yielding a very fast convergence coupled with optimal usage of valuable resources such as power and memory. We then show a simple extension of the above problem to address a well known problem in networks, namely the localization of a worm hole attack. We demonstrate the effectiveness of the presented algorithm with several substantiating examples.


Simulation Framework for Modeling Large-Scale Distributed Systems

Abstract: Simulation has become the evaluation method of choice for many areas of distributing computing research. However, most existing simulation packages have several limitations on the size and complexity of the system being modeled. Fine grained simulation of complex systems such as Grids requires high computational effort which can only be obtained by using an underlying distributed architecture. We are proposing a new distributed simulation system that has the advantage of being able to model very complex distributed systems while hiding the computational effort from the end-user.


A Comparison of Performance Measures via Online Search

Abstract: Though competitive analysis has been a very useful performance measure for the quality of online algorithms, it is recognized that it sometimes fails to distinguish between algorithms of different quality in practice. A number of alternative measures have been proposed, but, with a few exceptions, these have generally been applied only to the online problem they were developed in connection with. Recently, a systematic study of performance measures for online algorithms was initiated [Boyar, Irani, Larsen: Eleventh International Algorithms and Data Structures Symposium 2009], first focusing on a simple server problem. We continue this work by studying a fundamentally different online problem, online search, and the Reservation Price Policies in particular. The purpose of this line of work is to learn more about the applicability of various performance measures in different situations and the properties that the different measures emphasize. We investigate the following analysis techniques: Competitive, Relative Worst Order, Bijective, Average, Relative Interval, Random Order, and Max/Max. In addition to drawing conclusions on this work, we also investigate the measures' sensitivity to integral vs. real-valued domains, and as a part of this work, generalize some of the known performance measures. Finally, we have established the first optimality proof for Relative Interval Analysis.


Understanding Code Patterns - Analysis, Interpretation & Measurement

Abstract: This research paper aims to find, analyze and understand code patterns in any software system and measure its quality by defining standards and proposing a formula for the same. Every code that is written can be divided into different code segments, each having its own impact on the overall system. We can analyze these code segments to get the code quality. The measures used in this paper include Lines of Code, Number of calls made by a module, Execution time, the system knowledge of user and developers, the use of generalization, inheritance, reusability and other object-oriented concepts. The entire software code is divided into code snippets, based on the logic that they implement. Each of these code snippets has an impact. This measure is called Impact Factor and is valued by the software developer and/or other system stakeholders. Efficiency = (Code Area / Execution Time) * Qr


IBSEAD: - A Self-Evolving Self-Obsessed Learning Algorithm for Machine Learning

Abstract: We present IBSEAD or distributed autonomous entity systems based Interaction - a learning algorithm for the computer to self-evolve in a self-obsessed manner. This learning algorithm will present the computer to look at the internal and external environment in series of independent entities, which will interact with each other, with and/or without knowledge of the computer's brain. When a learning algorithm interacts, it does so by detecting and understanding the entities in the human algorithm. However, the problem with this approach is that the algorithm does not consider the interaction of the third party or unknown entities, which may be interacting with each other. These unknown entities in their interaction with the non-computer entities make an effect in the environment that influences the information and the behaviour of the computer brain. Such details and the ability to process the dynamic and unsettling nature of these interactions are absent in the current learning algorithm such as the decision tree learning algorithm. IBSEAD is able to evaluate and consider such algorithms and thus give us a better accuracy in simulation of the highly evolved nature of the human brain. Processes such as dreams, imagination and novelty, that exist in humans are not fully simulated by the existing learning algorithms. Also, Hidden Markov models (HMM) are useful in finding "hidden" entities, which may be known or unknown. However, this model fails to consider the case of unknown entities which maybe unclear or unknown. IBSEAD is better because it considers three types of entities- known, unknown and invisible. We present our case with a comparison of existing algorithms in known environments and cases and present the results of the experiments using dry run of the simulated runs of the existing machine learning algorithms versus IBSEAD.


On the behaviours produced by instruction sequences under execution

Abstract: We study several aspects of the behaviours produced by instruction sequences under execution in the setting of the algebraic theory of processes known as ACP. We use ACP to describe the behaviours produced by instruction sequences under execution and to describe two protocols implementing these behaviours in the case where the processing of instructions takes place remotely. We also show that all finite-state behaviours considered in ACP can be produced by instruction sequences under execution.


The Cost of Bounded Curvature

Abstract: We study the motion-planning problem for a car-like robot whose turning radius is bounded from below by one and which is allowed to move in the forward direction only (Dubins car). For two robot configurations $\sigma, \sigma'$, let $\ell(\sigma, \sigma')$ be the shortest bounded-curvature path from $\sigma$ to $\sigma'$. For $d \geq 0$, let $\ell(d)$ be the supremum of $\ell(\sigma, \sigma')$, over all pairs $(\sigma, \sigma')$ that are at Euclidean distance $d$. We study the function $\dub(d) = \ell(d) - d$, which expresses the difference between the bounded-curvature path length and the Euclidean distance of its endpoints. We show that $\dub(d)$ decreases monotonically from $\dub(0) = 7\pi/3$ to $\dub(\ds) = 2\pi$, and is constant for $d \geq \ds$. Here $\ds \approx 1.5874$. We describe pairs of configurations that exhibit the worst-case of $\dub(d)$ for every distance $d$.


Why 'GSA: A Gravitational Search Algorithm' Is Not Genuinely Based on the Law of Gravity


Visual Secret Sharing Scheme using Grayscale Images

Abstract: Pixel expansion and the quality of the reconstructed secret image has been a major issue of visual secret sharing (VSS) schemes. A number of probabilistic VSS schemes with minimum pixel expansion have been proposed for black and white (binary) secret images. This paper presents a probabilistic (2, 3)-VSS scheme for gray scale images. Its pixel expansion is larger in size but the quality of the image is perfect when it's reconstructed. The construction of the shadow images (transparent shares) is based on the binary OR operation.


A Note on Improved Loss Bounds for Multiple Kernel Learning

Abstract: In this paper, we correct an upper bound, presented in~\cite{hs-11}, on the generalisation error of classifiers learned through multiple kernel learning. The bound in~\cite{hs-11} uses Rademacher complexity and has an\emph{additive} dependence on the logarithm of the number of kernels and the margin achieved by the classifier. However, there are some errors in parts of the proof which are corrected in this paper. Unfortunately, the final result turns out to be a risk bound which has a \emph{multiplicative} dependence on the logarithm of the number of kernels and the margin achieved by the classifier.


External Memory Orthogonal Range Reporting with Fast Updates

Abstract: In this paper we describe data structures for orthogonal range reporting in external memory that support fast update operations. The query costs either match the query costs of the best previously known data structures or differ by a small multiplicative factor.


A Dynamic Algebraic Specification for Social Networks

Abstract: With the help of the Internet, social networks have grown rapidly. This has increased security requirements. We present a formalization of social networks as composite behavioral objects, defined using the Observational Transition System (OTS) approach. Our definition is then translated to the OTS/CafeOBJ algebraic specification methodology. This translation allows the formal verification of safety properties for social networks via the Proof Score method. Finally, using this methodology we formally verify some security properties.


A Dynamic Elimination-Combining Stack Algorithm

Abstract: Two key synchronization paradigms for the construction of scalable concurrent data-structures are software combining and elimination. Elimination-based concurrent data-structures allow operations with reverse semantics (such as push and pop stack operations) to "collide" and exchange values without having to access a central location. Software combining, on the other hand, is effective when colliding operations have identical semantics: when a pair of threads performing operations with identical semantics collide, the task of performing the combined set of operations is delegated to one of the threads and the other thread waits for its operation(s) to be performed. Applying this mechanism iteratively can reduce memory contention and increase throughput. The most highly scalable prior concurrent stack algorithm is the elimination-backoff stack. The elimination-backoff stack provides high parallelism for symmetric workloads in which the numbers of push and pop operations are roughly equal, but its performance deteriorates when workloads are asymmetric. We present DECS, a novel Dynamic Elimination-Combining Stack algorithm, that scales well for all workload types. While maintaining the simplicity and low-overhead of the elimination-bakcoff stack, DECS manages to benefit from collisions of both identical- and reverse-semantics operations. Our empirical evaluation shows that DECS scales significantly better than both blocking and non-blocking best prior stack algorithms.


