Showing papers in "Artificial Intelligence in 2011"

TL;DR: The Monte-Carlo revolution in computer Go is surveyed, the key ideas that led to the success of MoGo and subsequent Go programs are outlined, and for the first time a comprehensive description, in theory and in practice, of this extended framework for Monte- Carlo tree search is provided.

TL;DR: A clearer picture of the frontier between decidability and non-decidability of reasoning with positive rules, which have the same logical form as tuple-generating dependencies in databases and as conceptual graph rules are provided.

TL;DR: A natural extension of Dung's well-known model of argument systems in which attacks are associated with a weight, indicating the relative strength of the attack is introduced, and a framework of weighted argument systems is presented.

TL;DR: This paper defines a semantics for programs with arbitrary aggregates in the full ASP language allowing also for disjunction in the head (disjunctive logic programming - DLP), and proves that this semantics guarantees the minimality (and therefore the incomparability) of answer sets, and demonstrates that it coincides with the standard answer set semantics on aggregate-free programs.

TL;DR: This paper reviews previous ideas on various links, introduces the concept of cyber-physical society, proposes the ideal of the CP^3SME including its definition, characteristics, and multi-disciplinary revolution, and explores the methodology of linking through spaces for cyber- physical-socio intelligence.

TL;DR: A new definition of the concept of a stable model is proposed, which covers many constructs used in answer set programming and, unlike the original definition, refers neither to grounding nor to fixpoints.

TL;DR: This editorial of the special issue ''Representing, Processing, and Learning Preferences: Theoretical and Practical Challenges'' surveys past and ongoing research on preferences in AI, including references and pointers to the literature.

TL;DR: This paper develops a basic constraint programming model for specifying frequent itemsets and shows that this model can easily be extended to realize the other settings and discusses a number of interesting new research questions and challenges raised by the declarative constraint programming approach to data mining.

TL;DR: This paper proposes a novel approach to decentralised coordination, that is able to efficiently compute solutions with a guaranteed approximation ratio, and presents two generic pruning techniques to reduce the amount of computation that agents must perform when using the max-sum algorithm.

TL;DR: This paper proposes desirable properties of attack relations in the form of postulates and classify several well-known attack relations from the literature with regards to the satisfaction of these postulates.

TL;DR: The experimental results conclude that EWLS and EWCC are largely competitive on DIMACS benchmarks, where they outperform other current best heuristic algorithms on most hard instances, and dominate on the hard random BHOSLIB benchmarks.

TL;DR: The flourishing role of robots in society is described-from security to sex-and the numerous ethical and social issues are surveyed, which are located in three broad categories: safety & errors, law & ethics, and social impact.

TL;DR: An experimental evaluation of the on-the-fly directed search algorithm, K^@?

TL;DR: It is proved that conflict-driven clause-learning SAT solvers yield proof systems that indeed p-simulate general resolution without the need for any additional techniques, and this result can be generalized to certain other practical variations of the solvers, which are based on different learning schemes and restart policies.

TL;DR: This paper proposes a well-founded semantics for nondisjunctive hybrid MKNF knowledge bases that promises to provide better efficiency of reasoning, and that is compatible with both the OWL-based semantics and the traditional Well-Founded Semantics for logic programs.

TL;DR: A general theory is formulated to cope with how the status of arguments in a system evolves when the system is updated based on a new concept: the division of an argumentation framework and it is proved that under a certain semantics that satisfies the directionality criterion (complete, preferred, ideal, or grounded semantics), the extensions of the updated framework are equal to the result of a combination of the extension of an unaffected sub-framework.

TL;DR: A method is proposed which manually determines the quality and automatically compares its coverage with ResearchCyc, one of the largest manually created ontologies, and the lexical database WordNet, which shows that the taxonomy compares favorably in quality and coverage with broad-coverage manually created resources.

TL;DR: An online algorithm for planning under uncertainty in multi-agent settings modeled as DEC-POMDPs that can solve problems that are too large for the best existing offline planning algorithms and it outperforms the best online method, producing much higher value with much less communication in most cases.

TL;DR: The SampleSearch scheme is proposed that augments sampling with systematic constraint-based backtracking search, and a weighting scheme is derived which yields an unbiased estimate of the desired statistics (e.g., probability of evidence).

TL;DR: The notion of embeddings into voting rules: functions that receive an agent's utility function and return the agent's vote are presented and it is established that very low distortion can be obtained using randomizedembeddings, especially when the number of agents is large compared to thenumber of alternatives.

TL;DR: This paper begins with the first serious attempts to create strong computerised poker players by constructing knowledge-based and simulation-based systems, followed by the use of computational game theory to construct robust poker agents and the advances that have been made in this area.

TL;DR: The notion of strong equivalence between abstract argumentation frameworks was introduced in this article, where it is assumed that the frameworks in question are incomplete, i.e. further information might be added later to both frameworks simultaneously.

TL;DR: A new decision-theoretic model for decentralized sparse-interaction multiagent systems, Dec-SIMDPs, is contributed that explicitly distinguishes the situations in which the agents in the team must coordinate from those in which they can act independently.

TL;DR: This paper offers a new approach to belief change associated with performing actions that addresses some of the shortcomings of existing approaches to this problem.

TL;DR: To make the process effective when only a single problem instance needs to be solved, this work presents a variation in which the bootstrap learning of new heuristics is interleaved with problem-solving using the initial heuristic and whatever heuristic have been learned so far.

TL;DR: This research note introduces a graded BDI agent development framework, g-BDI for short, that allows to build agents as multi-context systems that reason about three fundamental and graded mental attitudes.

TL;DR: This work develops a novel approach for computing generalizations of classical plans by identifying sequences of actions that will make measurable progress when placed in a loop and demonstrates the scope and scalability of the proposed approach on a wide range of planning problems.

TL;DR: It is argued that the Turing test provides a solution to the forensic problem of anthropomorphism and is examined in relation to the current dispute between human-level AI and 'mindless intelligence'.

TL;DR: An existing account of motivation in intelligent systems is extended to provide a framework for surveying relevant literature in AI and robotics, and a design for a new motive management framework is sketched which goes beyond the current state of the art.