Showing papers in "Artificial Intelligence in 2002"

TL;DR: The bias-variance decomposition of the error is provided in this paper, which shows that the success of GASEN may lie in that it can significantly reduce the bias as well as the variance.

TL;DR: The algorithm allows combinatorial auctions to scale up to significantly larger numbers of items and bids than prior approaches to optimal winner determination by capitalizing on the fact that the space of bids is sparsely populated in practice.

TL;DR: A novel logic program like language, weight constraint rules, is developed for answer set programming purposes which offers a competitive implementation of the stable model semantics for normal programs and attractive performance for problems where the new types of rules provide a compact representation.

TL;DR: This article introduces the WoLF principle, “Win or Learn Fast”, for varying the learning rate, and examines this technique theoretically, proving convergence in self-play on a restricted class of iterated matrix games.

TL;DR: Algorithms that use an information-theoretic analysis to learn Bayesian network structures from data, requiring only polynomial numbers of conditional independence tests in typical cases are provided.

TL;DR: In this article, the authors present a trade-off strategy where multiple negotiation decision variables are traded-off against one another (e.g., paying a higher price in order to obtain an earlier delivery date or waiting longer to obtain a higher quality service).

TL;DR: This paper focuses on sentence compression, a simpler version of this larger challenge, and aims to achieve two goals simultaneously: the authors' compressions should be grammatical, and they should retain the most important pieces of information.

TL;DR: Applications of the answer set programming method to planning are related to the line of research on the frame problem that started with the invention of formal nonmonotonic reasoning in 1980.

TL;DR: The design considerations and architecture of the poker program Poki are described, which is a program capable of playing reasonably strong poker, but there remains considerable research to be done to play at world-class level.

TL;DR: It is concluded that decision complexity is more important than state-space complexity as a determining factor in games solved in the domain of two-person zero-sum games with perfect information and there is a trade-off between knowledge-based methods and brute-force methods.

TL;DR: A short introduction to logic programming approach to knowledge representation and reasoning is given to help the reader to develop a ‘feel’ for the field's history and some of its recent developments.

TL;DR: This work describes a new technique for designing more accurate admissible heuristic evaluation functions, based on pattern databases, that can be improved on the Fifteen Puzzle by a factor of over 2000, and to find optimal solutions to 50 random instances of the Twenty-Four Puzzle.

TL;DR: Global consistency maintaining is only one among several functionalities that should be offered by a CSP-based platform in order to help the user in her task; other important functionalities include providing explanations for some user's choices and ways to restore consistency.

TL;DR: The main result shows that the related decision problem is Π(p)2 -complete and is obtained solely via the graph-theoretic representation of an argument system, thus independent of the specific logic underpinning the reasoning theory.

TL;DR: The AUTHOR architecture is designed, implemented, and empirically evaluate a comprehensive computational model of narrative prose generation (NPG) that can create natural language stories for educational and entertainment environments and shows that AUTHOR is a well-defined and modularized architecture.

TL;DR: The consensus operator as discussed by the authors provides a method for combining possibly conflicting beliefs within the Dempster-Shafer belief theory, and represents an alternative to the traditional Dempsters rule.

TL;DR: This paper presents a multi-agent oriented method for solving CSPs (Constraint Satisfaction Problems), and examines the performance of this method in deriving solutions based on behavior prioritization and different selection probabilities.

TL;DR: Tabu decision repair as discussed by the authors performs a local search over partial assignments instead of complete assignments, and uses filtering techniques and conflict-based techniques to efficiently guide the search, which benefits from both classical approaches: a priori pruning of the search space from filtering-based search and possible repair of early mistakes from local search.

TL;DR: This paper views machine learning as a tool in a programmer's toolkit, and considers how it can be combined with other programming techniques to achieve and surpass world-class backgammon play.

TL;DR: This note studies the efficiency of the emergence of social conventions in complex networks, that is, how fast conventions are reached and finds out that scale-free graphs make the system as efficient as fully connected graphs.

TL;DR: In this paper, an active classifier is used to obtain the values of additional attributes and the penalty incurred if the classifier outputs the wrong classification, which can be useful when deciding whether to gather information relevant to a medical procedure or experiment.

TL;DR: For example, MAVEN as discussed by the authors combines a selective move generator, simulations of likely game scenarios, and the B* algorithm to produce a world-championship-caliber Scrabble-playing program.

TL;DR: A new path-consistency algorithm for combining RCC-8 relations and qualitative size relations is proposed and it is shown that deciding satisfiability for the combined sets of constraints is NP-hard, even when only the R CC-8 basic relations are used.

TL;DR: A number of look-back variants including backjumping and constraint recording which recognize and avoid some unnecessary explorations of the search space are described, including forward checking and dynamic variable ordering.

TL;DR: This work presents the concept of explanation in a deductive way, and defines multiple revision operators with respect to sets of sentences (representing explanations), giving representation theorems.

TL;DR: A novel approach for successfully harnessing the advantages of the two competing paradigms to develop planners that are significantly more powerful than either of the approaches, and presents AltAlt, a planner literally cobbled together from the implementations of Graphplan and state search style planners.

TL;DR: This work shows that a number of relevant parameterized problems in these areas are fixed-parameter tractable and shows that circumscriptive inference from a general propositional theory, when the attention is restricted to models of bounded size, is fixed- parameters intractable and is actually complete for a novel fixed- parameter complexity class.

TL;DR: In this article, the complexity of causal relationships in Pearl's structural models has been analyzed, focusing on causality between variables, event causality, and probabilistic causality.

TL;DR: The relationship between debugging using a dependency-based model and program slicing is discussed and it is obtained that slices of a program in a fault situation are equivalent to conflicts in model-based debugging.

TL;DR: This paper analyzes the computational complexity of credulous and sceptical reasoning under the semantics of admissible and preferred arguments for (the propositional variant of) the instances of the abstract framework capturing theorist, circumscription, logic programming, default logic, and autoepistemic logic.