SIP APIs for Voice and Video Communications on the Web

Abstract: Existing standard protocols for the web and Internet telephony fail to deliver real-time interactive communication from within a web browser. In particular, the client-server web protocol over reliable TCP is not always suitable for end-to-end low latency media path needed for interactive voice and video communication. To solve this, we compare the available platform options using the existing technologies such as modifying the web programming language and protocol, using an existing web browser plugin, and a separate host resident application that the web browser can talk to. We argue that using a separate application as an adaptor is a promising short term as well as long-term strategy for voice and video communications on the web. Our project aims at developing the open technology and sample implementations for web-based real-time voice and video communication applications. We describe the architecture of our project including (1) a RESTful web communication API over HTTP inspired by SIP message flows, (2) a web-friendly set of metadata for session description, and (3) an UDP-based end-to-end media path. All other telephony functions reside in the web application itself and/or in web feature servers. The adaptor approach allows us to easily add new voice and video codecs and NAT traversal technologies such as Host Identity Protocol. We want to make web-based communication accessible to millions of web developers, maximize the end user experience and security, and preserve the huge global investment in and experience from SIP systems while adhering to web standards and development tools as much as possible. We have created an open source prototype that allows you to freely use the conference application by directing a browser to the conference URL.


External-Memory Network Analysis Algorithms for Naturally Sparse Graphs

Abstract: In this paper, we present a number of network-analysis algorithms in the external-memory model. We focus on methods for large naturally sparse graphs, that is, n-vertex graphs that have O(n) edges and are structured so that this sparsity property holds for any subgraph of such a graph. We give efficient external-memory algorithms for the following problems for such graphs: - Finding an approximate d-degeneracy ordering; - Finding a cycle of length exactly c; - Enumerating all maximal cliques. Such problems are of interest, for example, in the analysis of social networks, where they are used to study network cohesion.


Vision-Based Navigation III: Pose and Motion from Omnidirectional Optical Flow and a Digital Terrain Map

Abstract: An algorithm for pose and motion estimation using corresponding features in omnidirectional images and a digital terrain map is proposed. In previous paper, such algorithm for regular camera was considered. Using a Digital Terrain (or Digital Elevation) Map (DTM/DEM) as a global reference enables recovering the absolute position and orientation of the camera. In order to do this, the DTM is used to formulate a constraint between corresponding features in two consecutive frames. In this paper, these constraints are extended to handle non-central projection, as is the case with many omnidirectional systems. The utilization of omnidirectional data is shown to improve the robustness and accuracy of the navigation algorithm. The feasibility of this algorithm is established through lab experimentation with two kinds of omnidirectional acquisition systems. The first one is polydioptric cameras while the second is catadioptric camera.


Quadratic-time Algorithm for the String Constrained LCS Problem

Abstract: The problem of finding a longest common subsequence of two main sequences with some constraint that must be a substring of the result (STR-IC-LCS) was formulated recently. It is a variant of the constrained longest common subsequence problem. As the known algorithms for the STR-IC-LCS problem are cubic-time, the presented quadratic-time algorithm is significantly faster.


Flash-based Audio and Video Communication in the Cloud

Abstract: Internet telephony and multimedia communication protocols have matured over the last fifteen years. Recently, the web is evolving as a popular platform for everything we do on the Internet including email, text chat, voice calls, discussions, enterprise apps and multi-party collaboration. Unfortunately, there is a disconnect between web and traditional Internet telephony protocols as they have ignored the constraints and requirements of each other. Consequently, the Flash Player is being used as a web browser plugin by many developers for web-based voice and video calls. We describe the challenges of video communication using a web browser, present a simple API using a Flash Player application, show how it supports wide range of web communication scenarios in the cloud, and describe how it can interoperate with Session Initiation Protocol (SIP)-based systems. We describe both the advantages and challenges of Flash Player based communication applications. The presented API could guide future work on communication-related web protocol extensions.


Automaton based detection of affected cells in three dimensional biological system

Abstract: The aim of this research review is to propose the logic and search mechanism for the development of an artificially intelligent automaton (AIA) that can find affected cells in a 3-dimensional biological system. Research on the possible application of such automatons to detect and control cancer cells in the human body are greatly focused MRI and PET scans finds the affected regions at the tissue level even as we can find the affected regions at the cellular level using the framework. The AIA may be designed to ensure optimum utilization as they record and might control the presence of affected cells in a human body. The proposed models and techniques can be generalized and used in any application where cells are injured or affected by some disease or accident. The best method to import AIA into the body without surgery or injection is to insert small pill like automata, carrying material viz drugs or leukocytes that is needed to correct the infection. In this process, the AIA can be compared to nano pills to deliver or support therapy. NanoHive simulation software was used to validate the framework of this paper. The existing nanomedicine models such as obstacle avoidance algorithm based models (Hla K H S et al 2008) and the framework in this model were tested in different simulation based experiments. The existing models such as obstacle avoidance based models failed in complex environmental conditions (such as changing environmental conditions, presence of semi-solid particles, etc) while the model in this paper executed its framework successfully.Come systems biology, this field of automatons deserves a bigger leap of understanding especially when pharmacogenomics is at its peak. The results also indicate the importance of artificial intelligence and other computational capabilities in the proposed model for the successful detection of affected cells.


A New Technique for Combining Multiple Classifiers using The Dempster-Shafer Theory of Evidence

Abstract: This paper presents a new classifier combination technique based on the Dempster-Shafer theory of evidence. The Dempster-Shafer theory of evidence is a powerful method for combining measures of evidence from different classifiers. However, since each of the available methods that estimates the evidence of classifiers has its own limitations, we propose here a new implementation which adapts to training data so that the overall mean square error is minimized. The proposed technique is shown to outperform most available classifier combination methods when tested on three different classification problems.


Searching for Bayesian Network Structures in the Space of Restricted Acyclic Partially Directed Graphs

Abstract: Although many algorithms have been designed to construct Bayesian network structures using different approaches and principles, they all employ only two methods: those based on independence criteria, and those based on a scoring function and a search procedure (although some methods combine the two). Within the score+search paradigm, the dominant approach uses local search methods in the space of directed acyclic graphs (DAGs), where the usual choices for defining the elementary modifications (local changes) that can be applied are arc addition, arc deletion, and arc reversal. In this paper, we propose a new local search method that uses a different search space, and which takes account of the concept of equivalence between network structures: restricted acyclic partially directed graphs (RPDAGs). In this way, the number of different configurations of the search space is reduced, thus improving efficiency. Moreover, although the final result must necessarily be a local optimum given the nature of the search method, the topology of the new search space, which avoids making early decisions about the directions of the arcs, may help to find better local optima than those obtained by searching in the DAG space. Detailed results of the evaluation of the proposed search method on several test problems, including the well-known Alarm Monitoring System, are also presented.


Learning to Order BDD Variables in Verification

Abstract: The size and complexity of software and hardware systems have significantly increased in the past years. As a result, it is harder to guarantee their correct behavior. One of the most successful methods for automated verification of finite-state systems is model checking. Most of the current model-checking systems use binary decision diagrams (BDDs) for the representation of the tested model and in the verification process of its properties. Generally, BDDs allow a canonical compact representation of a boolean function (given an order of its variables). The more compact the BDD is, the better performance one gets from the verifier. However, finding an optimal order for a BDD is an NP-complete problem. Therefore, several heuristic methods based on expert knowledge have been developed for variable ordering. We propose an alternative approach in which the variable ordering algorithm gains 'ordering experience' from training models and uses the learned knowledge for finding good orders. Our methodology is based on offline learning of pair precedence classifiers from training models, that is, learning which variable pair permutation is more likely to lead to a good order. For each training model, a number of training sequences are evaluated. Every training model variable pair permutation is then tagged based on its performance on the evaluated orders. The tagged permutations are then passed through a feature extractor and are given as examples to a classifier creation algorithm. Given a model for which an order is requested, the ordering algorithm consults each precedence classifier and constructs a pair precedence table which is used to create the order. Our algorithm was integrated with SMV, which is one of the most widely used verification systems. Preliminary empirical evaluation of our methodology, using real benchmark models, shows performance that is better than random ordering and is competitive with existing algorithms that use expert knowledge. We believe that in sub-domains of models (alu, caches, etc.) our system will prove even more valuable. This is because it features the ability to learn sub-domain knowledge, something that no other ordering algorithm does.


Decentralized Supply Chain Formation: A Market Protocol and Competitive Equilibrium Analysis

Abstract: Supply chain formation is the process of determining the structure and terms of exchange relationships to enable a multilevel, multiagent production activity. We present a simple model of supply chains, highlighting two characteristic features: hierarchical subtask decomposition, and resource contention. To decentralize the formation process, we introduce a market price system over the resources produced along the chain. In a competitive equilibrium for this system, agents choose locally optimal allocations with respect to prices, and outcomes are optimal overall. To determine prices, we define a market protocol based on distributed, progressive auctions, and myopic, non-strategic agent bidding policies. In the presence of resource contention, this protocol produces better solutions than the greedy protocols common in the artificial intelligence and multiagent systems literature. The protocol often converges to high-value supply chains, and when competitive equilibria exist, typically to approximate competitive equilibria. However, complementarities in agent production technologies can cause the protocol to wastefully allocate inputs to agents that do not produce their outputs. A subsequent decommitment phase recovers a significant fraction of the lost surplus.


