Showing papers in "Artificial Intelligence in 2001"

TL;DR: A more robust algorithm is developed called MixtureMCL, which integrates two complimentary ways of generating samples in the estimation of Monte Carlo Localization algorithms, and is applied to mobile robots equipped with range finders.

TL;DR: A family of heuristic search planners are studied based on a simple and general heuristic that assumes that action preconditions are independent, which is used in the context of best-first and hill-climbing search algorithms, and tested over a large collection of domains.

TL;DR: In this article, a new semantics, called Local Models Semantics, is proposed to provide a foundation for reasoning with contexts, which captures and makes precise the two main intuitions underlying contextual reasoning: (i) reasoning is mainly local and uses only part of what is potentially available (e.g., what is known, the available inference procedures), this part is what we call context; however, there is compatibility among the reasoning performed in different contexts.

TL;DR: While previous work only considered (1 + 1) EAs without any crossover, the EAs considered in this paper are fairly general, which use a finite population, crossover, mutation, and selection.

TL;DR: It will be seen how, in a portfolio setting, it can be advantageous to use a more “risk-seeking” strategy with a high variance in run time, such as a randomized depth-first search approach in mixed integer programming versus the more traditional best-bound approach.

TL;DR: It is shown that LAO* can be used to solve Markov decision problems and that it shares the advantage heuristic search has over dynamic programming for other classes of problems.

TL;DR: In this paper, the authors explore a framework for argumentation based on classical logic, in which an argument is a pair where the first item in the pair is a minimal consistent set of formulae that proves the second item (which is a formula).

TL;DR: The goal of this paper is to present Computer Go by showing the links between existing studies on Computer Go and different AI related domains: evaluation function, heuristic search, machine learning, automatic knowledge generation, mathematical morphology and cognitive science.

TL;DR: This paper presents a sustained investigation of the (in)famous Blocks World planning problem, and provides the level of understanding required for its effective use as a benchmark, and includes methods for generating random problems for systematic experimentation.

TL;DR: A new method of extraction is presented that captures nonmonotonic rules encoded in the network, and it is proved that such a method is sound and able to keep the soundness of the extraction algorithm.

TL;DR: The work presented in this paper aims at developing a system for automatic translation of gestures of the manual alphabets in the Arabic sign language using a collection of ANFIS networks, each of which is trained to recognize one gesture.

TL;DR: A high-level action description language that allows specification of sensing actions and their effects in its domain description and allows queries with conditional plans is developed and provably correct translations of domain description in the language to axioms in first-order logic are given.

TL;DR: GP-CSP is described, a system that does planning by automatically converting Graphplan's planning graph into a CSP encoding and solving it using standard CSP solvers, demonstrating that it is superior to both the Blackbox system and an ILP-based planner in a wide range of planning domains.

TL;DR: It is argued that defeaters that are too weak to defeat an inference outright may still diminish the strength of the conclusion, and a characterization of degrees of justification is proposed that captures the various principles endorsed and constructs an algorithm for computing them.

TL;DR: The analysis allows us to accurately predict the performance of IDA ∗ on actual problems such as the sliding-tile puzzles and Rubik's Cube, and shows that the asymptotic heuristic branching factor is the same as the brute-force branching factor.

TL;DR: Leveled Division Set (LDS) as discussed by the authors is a modification of the generalized Vickrey auction (GVA) protocol that utilizes reservation prices of auctioned goods for making decisions on whether to sell goods in a bundle or separately.

TL;DR: This paper analyzes the characteristics of existing techniques for meta-level control of anytime algorithms and develops a new framework for monitoring and control that handles effectively the uncertainty associated with the algorithm’s performance profile, the uncertaintyassociated with the domain of operation, and the cost of monitoring progress.

TL;DR: Using the challenging domain of Sokoban, this paper studies the effect of general search enhancements on program performance and leads to a new taxonomy of search enhancements, and a new framework for developing single-agent search applications.

TL;DR: This paper introduces a Robust Bayes Classifier able to handle incomplete databases with no assumption about the pattern of missing data and provides two scoring methods to rank intervals and a decision theoretic approach to trade off the risk of an erroneous classification and the choice of not classifying unequivocally a case.

TL;DR: Empirical evaluation of general purpose ATP systems to determine which systems work well for what types of problems and assigning ratings to problems based on their difficulty.

TL;DR: In this paper, a notion of strongest necessary and weakest sufficient conditions for propositional theory T and a proposition q is proposed, which can be naturally extended to arbitrary formulas and discuss some of their potential applications.

TL;DR: In this paper, the authors present and evaluate the power of a new scheme that generates search heuristics mechanically for problems expressed using a set of functions or relations over a finite set of variables.

TL;DR: A formal foundation for conceptual spaces is presented, how they are theoretically and practically linked to higher and lower representational levels are shown, and dynamics which allow the orbits of states in the space to solve appropriate meso level reasoning tasks are developed.

TL;DR: This paper re-interpret the classical in terms of a classic measure based on sets, the Marczewski-Steinhaus metric, and also in Terms of "proportional reduction of errors" (PRE) measures.

TL;DR: A family of algorithms combining tree-clustering with conditioning that trade space for time are proposed, useful for reasoning in probabilistic and deterministic networks as well as for accomplishing optimization tasks.

TL;DR: The computer program PRET automates this process by building a layer of artificial intelligence techniques around a set of traditional formal engineering methods, using a small, powerful meta-domain theory that tailors the space of candidate models to the problem at hand.

TL;DR: In this article, a framework for exploiting symmetry within depth-first search is proposed, leading to two heuristics for variable selection and a domain pruning procedure, which are then applied to two highly symmetric combinatorial problems, namely the Ramsey problem and the generation of balanced incomplete block designs.

TL;DR: A normative theory of interaction-negotiation in particular-among self-interested computationally limited agents where computational actions are game theoretically treated as part of an agent's strategy is developed.

TL;DR: In this article, the authors identify a series of measurable characteristics of local search behavior that are predictive of problem solving efficiency and introduce a new local search method, SDF ("smoothed descent and flood"), that builds upon the intuitions gained by their study.