Showing papers in "Artificial Intelligence in 2000"

TL;DR: It will be argued that the development of robust and scalable software systems requires autonomous agents that can complete their objectives while situated in a dynamic and uncertain environment, that can engage in rich, high-level social interactions, and that can operate within flexible organisational structures.

TL;DR: The assumptions and guarantees behind the generality of the SSH across environments and sensorimotor systems are described and evidence is presented from several partial implementations of the ssh on simulated and physical robots.

TL;DR: This article describes six wrapper classes, and uses a combination of empirical and analytical techniques to evaluate the computational tradeoffs among them, finding that most of their wrapper classes are reasonably useful, yet can rapidly learned.

TL;DR: This work shows how domain dependent search control knowledge can be represented in a temporal logic, and then utilized to effectively control a forward-chaining planner.

TL;DR: A formal definition in the situation calculus of such a programming language is presented and illustrated with some examples that includes facilities for prioritizing the execution of concurrent processes, interrupting the execution when certain conditions become true, and dealing with exogenous actions.

TL;DR: The goal of the research described here is to automatically create a computer understandable knowledge base whose content mirrors that of the World Wide Web, and several machine learning algorithms for this task are described, and promising initial results with a prototype system that has created a knowledge base describing university people, courses, and research projects.

TL;DR: The PageGather algorithm, which automatically identifies candidate link sets to include in index pages based on user access logs, is presented and it is demonstrated experimentally that PageGathering outperforms the Apriori data mining algorithm on this task.

TL;DR: This work uses dynamic Bayesian networks (with decision trees representing the local families of conditional probability distributions) to represent stochastic actions in an MDP, together with a decision-tree representation of rewards, and develops versions of standard dynamic programming algorithms that directly manipulate decision-Tree representations of policies and value functions.

TL;DR: This work introduces a new class of tractable CSPs based on the concept of hypertree decomposition recently developed in Database Theory, and provides a framework for comparing parametric decomposition-based methods according to tractability criteria and compares the most relevant methods.

TL;DR: This paper describes an iterative dynamic programming algorithm which computes an interval value function for a given bounded parameter MDP and specified policy and introduces {\em interval value functions\/} as a natural extension of traditional value functions.

TL;DR: In this article, Extremal Optimization is proposed to find high-quality solutions to hard optimization problems, inspired by self-organizing processes often found in nature, successively eliminating extremely undesirable components of sub-optimal solutions.

TL;DR: FSS-EBNA is an evolutionary, population-based, randomized search algorithm, and it can be executed when domain knowledge is not available, using Bayesian networks to factorize the probability distribution of the best solutions in a generation of the search.

TL;DR: In this article, the authors extend the framework of simple temporal problems studied originally by Dechter, Meiri and Pearl to consider constraints of the form x 1 −y 1 ≤r 1 ∨⋯∨ x n −y n ≤r n, where x 1,…,x n,y 1,…,y n are variables ranging over the real numbers, r 1,..,r n are real constants, and n≥1.

TL;DR: A framework of prioritized logic programming (PLP), which has a mechanism of explicit representation of priority information in a program, which increases the expressive power of logic programming and exploits new applications in knowledge representation.

TL;DR: It is shown that the planning problem in presence of incompleteness is indeed harder: it belongs to the next level of complexity hierarchy (in precise terms, it is Σ2P-complete), and under certain conditions, one of these approximations makes the problem NP-complete.

TL;DR: New accuracy estimation methods which are extensions of the k-fold cross-validation method are proposed, attempting to construct more representative folds, therefore reducing the bias of the resulting estimator.

TL;DR: A tableau calculus for the description logic ALC for checking the satisfiability of a concept with respect to a TBox with general axioms is proposed, and transformed into the first simple tableau-based decision procedure working in single exponential time.

TL;DR: Calculus provides an axiomatization of space which takes regions as primitive, rather than as constructions from sets of points, and Boolean connection algebras (BCAs) are introduced, it is proved that these structures are equivalent to models of the RCC axioms.

TL;DR: The rationale, architecture, and implementation of a next generation information gathering system—a system that integrates several areas of Artificial Intelligence research under a single umbrella is described.

TL;DR: The notions of minimal and simple social laws are presented, which capture two basic criteria for selecting among alternative (useful) social laws, and study these criteria in the framework of basic settings, namely Automated Guided Vehicles and Distributed Computing.

TL;DR: It is found that grouping morphological variants makes a significant improvement in retrieval performance, seen by grouping inflectional as well as derivational variants.

TL;DR: This introduction attempts to place the special-issue papers in context, but readers are cautioned that the eld is too young and moving too quickly for a comprehensive survey article.

TL;DR: It is derived from the previous result that for both RAs the authors “jump” from tractability to intractability if they add the universal relation to the set of all atoms, which indicates that the approach is promising.

TL;DR: A framework for the representation of visual knowledge in a robotic agent, with special attention to the understanding of dynamic scenes, is proposed, adopting the notion of conceptual space (CS—Gardenfors (2000)), a representation where information is characterized in terms of a metric space.

TL;DR: In this paper, the authors present an upper bound on the revenue obtained by a seller in any auction with a fixed number of participants, and show that this bound may be a least upper bound in some setups.

TL;DR: Experimental results show that an algorithm using a novel texture-based heuristic commitment technique together with extended edge-finding propagators achieves the best overall performance of the techniques tested.

TL;DR: In this article, a new approach to deal with default information based on the theory of belief functions is presented, inspired by Adams' epsilon semantics, where mass values are either close to 0 or close to 1.

TL;DR: A concept of dominance is developed which enables the comparison of different consistency tests as well as a simple method for proving dominance and an extensive comparison of all consistency tests are given.

TL;DR: This work addresses the problem of reasoning about preferences among properties in Reiter's default logic by transforming an ordered default theory into a second, standard default theory wherein the preferences are respected, in that defaults are applied in the prescribed order.