K-Implementation

Abstract: This paper discusses an interested party who wishes to influence the behavior of agents in a game (multi-agent interaction), which is not under his control. The interested party cannot design a new game, cannot enforce agents' behavior, cannot enforce payments by the agents, and cannot prohibit strategies available to the agents. However, he can influence the outcome of the game by committing to non-negative monetary transfers for the different strategy profiles that may be selected by the agents. The interested party assumes that agents are rational in the commonly agreed sense that they do not use dominated strategies. Hence, a certain subset of outcomes is implemented in a given game if by adding non-negative payments, rational players will necessarily produce an outcome in this subset. Obviously, by making sufficiently big payments one can implement any desirable outcome. The question is what is the cost of implementation? In this paper we introduce the notion of k-implementation of a desired set of strategy profiles, where k stands for the amount of payment that need to be actually made in order to implement desirable outcomes. A major point in k-implementation is that monetary offers need not necessarily materialize when following desired behaviors. We define and study k-implementation in the contexts of games with complete and incomplete information. In the latter case we mainly focus on the VCG games. Our setting is later extended to deal with mixed strategies using correlation devices. Together, the paper introduces and studies the implementation of desirable outcomes by a reliable party who cannot modify game rules (i.e. provide protocols), complementing previous work in mechanism design, while making it more applicable to many realistic CS settings.


CP-nets: A Tool for Representing and Reasoning withConditional Ceteris Paribus Preference Statements

Abstract: Information about user preferences plays a key role in automated decision making. In many domains it is desirable to assess such preferences in a qualitative rather than quantitative way. In this paper, we propose a qualitative graphical representation of preferences that reflects conditional dependence and independence of preference statements under a ceteris paribus (all else being equal) interpretation. Such a representation is often compact and arguably quite natural in many circumstances. We provide a formal semantics for this model, and describe how the structure of the network can be exploited in several inference tasks, such as determining whether one outcome dominates (is preferred to) another, ordering a set outcomes according to the preference relation, and constructing the best outcome subject to available evidence.


Complexity Results and Approximation Strategies for MAP Explanations

Abstract: MAP is the problem of finding a most probable instantiation of a set of variables given evidence. MAP has always been perceived to be significantly harder than the related problems of computing the probability of a variable instantiation Pr, or the problem of computing the most probable explanation (MPE). This paper investigates the complexity of MAP in Bayesian networks. Specifically, we show that MAP is complete for NP^PP and provide further negative complexity results for algorithms based on variable elimination. We also show that MAP remains hard even when MPE and Pr become easy. For example, we show that MAP is NP-complete when the networks are restricted to polytrees, and even then can not be effectively approximated. Given the difficulty of computing MAP exactly, and the difficulty of approximating MAP while providing useful guarantees on the resulting approximation, we investigate best effort approximations. We introduce a generic MAP approximation framework. We provide two instantiations of the framework; one for networks which are amenable to exact inference Pr, and one for networks for which even exact inference is too hard. This allows MAP approximation on networks that are too complex to even exactly solve the easier problems, Pr and MPE. Experimental results indicate that using these approximation algorithms provides much better solutions than standard techniques, and provide accurate MAP estimates in many cases.


Taming Numbers and Durations in the Model Checking Integrated Planning System

Abstract: The Model Checking Integrated Planning System (MIPS) is a temporal least commitment heuristic search planner based on a flexible object-oriented workbench architecture. Its design clearly separates explicit and symbolic directed exploration algorithms from the set of on-line and off-line computed estimates and associated data structures. MIPS has shown distinguished performance in the last two international planning competitions. In the last event the description language was extended from pure propositional planning to include numerical state variables, action durations, and plan quality objective functions. Plans were no longer sequences of actions but time-stamped schedules. As a participant of the fully automated track of the competition, MIPS has proven to be a general system; in each track and every benchmark domain it efficiently computed plans of remarkable quality. This article introduces and analyzes the most important algorithmic novelties that were necessary to tackle the new layers of expressiveness in the benchmark problems and to achieve a high level of performance. The extensions include critical path analysis of sequentially generated plans to generate corresponding optimal parallel plans. The linear time algorithm to compute the parallel plan bypasses known NP hardness results for partial ordering by scheduling plans with respect to the set of actions and the imposed precedence relations. The efficiency of this algorithm also allows us to improve the exploration guidance: for each encountered planning state the corresponding approximate sequential plan is scheduled. One major strength of MIPS is its static analysis phase that grounds and simplifies parameterized predicates, functions and operators, that infers knowledge to minimize the state description length, and that detects domain object symmetries. The latter aspect is analyzed in detail. MIPS has been developed to serve as a complete and optimal state space planner, with admissible estimates, exploration engines and branching cuts. In the competition version, however, certain performance compromises had to be made, including floating point arithmetic, weighted heuristic search exploration according to an inadmissible estimate and parameterized optimization.


IDL-Expressions: A Formalism for Representing and Parsing Finite Languages in Natural Language Processing

Abstract: We propose a formalism for representation of finite languages, referred to as the class of IDL-expressions, which combines concepts that were only considered in isolation in existing formalisms. The suggested applications are in natural language processing, more specifically in surface natural language generation and in machine translation, where a sentence is obtained by first generating a large set of candidate sentences, represented in a compact way, and then by filtering such a set through a parser. We study several formal properties of IDL-expressions and compare this new formalism with more standard ones. We also present a novel parsing algorithm for IDL-expressions and prove a non-trivial upper bound on its time complexity.


Effective Dimensions of Hierarchical Latent Class Models

Abstract: Hierarchical latent class (HLC) models are tree-structured Bayesian networks where leaf nodes are observed while internal nodes are latent. There are no theoretically well justified model selection criteria for HLC models in particular and Bayesian networks with latent nodes in general. Nonetheless, empirical studies suggest that the BIC score is a reasonable criterion to use in practice for learning HLC models. Empirical studies also suggest that sometimes model selection can be improved if standard model dimension is replaced with effective model dimension in the penalty term of the BIC score. Effective dimensions are difficult to compute. In this paper, we prove a theorem that relates the effective dimension of an HLC model to the effective dimensions of a number of latent class models. The theorem makes it computationally feasible to compute the effective dimensions of large HLC models. The theorem can also be used to compute the effective dimensions of general tree models.


Concurrent Auctions Across The Supply Chain

Abstract: With the recent technological feasibility of electronic commerce over the Internet, much attention has been given to the design of electronic markets for various types of electronically-tradable goods. Such markets, however, will normally need to function in some relationship with markets for other related goods, usually those downstream or upstream in the supply chain. Thus, for example, an electronic market for rubber tires for trucks will likely need to be strongly influenced by the rubber market as well as by the truck market. In this paper we design protocols for exchange of information between a sequence of markets along a single supply chain. These protocols allow each of these markets to function separately, while the information exchanged ensures efficient global behavior across the supply chain. Each market that forms a link in the supply chain operates as a double auction, where the bids on one side of the double auction come from bidders in the corresponding segment of the industry, and the bids on the other side are synthetically generated by the protocol to express the combined information from all other links in the chain. The double auctions in each of the markets can be of several types, and we study several variants of incentive compatible double auctions, comparing them in terms of their efficiency and of the market revenue.


A Personalized System for Conversational Recommendations

Abstract: Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -- the Adaptive Place Advisor -- that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system.


Coherent Integration of Databases by Abductive Logic Programming

Abstract: We introduce an abductive method for a coherent integration of independent data-sources. The idea is to compute a list of data-facts that should be inserted to the amalgamated database or retracted from it in order to restore its consistency. This method is implemented by an abductive solver, called Asystem, that applies SLDNFA-resolution on a meta-theory that relates different, possibly contradicting, input databases. We also give a pure model-theoretic analysis of the possible ways to `recover' consistent data from an inconsistent database in terms of those models of the database that exhibit as minimal inconsistent information as reasonably possible. This allows us to characterize the `recovered databases' in terms of the `preferred' (i.e., most consistent) models of the theory. The outcome is an abductive-based application that is sound and complete with respect to a corresponding model-based, preferential semantics, and -- to the best of our knowledge -- is more expressive (thus more general) than any other implementation of coherent integration of databases.


Grounded Semantic Composition for Visual Scenes

Abstract: We present a visually-grounded language understanding model based on a study of how people verbally describe objects in scenes. The emphasis of the model is on the combination of individual word meanings to produce meanings for complex referring expressions. The model has been implemented, and it is able to understand a broad range of spatial referring expressions. We describe our implementation of word level visually-grounded semantics and their embedding in a compositional parsing framework. The implemented system selects the correct referents in response to natural language expressions for a large percentage of test cases. In an analysis of the system's successes and failures we reveal how visual context influences the semantics of utterances and propose future extensions to the model that take such context into account.


