Showing papers in "Artificial Intelligence in 2009"

TL;DR: This paper extends Dung's theory to accommodate arguments that claim preferences between other arguments, thus incorporating meta-level argumentation based reasoning about preferences in the object level and formalising argument based logic programming with defeasible priorities in the extended theory.

TL;DR: This paper articulates the optimistic and pessimistic decision criteria defined in qualitative decision making under uncertainty, in terms of an argumentation process.

TL;DR: The results indicate significant progress in the field, but they also reveal that some important issues remain open and require further research, such as dealing with strong constraints and computing high quality plans in metric-time domains and domains involving soft goals or constraints.

TL;DR: It is argued that the biological foundations of enactive cognitive science can provide the conceptual tools that are needed to diagnose more clearly the shortcomings of current embodied AI and provide a promising way of eventually overcoming the current limitations of embodied AI, especially in terms of providing fuller models of natural embodied cognition.

TL;DR: A prototype, called BT-45, is presented, which generates textual summaries of about 45 minutes of continuous physiological signals and discrete events and brings together techniques from the different areas of signal processing, medical reasoning, knowledge engineering, and natural language generation.

TL;DR: An efficient method for translating planning tasks specified in the standard PDDL formalism into a concise grounded representation that uses finite-domain state variables instead of the straight-forward propositional encoding is introduced.

TL;DR: A way to deal harmoniously with a larger set of problems while giving a fine control on the solving mechanisms is given, to give more freedom in solver design by introducing programming concepts where only configuration parameters were previously available.

TL;DR: It is shown that this clarifies several difficult issues in plan recognition including the execution of multiple interleaved root goals, partially ordered plans, and failing to observe actions.

TL;DR: This chapter studies the main issues regarding user profiles from the perspectives of these research fields, and examines what information constitutes a user profile; how the user profile is represented; how it is acquired and built; and how the profile information is used.

TL;DR: This work investigates the problem of coalitional manipulation in elections, and puts forward efficient algorithms for the problem in Scoring rules, Maximum and Plurality with runoff, and analyzes their windows of error.

TL;DR: This paper proposes a method for compiling TEP planning problems into simpler domains containing only final-state preferences and metric functions, and proposes a variety of heuristic functions for planning with final- state preferences, together with an incremental best-first planning algorithm.

TL;DR: The key contribution of this work is the idea that global structural information of human activities can be encoded using a subset of their local event subsequences, and that this encoding is sufficient for activity-class discovery and classification.

TL;DR: It is shown that while typically less tractable than credulous admissibi-lity semantics, the natural decision problems arising with this extension-based model can, perhaps surprisingly, be decided more efficiently than sceptical preferred semantics.

TL;DR: This paper exploits efficient duration modeling using the novel Coxian distribution to form the Coxian hidden semi-Markov model (CxHSMM) and applies it to the problem of learning and recognizing ADLs with complex temporal dependencies, demonstrating that Coxian modeling outperforms a range of baseline models for the task of activity segmentation.

TL;DR: This is the first of two papers presenting and evaluating the power of a new framework for combinatorial optimization in graphical models, based on AND/OR search spaces, and introduces a new generation of depth-first Branch-and-Bound algorithms that explore the AND/or search tree using static and dynamic variable orderings.

TL;DR: This article proposes an approach that translates an expressive subset of the PPDDL representation to a first-order MDP (FOMDP) specification and then derives a domain-independent policy without grounding at any intermediate step and presents proof-of-concept results of this approach.

TL;DR: This paper proposes yet another alternative proof of Arrow's Impossibility Theorem using induction to reduce the theorem to the base case with 3 alternatives and 2 agents and then use computers to verify the base cases.

TL;DR: In this paper, a mediator is defined to be a reliable entity, which can ask the agents for the right to play on their behalf, and is guaranteed to behave in a pre-specified way based on messages received from the agents.

TL;DR: A generalization of the region connection calculus based on fuzzy set theory is presented, and it is revealed that reasoning in this fuzzy RCC is NP-complete, thus preserving the computational complexity of reasoning in the RCC.

TL;DR: An epistemic model of the uncertainty associated with vague concepts and an interpretation of label semantics which incorporates prototype theory by introducing uncertain thresholds on the distance between elements and prototypes for description labels are introduced.

TL;DR: This work presents a case-based reasoning approach for cooperative action selection, which relies on the storage, retrieval, and adaptation of example cases for coordinated attacking passes between robots in the presence of the defending opponent robots.

TL;DR: It is shown that different forms of knowledge updates may be represented via knowledge forgetting, and how knowledge forgetting can be used in formalizing and reasoning about knowledge games with bounded memory.

TL;DR: The contribution is to describe the first competitive planner capable of solving problems that require concurrent actions, Crikey, that can successfully tackle a wide range of temporal problems.

TL;DR: This work defines ranking games as a class of n-player normal-form games with a payoff structure reflecting the players' von Neumann-Morgenstern preferences over their individual ranks, and investigates the computational complexity of a variety of common game-theoretic solution concepts in ranking games.

TL;DR: This paper proposes an extension of DL languages with description graphs-a knowledge modeling construct that can accurately describe objects with parts connected in arbitrary ways, and presents practical evidence that such a logic can be used to model nontrivial structured objects.

TL;DR: This work develops, investigates, and compares two logic-based knowledge representation formalisms for reasoning about coalitional games, and shows that, while cgl is more expressive than mcgl, the former can only be used to reason aboutcoalitional games with finitely many outcomes, while the latter can be used also about games with infinitely many outcomes.

TL;DR: In this paper, the authors developed a model to support group dynamics of autonomous synthetic characters (SGD model) inspired by theories developed in human social psychological sciences, which was used in a collaborative computer game that was tested with users.

TL;DR: The previous results related to weighted A^* from authors like Pohl, Pearl, Kim, Likhachev and others are presented in a more condensed and unifying form and a novel general bound on suboptimality of the result is derived.

TL;DR: A both theoretically grounded and computationally tractable ABN framework that allows agents to argue, negotiate, and resolve conflicts relating to their social influences within a multi-agent society and exemplifies the versatility of the framework and its ability to facilitate complex argumentation dialogues within artificial agent societies.

TL;DR: An original look-ahead approach that allows to guide backtrack search toward sources of conflicts and, as a side effect, to obtain a behavior similar to a backjumping technique is presented.