Existence of Multiagent Equilibria with Limited Agents

Abstract: Multiagent learning is a necessary yet challenging problem as multiagent systems become more prevalent and environments become more dynamic. Much of the groundbreaking work in this area draws on notable results from game theory, in particular, the concept of Nash equilibria. Learners that directly learn an equilibrium obviously rely on their existence. Learners that instead seek to play optimally with respect to the other players also depend upon equilibria since equilibria are fixed points for learning. From another perspective, agents with limitations are real and common. These may be undesired physical limitations as well as self-imposed rational limitations, such as abstraction and approximation techniques, used to make learning tractable. This article explores the interactions of these two important concepts: equilibria and limitations in learning. We introduce the question of whether equilibria continue to exist when agents have limitations. We look at the general effects limitations can have on agent behavior, and define a natural extension of equilibria that accounts for these limitations. Using this formalization, we make three major contributions: (i) a counterexample for the general existence of equilibria with limitations, (ii) sufficient conditions on limitations that preserve their existence, (iii) three general classes of games and limitations that satisfy these conditions. We then present empirical results from a specific multiagent learning algorithm applied to a specific instance of limited agents. These results demonstrate that learning with limitations is feasible, when the conditions outlined by our theoretical analysis hold.


Price Prediction in a Trading Agent Competition

Abstract: The 2002 Trading Agent Competition (TAC) presented a challenging market game in the domain of travel shopping. One of the pivotal issues in this domain is uncertainty about hotel prices, which have a significant influence on the relative cost of alternative trip schedules. Thus, virtually all participants employ some method for predicting hotel prices. We survey approaches employed in the tournament, finding that agents apply an interesting diversity of techniques, taking into account differing sources of evidence bearing on prices. Based on data provided by entrants on their agents' actual predictions in the TAC-02 finals and semifinals, we analyze the relative efficacy of these approaches. The results show that taking into account game-specific information about flight prices is a major distinguishing factor. Machine learning methods effectively induce the relationship between flight and hotel prices from game data, and a purely analytical approach based on competitive equilibrium analysis achieves equal accuracy with no historical data. Employing a new measure of prediction quality, we relate absolute accuracy to bottom-line performance in the game.


Compositional Model Repositories via Dynamic Constraint Satisfaction with Order-of-Magnitude Preferences

Abstract: The predominant knowledge-based approach to automated model construction, compositional modelling, employs a set of models of particular functional components. Its inference mechanism takes a scenario describing the constituent interacting components of a system and translates it into a useful mathematical model. This paper presents a novel compositional modelling approach aimed at building model repositories. It furthers the field in two respects. Firstly, it expands the application domain of compositional modelling to systems that can not be easily described in terms of interacting functional components, such as ecological systems. Secondly, it enables the incorporation of user preferences into the model selection process. These features are achieved by casting the compositional modelling problem as an activity-based dynamic preference constraint satisfaction problem, where the dynamic constraints describe the restrictions imposed over the composition of partial models and the preferences correspond to those of the user of the automated modeller. In addition, the preference levels are represented through the use of symbolic values that differ in orders of magnitude.


Competitive Coevolution through Evolutionary Complexification

Abstract: Two major goals in machine learning are the discovery and improvement of solutions to complex problems. In this paper, we argue that complexification, i.e. the incremental elaboration of solutions through adding new structure, achieves both these goals. We demonstrate the power of complexification through the NeuroEvolution of Augmenting Topologies (NEAT) method, which evolves increasingly complex neural network architectures. NEAT is applied to an open-ended coevolutionary robot duel domain where robot controllers compete head to head. Because the robot duel domain supports a wide range of strategies, and because coevolution benefits from an escalating arms race, it serves as a suitable testbed for studying complexification. When compared to the evolution of networks with fixed structure, complexifying evolution discovers significantly more sophisticated strategies. The results suggest that in order to discover and improve complex solutions, evolution, and search in general, should be allowed to complexify as well as optimize.


Dual Modelling of Permutation and Injection Problems

Abstract: When writing a constraint program, we have to choose which variables should be the decision variables, and how to represent the constraints on these variables. In many cases, there is considerable choice for the decision variables. Consider, for example, permutation problems in which we have as many values as variables, and each variable takes an unique value. In such problems, we can choose between a primal and a dual viewpoint. In the dual viewpoint, each dual variable represents one of the primal values, whilst each dual value represents one of the primal variables. Alternatively, by means of channelling constraints to link the primal and dual variables, we can have a combined model with both sets of variables. In this paper, we perform an extensive theoretical and empirical study of such primal, dual and combined models for two classes of problems: permutation problems and injection problems. Our results show that it often be advantageous to use multiple viewpoints, and to have constraints which channel between them to maintain consistency. They also illustrate a general methodology for comparing different constraint models.


Generalizing Boolean Satisfiability I: Background and Survey of Existing Work

Abstract: This is the first of three planned papers describing ZAP, a satisfiability engine that substantially generalizes existing tools while retaining the performance characteristics of modern high-performance solvers. The fundamental idea underlying ZAP is that many problems passed to such engines contain rich internal structure that is obscured by the Boolean representation used; our goal is to define a representation in which this structure is apparent and can easily be exploited to improve computational performance. This paper is a survey of the work underlying ZAP, and discusses previous attempts to improve the performance of the Davis-Putnam-Logemann-Loveland algorithm by exploiting the structure of the problem being solved. We examine existing ideas including extensions of the Boolean language to allow cardinality constraints, pseudo-Boolean representations, symmetry, and a limited form of quantification. While this paper is intended as a survey, our research results are contained in the two subsequent articles, with the theoretical structure of ZAP described in the second paper in this series, and ZAP's implementation described in the third.


PHA*: Finding the Shortest Path with A* in An Unknown Physical Environment

Abstract: We address the problem of finding the shortest path between two points in an unknown real physical environment, where a traveling agent must move around in the environment to explore unknown territory. We introduce the Physical-A* algorithm (PHA*) for solving this problem. PHA* expands all the mandatory nodes that A* would expand and returns the shortest path between the two points. However, due to the physical nature of the problem, the complexity of the algorithm is measured by the traveling effort of the moving agent and not by the number of generated nodes, as in standard A*. PHA* is presented as a two-level algorithm, such that its high level, A*, chooses the next node to be expanded and its low level directs the agent to that node in order to explore it. We present a number of variations for both the high-level and low-level procedures and evaluate their performance theoretically and experimentally. We show that the travel cost of our best variation is fairly close to the optimal travel cost, assuming that the mandatory nodes of A* are known in advance. We then generalize our algorithm to the multi-agent case, where a number of cooperative agents are designed to solve the problem. Specifically, we provide an experimental implementation for such a system. It should be noted that the problem addressed here is not a navigation problem, but rather a problem of finding the shortest path between two points for future usage.


Restricted Value Iteration: Theory and Algorithms

Abstract: Value iteration is a popular algorithm for finding near optimal policies for POMDPs. It is inefficient due to the need to account for the entire belief space, which necessitates the solution of large numbers of linear programs. In this paper, we study value iteration restricted to belief subsets. We show that, together with properly chosen belief subsets, restricted value iteration yields near-optimal policies and we give a condition for determining whether a given belief subset would bring about savings in space and time. We also apply restricted value iteration to two interesting classes of POMDPs, namely informative POMDPs and near-discernible POMDPs.


A Maximal Tractable Class of Soft Constraints

Abstract: Many researchers in artificial intelligence are beginning to explore the use of soft constraints to express a set of (possibly conflicting) problem requirements. A soft constraint is a function defined on a collection of variables which associates some measure of desirability with each possible combination of values for those variables. However, the crucial question of the computational complexity of finding the optimal solution to a collection of soft constraints has so far received very little attention. In this paper we identify a class of soft binary constraints for which the problem of finding the optimal solution is tractable. In other words, we show that for any given set of such constraints, there exists a polynomial time algorithm to determine the assignment having the best overall combined measure of desirability. This tractable class includes many commonly-occurring soft constraints, such as 'as near as possible' or 'as soon as possible after', as well as crisp constraints such as 'greater than'. Finally, we show that this tractable class is maximal, in the sense that adding any other form of soft binary constraint which is not in the class gives rise to a class of problems which is NP-hard.


Towards Understanding and Harnessing the Potential of Clause Learning

Abstract: Efficient implementations of DPLL with the addition of clause learning are the fastest complete Boolean satisfiability solvers and can handle many significant real-world problems, such as verification, planning and design. Despite its importance, little is known of the ultimate strengths and limitations of the technique. This paper presents the first precise characterization of clause learning as a proof system (CL), and begins the task of understanding its power by relating it to the well-studied resolution proof system. In particular, we show that with a new learning scheme, CL can provide exponentially shorter proofs than many proper refinements of general resolution (RES) satisfying a natural property. These include regular and Davis-Putnam resolution, which are already known to be much stronger than ordinary DPLL. We also show that a slight variant of CL with unlimited restarts is as powerful as RES itself. Translating these analytical results to practice, however, presents a challenge because of the nondeterministic nature of clause learning algorithms. We propose a novel way of exploiting the underlying problem structure, in the form of a high level problem description such as a graph or PDDL specification, to guide clause learning algorithms toward faster solutions. We show that this leads to exponential speed-ups on grid and randomized pebbling problems, as well as substantial improvements on certain ordering formulas.


Graduality in Argumentation

Abstract: Argumentation is based on the exchange and valuation of interacting arguments, followed by the selection of the most acceptable of them (for example, in order to take a decision, to make a choice). Starting from the framework proposed by Dung in 1995, our purpose is to introduce 'graduality' in the selection of the best arguments, i.e., to be able to partition the set of the arguments in more than the two usual subsets of 'selected' and 'non-selected' arguments in order to represent different levels of selection. Our basic idea is that an argument is all the more acceptable if it can be preferred to its attackers. First, we discuss general principles underlying a 'gradual' valuation of arguments based on their interactions. Following these principles, we define several valuation models for an abstract argumentation system. Then, we introduce 'graduality' in the concept of acceptability of arguments. We propose new acceptability classes and a refinement of existing classes taking advantage of an available 'gradual' valuation.


Explicit Learning Curves for Transduction and Application to Clustering and Compression Algorithms

Abstract: Inductive learning is based on inferring a general rule from a finite data set and using it to label new data. In transduction one attempts to solve the problem of using a labeled training set to label a set of unlabeled points, which are given to the learner prior to learning. Although transduction seems at the outset to be an easier task than induction, there have not been many provably useful algorithms for transduction. Moreover, the precise relation between induction and transduction has not yet been determined. The main theoretical developments related to transduction were presented by Vapnik more than twenty years ago. One of Vapnik's basic results is a rather tight error bound for transductive classification based on an exact computation of the hypergeometric tail. While tight, this bound is given implicitly via a computational routine. Our first contribution is a somewhat looser but explicit characterization of a slightly extended PAC-Bayesian version of Vapnik's transductive bound. This characterization is obtained using concentration inequalities for the tail of sums of random variables obtained by sampling without replacement. We then derive error bounds for compression schemes such as (transductive) support vector machines and for transduction algorithms based on clustering. The main observation used for deriving these new error bounds and algorithms is that the unlabeled test points, which in the transductive setting are known in advance, can be used in order to construct useful data dependent prior distributions over the hypothesis space.


Decentralized Control of Cooperative Systems: Categorization and Complexity Analysis

Abstract: Decentralized control of cooperative systems captures the operation of a group of decision makers that share a single global objective. The difficulty in solving optimally such problems arises when the agents lack full observability of the global state of the system when they operate. The general problem has been shown to be NEXP-complete. In this paper, we identify classes of decentralized control problems whose complexity ranges between NEXP and P. In particular, we study problems characterized by independent transitions, independent observations, and goal-oriented objective functions. Two algorithms are shown to solve optimally useful classes of goal-oriented decentralized processes in polynomial time. This paper also studies information sharing among the decision-makers, which can improve their performance. We distinguish between three ways in which agents can exchange information: indirect communication, direct communication and sharing state features that are not controlled by the agents. Our analysis shows that for every class of problems we consider, introducing direct or indirect communication does not change the worst-case complexity. The results provide a better understanding of the complexity of decentralized control problems that arise in practice and facilitate the development of planning algorithms for these problems.


Reinforcement Learning for Agents with Many Sensors and Actuators Acting in Categorizable Environments

Abstract: In this paper, we confront the problem of applying reinforcement learning to agents that perceive the environment through many sensors and that can perform parallel actions using many actuators as is the case in complex autonomous robots. We argue that reinforcement learning can only be successfully applied to this case if strong assumptions are made on the characteristics of the environment in which the learning is performed, so that the relevant sensor readings and motor commands can be readily identified. The introduction of such assumptions leads to strongly-biased learning systems that can eventually lose the generality of traditional reinforcement-learning algorithms. In this line, we observe that, in realistic situations, the reward received by the robot depends only on a reduced subset of all the executed actions and that only a reduced subset of the sensor inputs (possibly different in each situation and for each action) are relevant to predict the reward. We formalize this property in the so called 'categorizability assumption' and we present an algorithm that takes advantage of the categorizability of the environment, allowing a decrease in the learning time with respect to existing reinforcement-learning algorithms. Results of the application of the algorithm to a couple of simulated realistic-robotic problems (landmark-based navigation and the six-legged robot gait generation) are reported to validate our approach and to compare it to existing flat and generalization-based reinforcement-learning approaches.


Additive Pattern Database Heuristics

Abstract: We explore a method for computing admissible heuristic evaluation functions for search problems. It utilizes pattern databases, which are precomputed tables of the exact cost of solving various subproblems of an existing problem. Unlike standard pattern database heuristics, however, we partition our problems into disjoint subproblems, so that the costs of solving the different subproblems can be added together without overestimating the cost of solving the original problem. Previously, we showed how to statically partition the sliding-tile puzzles into disjoint groups of tiles to compute an admissible heuristic, using the same partition for each state and problem instance. Here we extend the method and show that it applies to other domains as well. We also present another method for additive heuristics which we call dynamically partitioned pattern databases. Here we partition the problem into disjoint subproblems for each state of the search dynamically. We discuss the pros and cons of each of these methods and apply both methods to three different problem domains: the sliding-tile puzzles, the 4-peg Towers of Hanoi problem, and finding an optimal vertex cover of a graph. We find that in some problem domains, static partitioning is most effective, while in others dynamic partitioning is a better choice. In each of these problem domains, either statically partitioned or dynamically partitioned pattern database heuristics are the best known heuristics for the problem.


On Prediction Using Variable Order Markov Models

Abstract: This paper is concerned with algorithms for prediction of discrete sequences over a finite alphabet, using variable order Markov models. The class of such algorithms is large and in principle includes any lossless compression algorithm. We focus on six prominent prediction algorithms, including Context Tree Weighting (CTW), Prediction by Partial Match (PPM) and Probabilistic Suffix Trees (PSTs). We discuss the properties of these algorithms and compare their performance using real life sequences from three domains: proteins, English text and music pieces. The comparison is made with respect to prediction quality as measured by the average log-loss. We also compare classification algorithms based on these predictors with respect to a number of large protein classification tasks. Our results indicate that a "decomposed" CTW (a variant of the CTW algorithm) and PPM outperform all other algorithms in sequence prediction tasks. Somewhat surprisingly, a different algorithm, which is a modification of the Lempel-Ziv compression algorithm, significantly outperforms all algorithms on the protein classification problems.


Ordered Landmarks in Planning

Abstract: Many known planning tasks have inherent constraints concerning the best order in which to achieve the goals. A number of research efforts have been made to detect such constraints and to use them for guiding search, in the hope of speeding up the planning process. We go beyond the previous approaches by considering ordering constraints not only over the (top-level) goals, but also over the sub-goals that will necessarily arise during planning. Landmarks are facts that must be true at some point in every valid solution plan. We extend Koehler and Hoffmann's definition of reasonable orders between top level goals to the more general case of landmarks. We show how landmarks can be found, how their reasonable orders can be approximated, and how this information can be used to decompose a given planning task into several smaller sub-tasks. Our methodology is completely domain- and planner-independent. The implementation demonstrates that the approach can yield significant runtime performance improvements when used as a control loop around state-of-the-art sub-optimal planning systems, as exemplified by FF and LPG.


Finding Approximate POMDP solutions Through Belief Compression

Abstract: Standard value function approaches to finding policies for Partially Observable Markov Decision Processes (POMDPs) are generally considered to be intractable for large models. The intractability of these algorithms is to a large extent a consequence of computing an exact, optimal policy over the entire belief space. However, in real-world POMDP problems, computing the optimal policy for the full belief space is often unnecessary for good control even for problems with complicated policy classes. The beliefs experienced by the controller often lie near a structured, low-dimensional subspace embedded in the high-dimensional belief space. Finding a good approximation to the optimal value function for only this subspace can be much easier than computing the full value function. We introduce a new method for solving large-scale POMDPs by reducing the dimensionality of the belief space. We use Exponential family Principal Components Analysis (Collins, Dasgupta and Schapire, 2002) to represent sparse, high-dimensional belief spaces using small sets of learned features of the belief state. We then plan only in terms of the low-dimensional belief features. By planning in this low-dimensional space, we can find policies for POMDP models that are orders of magnitude larger than models that can be handled by conventional techniques. We demonstrate the use of this algorithm on a synthetic problem and on mobile robot navigation tasks.


A Comprehensive Trainable Error Model for Sung Music Queries

Abstract: We propose a model for errors in sung queries, a variant of the hidden Markov model (HMM). This is a solution to the problem of identifying the degree of similarity between a (typically error-laden) sung query and a potential target in a database of musical works, an important problem in the field of music information retrieval. Similarity metrics are a critical component of query-by-humming (QBH) applications which search audio and multimedia databases for strong matches to oral queries. Our model comprehensively expresses the types of error or variation between target and query: cumulative and non-cumulative local errors, transposition, tempo and tempo changes, insertions, deletions and modulation. The model is not only expressive, but automatically trainable, or able to learn and generalize from query examples. We present results of simulations, designed to assess the discriminatory potential of the model, and tests with real sung queries, to demonstrate relevance to real-world applications.


Phase Transitions and Backbones of the Asymmetric Traveling Salesman Problem

Abstract: In recent years, there has been much interest in phase transitions of combinatorial problems. Phase transitions have been successfully used to analyze combinatorial optimization problems, characterize their typical-case features and locate the hardest problem instances. In this paper, we study phase transitions of the asymmetric Traveling Salesman Problem (ATSP), an NP-hard combinatorial optimization problem that has many real-world applications. Using random instances of up to 1,500 cities in which intercity distances are uniformly distributed, we empirically show that many properties of the problem, including the optimal tour cost and backbone size, experience sharp transitions as the precision of intercity distances increases across a critical value. Our experimental results on the costs of the ATSP tours and assignment problem agree with the theoretical result that the asymptotic cost of assignment problem is pi ^2 /6 the number of cities goes to infinity. In addition, we show that the average computational cost of the well-known branch-and-bound subtour elimination algorithm for the problem also exhibits a thrashing behavior, transitioning from easy to difficult as the distance precision increases. These results answer positively an open question regarding the existence of phase transitions in the ATSP, and provide guidance on how difficult ATSP problem instances should be generated.


Fixed parameter algorithms for restricted coloring problems

Abstract: In this paper, we obtain polynomial time algorithms to determine the acyclic chromatic number, the star chromatic number, the Thue chromatic number, the harmonious chromatic number and the clique chromatic number of $P_4$-tidy graphs and $(q,q-4)$-graphs, for every fixed $q$. These classes include cographs, $P_4$-sparse and $P_4$-lite graphs. All these coloring problems are known to be NP-hard for general graphs. These algorithms are fixed parameter tractable on the parameter $q(G)$, which is the minimum $q$ such that $G$ is a $(q,q-4)$-graph. We also prove that every connected $(q,q-4)$-graph with at least $q$ vertices is 2-clique-colorable and that every acyclic coloring of a cograph is also nonrepetitive.


Optimal Multi-Robot Path Planning with Temporal Logic Constraints

Abstract: In this paper we present a method for automatically planning optimal paths for a group of robots that satisfy a common high level mission specification. Each robot's motion in the environment is modeled as a weighted transition system. The mission is given as a Linear Temporal Logic formula. In addition, an optimizing proposition must repeatedly be satisfied. The goal is to minimize the maximum time between satisfying instances of the optimizing proposition. Our method is guaranteed to compute an optimal set of robot paths. We utilize a timed automaton representation in order to capture the relative position of the robots in the environment. We then obtain a bisimulation of this timed automaton as a finite transition system that captures the joint behavior of the robots and apply our earlier algorithm for the single robot case to optimize the group motion. We present a simulation of a persistent monitoring task in a road network environment.


Formal Model Engineering for Embedded Systems Using Real-Time Maude

Abstract: This paper motivates why Real-Time Maude should be well suited to provide a formal semantics and formal analysis capabilities to modeling languages for embedded systems. One can then use the code generation facilities of the tools for the modeling languages to automatically synthesize Real-Time Maude verification models from design models, enabling a formal model engineering process that combines the convenience of modeling using an informal but intuitive modeling language with formal verification. We give a brief overview six fairly different modeling formalisms for which Real-Time Maude has provided the formal semantics and (possibly) formal analysis. These models include behavioral subsets of the avionics modeling standard AADL, Ptolemy II discrete-event models, two EMF-based timed model transformation systems, and a modeling language for handset software.


Rascal: From Algebraic Specification to Meta-Programming

Abstract: Algebraic specification has a long tradition in bridging the gap between specification and programming by making specifications executable. Building on extensive experience in designing, implementing and using specification formalisms that are based on algebraic specification and term rewriting (namely Asf and Asf+Sdf), we are now focusing on using the best concepts from algebraic specification and integrating these into a new programming language: Rascal. This language is easy to learn by non-experts but is also scalable to very large meta-programming applications. We explain the algebraic roots of Rascal and its main application areas: software analysis, software transformation, and design and implementation of domain-specific languages. Some example applications in the domain of Model-Driven Engineering (MDE) are described to illustrate this.


Typed lambda-terms in categorical attributed graph transformation

Abstract: This paper deals with model transformation based on attributed graph rewriting. Our contribution investigates a single pushout approach for applying the rewrite rules. The computation of graph attributes is obtained through the use of typed lambda-calculus with inductive types. In this paper we present solutions to cope with single pushout construction for the graph structure and the computations functions. As this rewrite system uses inductive types, the expressiveness of attribute computations is facilitated and appears more efficient than the one based on Sigma-algebras. Some examples showing the interest of our computation approach are described in this paper.


Formal Visual Modeling of Real-Time Systems in e-Motions: Two Case Studies

Abstract: e-Motions is an Eclipse-based visual timed model transformation framework with a Real-Time Maude semantics that supports the usual Maude formal analysis methods, including simulation, reachability analysis, and LTL model checking. e-Motions is characterized by a novel and powerful set of constructs for expressing timed behaviors. In this paper we illustrate the use of these constructs --- and thereby implicitly investigate their suitability to define real-time systems in an intuitive way --- to define and formally analyze two prototypical and very different real-time systems: (i) a simple round trip time protocol for computing the time it takes a message to travel from one node to another, and back; and (ii) the EDF scheduling algorithm.


Prototyping the Semantics of a DSL using ASF+SDF: Link to Formal Verification of DSL Models

Abstract: A formal definition of the semantics of a domain-specific language (DSL) is a key prerequisite for the verification of the correctness of models specified using such a DSL and of transformations applied to these models. For this reason, we implemented a prototype of the semantics of a DSL for the specification of systems consisting of concurrent, communicating objects. Using this prototype, models specified in the DSL can be transformed to labeled transition systems (LTS). This approach of transforming models to LTSs allows us to apply existing tools for visualization and verification to models with little or no further effort. The prototype is implemented using the ASF+SDF Meta-Environment, an IDE for the algebraic specification language ASF+SDF, which offers efficient execution of the transformation as well as the ability to read models and produce LTSs without any additional pre or post processing.


Tracing Properties of UML and OCL Models with Maude

Abstract: The starting point of this paper is a system described in form of a UML class diagram where system states are characterized by OCL invariants and system transitions are defined by OCL pre- and postconditions. The aim of our approach is to assist the developer in learning about the consequences of the described system states and transitions and about the formal implications of the properties that are explicitly given. We propose to draw conclusions about the stated constraints by translating the UML and OCL model into the algebraic specification language and system Maude, which is based on rewrite logic. We will concentrate in this paper on employing Maude's capabilities for state search. Maude's state search offers the possibility to describe a start configuration of the system and then explore all configurations reachable by rewriting. The search can be adjusted by formulating requirements for the allowed states and the allowed transitions.


Towards a Reliable Framework of Uncertainty-Based Group Decision Support System

Abstract: This study proposes a framework of Uncertainty-based Group Decision Support System (UGDSS). It provides a platform for multiple criteria decision analysis in six aspects including (1) decision environment, (2) decision problem, (3) decision group, (4) decision conflict, (5) decision schemes and (6) group negotiation. Based on multiple artificial intelligent technologies, this framework provides reliable support for the comprehensive manipulation of applications and advanced decision approaches through the design of an integrated multi-agents architecture.


A Probabilistic Attack on NP-complete Problems

Abstract: Using the probability theory-based approach, this paper reveals the equivalence of an arbitrary NP-complete problem to a problem of checking whether a level set of a specifically constructed harmonic cost function (with all diagonal entries of its Hessian matrix equal to zero) intersects with a unit hypercube in many-dimensional Euclidean space. This connection suggests the possibility that methods of continuous mathematics can provide crucial insights into the most intriguing open questions in modern complexity theory.


Approximating Tverberg Points in Linear Time for Any Fixed Dimension

Abstract: Let P be a d-dimensional n-point set. A Tverberg-partition of P is a partition of P into r sets P_1, ..., P_r such that the convex hulls conv(P_1), ..., conv(P_r) have non-empty intersection. A point in the intersection of the conv(P_i)'s is called a Tverberg point of depth r for P. A classic result by Tverberg implies that there always exists a Tverberg partition of size n/(d+1), but it is not known how to find such a partition in polynomial time. Therefore, approximate solutions are of interest. We describe a deterministic algorithm that finds a Tverberg partition of size n/4(d+1)^3 in time d^{O(log d)} n. This means that for every fixed dimension we can compute an approximate Tverberg point (and hence also an approximate centerpoint) in linear time. Our algorithm is obtained by combining a novel lifting approach with a recent result by Miller and Sheehy (2010).


The Influence of Global Constraints on Similarity Measures for Time-Series Databases

Abstract: A time series consists of a series of values or events obtained over repeated measurements in time. Analysis of time series represents and important tool in many application areas, such as stock market analysis, process and quality control, observation of natural phenomena, medical treatments, etc. A vital component in many types of time-series analysis is the choice of an appropriate distance/similarity measure. Numerous measures have been proposed to date, with the most successful ones based on dynamic programming. Being of quadratic time complexity, however, global constraints are often employed to limit the search space in the matrix during the dynamic programming procedure, in order to speed up computation. Furthermore, it has been reported that such constrained measures can also achieve better accuracy. In this paper, we investigate two representative time-series distance/similarity measures based on dynamic programming, Dynamic Time Warping (DTW) and Longest Common Subsequence (LCS), and the effects of global constraints on them. Through extensive experiments on a large number of time-series data sets, we demonstrate how global constrains can significantly reduce the computation time of DTW and LCS. We also show that, if the constraint parameter is tight enough (less than 10-15% of time-series length), the constrained measure becomes significantly different from its unconstrained counterpart, in the sense of producing qualitatively different 1-nearest neighbor graphs. This observation explains the potential for accuracy gains when using constrained measures, highlighting the need for careful tuning of constraint parameters in order to achieve a good trade-off between speed and accuracy.


Unstructured Human Activity Detection from RGBD Images

Abstract: Being able to detect and recognize human activities is essential for several applications, including personal assistive robotics. In this paper, we perform detection and recognition of unstructured human activity in unstructured environments. We use a RGBD sensor (Microsoft Kinect) as the input sensor, and compute a set of features based on human pose and motion, as well as based on image and pointcloud information. Our algorithm is based on a hierarchical maximum entropy Markov model (MEMM), which considers a person's activity as composed of a set of sub-activities. We infer the two-layered graph structure using a dynamic programming approach. We test our algorithm on detecting and recognizing twelve different activities performed by four people in different environments, such as a kitchen, a living room, an office, etc., and achieve good performance even when the person was not seen before in the training set.


How Not to Characterize Planar-emulable Graphs

Abstract: We investigate the question of which graphs have planar emulators (a locally-surjective homomorphism from some finite planar graph) -- a problem raised already in Fellows' thesis (1985) and conceptually related to the better known planar cover conjecture by Negami (1986). For over two decades, the planar emulator problem lived poorly in a shadow of Negami's conjecture--which is still open--as the two were considered equivalent. But, in the end of 2008, a surprising construction by Rieck and Yamashita falsified the natural "planar emulator conjecture", and thus opened a whole new research field. We present further results and constructions which show how far the planar-emulability concept is from planar-coverability, and that the traditional idea of likening it to projective embeddability is actually very out-of-place. We also present several positive partial characterizations of planar-emulable graphs.


On the origin of ambiguity in efficient communication

Abstract: This article studies the emergence of ambiguity in communication through the concept of logical irreversibility and within the framework of Shannon's information theory. This leads us to a precise and general expression of the intuition behind Zipf's vocabulary balance in terms of a symmetry equation between the complexities of the coding and the decoding processes that imposes an unavoidable amount of logical uncertainty in natural communication. Accordingly, the emergence of irreversible computations is required if the complexities of the coding and the decoding processes are balanced in a symmetric scenario, which means that the emergence of ambiguous codes is a necessary condition for natural communication to succeed.


Law of Connectivity in Machine Learning

Abstract: We present in this paper our law that there is always a connection present between two entities, with a selfconnection being present at least in each node. An entity is an object, physical or imaginary, that is connected by a path (or connection) and which is important for achieving the desired result of the scenario. In machine learning, we state that for any scenario, a subject entity is always, directly or indirectly, connected and affected by single or multiple independent / dependent entities, and their impact on the subject entity is dependent on various factors falling into the categories such as the existenc


Unbounded Contention Resolution in Multiple-Access Channels

Abstract: A frequent problem in settings where a unique resource must be shared among users is how to resolve the contention that arises when all of them must use it, but the resource allows only for one user each time. The application of efficient solutions for this problem spans a myriad of settings such as radio communication networks or databases. For the case where the number of users is unknown, recent work has yielded fruitful results for local area networks and radio networks, although either a (possibly loose) upper bound on the number of users needs to be known, or the solution is suboptimal, or it is only implicit or embedded in other problems, with bounds proved only asymptotically. In this paper, under the assumption that collision detection or information on the number of contenders is not available, we present a novel protocol for contention resolution in radio networks, and we recreate a protocol previously used for other problems, tailoring the constants for our needs. In contrast with previous work, both protocols are proved to be optimal up to a small constant factor and with high probability for big enough number of contenders. Additionally, the protocols are evaluated and contrasted with the previous work by extensive simulations. The evaluation shows that the complexity bounds obtained by the analysis are rather tight, and that both protocols proposed have small and predictable complexity for many system sizes (unlike previous proposals).


Simple Algorithm Portfolio for SAT

Abstract: The importance of algorithm portfolio techniques for SAT has long been noted, and a number of very successful systems have been devised, including the most successful one --- SATzilla. However, all these systems are quite complex (to understand, reimplement, or modify). In this paper we propose a new algorithm portfolio for SAT that is extremely simple, but in the same time so efficient that it outperforms SATzilla. For a new SAT instance to be solved, our portfolio finds its k-nearest neighbors from the training set and invokes a solver that performs the best at those instances. The main distinguishing feature of our algorithm portfolio is the locality of the selection procedure --- the selection of a SAT solver is based only on few instances similar to the input one.


Completeness of Epistemic Coalition Logic with Group Knowledge

Abstract: Coalition logic is one of the most popular logics for multi-agent systems. While epistemic extensions of coalition logic have received much attention, existence of their complete axiomatisations has so far been an open problem. In this paper we settle several of those problems. We prove completeness for epistemic coalition logic with common knowledge, with distributed knowledge, and with both common and distributed knowledge, respectively.


First-order finite satisfiability vs tree automata in safety verification

Abstract: In this paper we deal with verification of safety properties of term-rewriting systems. The verification problem is translated to a purely logical problem of finding a finite countermodel for a first-order formula, which further resolved by a generic finite model finding procedure. A finite countermodel produced during successful verification provides with a concise description of the system invariant sufficient to demonstrate a specific safety property. We show the relative completeness of this approach with respect to the tree automata completion technique. On a set of examples taken from the literature we demonstrate the efficiency of finite model finding approach as well as its explanatory power.


Optimal Divide and Query (extended version)

Abstract: Algorithmic debugging is a semi-automatic debugging technique that allows the programmer to precisely identify the location of bugs without the need to inspect the source code. The technique has been successfully adapted to all paradigms and mature implementations have been released for languages such as Haskell, Prolog or Java. During three decades, the algorithm introduced by Shapiro and later improved by Hirunkitti has been thought optimal. In this paper we first show that this algorithm is not optimal, and moreover, in some situations it is unable to find all possible solutions, thus it is incomplete. Then, we present a new version of the algorithm that is proven optimal, and we introduce some equations that allow the algorithm to identify all optimal solutions.


Vision-Based Navigation I: A navigation filter for fusing DTM/correspondence updates

Abstract: An algorithm for pose and motion estimation using corresponding features in images and a digital terrain map is proposed. Using a Digital Terrain (or Digital Elevation) Map (DTM/DEM) as a global reference enables recovering the absolute position and orientation of the camera. In order to do this, the DTM is used to formulate a constraint between corresponding features in two consecutive frames. The utilization of data is shown to improve the robustness and accuracy of the inertial navigation algorithm. Extended Kalman filter was used to combine results of inertial navigation algorithm and proposed vision-based navigation algorithm. The feasibility of this algorithms is established through numerical simulations.


The second-price auction solves King Solomon's dilemma

Abstract: The planner wants to give k identical, indivisible objects to the top k valuation agents at zero costs. Each agent knows her own valuation of the object and whether it is among the top k. Modify the (k+1)st-price sealed-bid auction by introducing a small participation fee and the option not to participate in it. This simple mechanism implements the desired outcome in iteratively undominated strategies. Moreover, no pair of agents can profitably deviate from the equilibrium by coordinating their strategies or bribing each other.


Abstraction Super-structuring Normal Forms: Towards a Theory of Structural Induction

Abstract: Induction is the process by which we obtain predictive laws or theories or models of the world. We consider the structural aspect of induction. We answer the question as to whether we can find a finite and minmalistic set of operations on structural elements in terms of which any theory can be expressed. We identify abstraction (grouping similar entities) and super-structuring (combining topologically e.g., spatio-temporally close entities) as the essential structural operations in the induction process. We show that only two more structural operations, namely, reverse abstraction and reverse super-structuring (the duals of abstraction and super-structuring respectively) suffice in order to exploit the full power of Turing-equivalent generative grammars in induction. We explore the implications of this theorem with respect to the nature of hidden variables, radical positivism and the 2-century old claim of David Hume about the principles of connexion among ideas.


The Nakamura numbers for computable simple games

Abstract: The Nakamura number of a simple game plays a critical role in preference aggregation (or multi-criterion ranking): the number of alternatives that the players can always deal with rationally is less than this number. We comprehensively study the restrictions that various properties for a simple game impose on its Nakamura number. We find that a computable game has a finite Nakamura number greater than three only if it is proper, nonstrong, and nonweak, regardless of whether it is monotonic or whether it has a finite carrier. The lack of strongness often results in alternatives that cannot be strictly ranked.


Homological Coordinatization

Abstract: In this paper, we review a method for computing and parameterizing the set of homotopy classes of chain maps between two chain complexes. This is then applied to finding topologically meaningful maps between simplicial complexes, which in the context of topological data analysis, can be viewed as an extension of conventional unsupervised learning methods to simplicial complexes.


Automatic Multi-GPU Code Generation applied to Simulation of Electrical Machines

Abstract: The electrical and electronic engineering has used parallel programming to solve its large scale complex problems for performance reasons. However, as parallel programming requires a non-trivial distribution of tasks and data, developers find it hard to implement their applications effectively. Thus, in order to reduce design complexity, we propose an approach to generate code for hybrid architectures (e.g. CPU + GPU) using OpenCL, an open standard for parallel programming of heterogeneous systems. This approach is based on Model Driven Engineering (MDE) and the MARTE profile, standard proposed by Object Management Group (OMG). The aim is to provide resources to non-specialists in parallel programming to implement their applications. Moreover, thanks to model reuse capacity, we can add/change functionalities or the target architecture. Consequently, this approach helps industries to achieve their time-to-market constraints and confirms by experimental tests, performance improvements using multi-GPU environments.


Parameterized Regular Expressions and their Languages

Abstract: We study regular expressions that use variables, or parameters, which are interpreted as alphabet letters. We consider two classes of languages denoted by such expressions: under the possibility semantics, a word belongs to the language if it is denoted by some regular expression obtained by replacing variables with letters; under the certainly semantics, the word must be denoted by every such expression. Such languages are regular, and we show that they naturally arise in several applications such as querying graph databases and program analysis. As the main contribution of the paper, we provide a complete characterization of the complexity of the main computational problems related to such languages: nonemptiness, universality, containment, membership, as well as the problem of constructing NFAs capturing such languages. We also look at the extension when domains of variables could be arbitrary regular languages, and show that under the certainty semantics, languages remain regular and the complexity of the main computational problems does not change.


Managing key multicasting through orthogonal systems

Abstract: In this paper we propose a new protocol to manage multicast key distribution. The protocol is based on the use of orthogonal systems in vector spaces. The main advantage in comparison to other existing multicast key management protocols is that the length and the number of the messages which have to be sent are considerably smaller. This makes the protocol especially attractive when the number of legitimate receivers is large.


Pushing the limits of Full-duplex: Design and Real-time Implementation

Abstract: Recent work has shown the feasibility of single-channel full-duplex wireless physical layer, allowing nodes to send and receive in the same frequency band at the same time. In this report, we first design and implement a real-time 64-subcarrier 10 MHz full-duplex OFDM physical layer, FD-PHY. The proposed FD-PHY not only allows synchronous full-duplex transmissions but also selective asynchronous full-duplex modes. Further, we show that in over-the-air experiments using optimal antenna placement on actual devices, the self-interference can be suppressed upto 80dB, which is 10dB more than prior reported results. Then we propose a full-duplex MAC protocol, FD-MAC, which builds on IEEE 802.11 with three new mechanisms -- shared random backoff, header snooping and virtual backoffs. The new mechanisms allow FD-MAC to discover and exploit full-duplex opportunities in a distributed manner. Our over-the-air tests show over 70% throughput gains from using full-duplex over half-duplex in realistically used cases.


Applications of Discrepancy Theory in Multiobjective Approximation

Abstract: We apply a multi-color extension of the Beck-Fiala theorem to show that the multiobjective maximum traveling salesman problem is randomized 1/2-approximable on directed graphs and randomized 2/3-approximable on undirected graphs. Using the same technique we show that the multiobjective maximum satisfiablilty problem is 1/2-approximable.


Infinitary Term Graph Rewriting

Abstract: Term graph rewriting provides a formalism for implementing term rewriting in an efficient manner by avoiding duplication. Infinitary term rewriting has been introduced to study infinite term reduction sequences. Such infinite reductions can be used to reason about lazy evaluation. In this paper, we combine term graph rewriting and infinitary term rewriting thereby addressing both components of lazy evaluation: non-strictness and sharing. Moreover, we show how our theoretical underpinnings, based on a metric space and a complete semilattice, provides a unified framework for both term rewriting and term graph rewriting. This makes it possible to study the correspondences between these two worlds. As an example, we show how the soundness of term graph rewriting w.r.t. term rewriting can be extended to the infinitary setting.


A Framework for Designing 3D Virtual Environments

Abstract: The process of design and development of virtual environments can be supported by tools and frameworks, to save time in technical aspects and focusing on the content. In this paper we present an academic framework which provides several levels of abstraction to ease this work. It includes state-of-the-art components we devised or integrated adopting open-source solutions in order to face specific problems. Its architecture is modular and customizable, the code is open-source.


Proceedings Ninth Workshop on Quantitative Aspects of Programming Languages

Abstract: This volume contains the proceedings of the Ninth Workshop on Quantitative Aspects of Programming Languages (QAPL 2011), held in Saarbrucken, Germany, April 1--3, 2011. QAPL 2011 is a satellite event of the European Joint Conferences on Theory and Practice of Software (ETAPS 2011). The workshop theme is on quantitative aspects of computation. These aspects are related to the use of physical quantities (storage space, time, bandwidth, etc.) as well as mathematical quantities (e.g. probability and measures for reliability, security and trust), and play an important (sometimes essential) role in characterising the behavior and determining the properties of systems. Such quantities are central to the definition of both the model of systems (architecture, language design, semantics) and the methodologies and tools for the analysis and verification of the systems properties. The aim of this workshop is to discuss the explicit use of quantitative information such as time and probabilities either directly in the model or as a tool for the analysis of systems.


Generalized Maneuvers in Route Planning

Abstract: We study an important practical aspect of the route planning problem in real-world road networks -- maneuvers. Informally, maneuvers represent various irregularities of the road network graph such as turn-prohibitions, traffic light delays, round-abouts, forbidden passages and so on. We propose a generalized model which can handle arbitrarily complex (and even negative) maneuvers, and outline how to enhance Dijkstra's algorithm in order to solve route planning queries in this model without prior adjustments of the underlying road network graph.


Motion Planning via Manifold Samples

Abstract: We present a general and modular algorithmic framework for path planning of robots. Our framework combines geometric methods for exact and complete analysis of low-dimensional configuration spaces, together with practical, considerably simpler sampling-based approaches that are appropriate for higher dimensions. In order to facilitate the transfer of advanced geometric algorithms into practical use, we suggest taking samples that are entire low-dimensional manifolds of the configuration space that capture the connectivity of the configuration space much better than isolated point samples. Geometric algorithms for analysis of low-dimensional manifolds then provide powerful primitive operations. The modular design of the framework enables independent optimization of each modular component. Indeed, we have developed, implemented and optimized a primitive operation for complete and exact combinatorial analysis of a certain set of manifolds, using arrangements of curves of rational functions and concepts of generic programming. This in turn enabled us to implement our framework for the concrete case of a polygonal robot translating and rotating amidst polygonal obstacles. We demonstrate that the integration of several carefully engineered components leads to significant speedup over the popular PRM sampling-based algorithm, which represents the more simplistic approach that is prevalent in practice. We foresee possible extensions of our framework to solving high-dimensional problems beyond motion planning.


Automatic Road Lighting System (ARLS) Model Based on Image Processing of Moving Object

Abstract: Using a vehicle toy (in next future called vehicle) as a moving object an automatic road lighting system (ARLS) model is constructed. A digital video camera with 25 fps is used to capture the vehicle motion as it moves in the test segment of the road. Captured images are then processed to calculate vehicle speed. This information of the speed together with position of vehicle is then used to control the lighting system along the path that passes by the vehicle. Length of the road test segment is 1 m, the video camera is positioned about 1.1 m above the test segment, and the vehicle toy dimension is 13 cm \times 9.3 cm. In this model, the maximum speed that ARLS can handle is about 1.32 m/s, and the highest performance is obtained about 91% at speed 0.93 m/s.