Showing papers in "Artificial Intelligence in 1998"

TL;DR: A novel algorithm for solving pomdps off line and how, in some cases, a finite-memory controller can be extracted from the solution to a POMDP is outlined.

TL;DR: This paper presents several solutions to the problem of task allocation among autonomous agents, and suggests that the agents form coalitions in order to perform tasks or improve the efficiency of their performance.

TL;DR: This paper describes an approach that integrates both paradigms: grid-based and topological, which gains advantages from both worlds: accuracy/consistency and efficiency.

TL;DR: This dissertation discusses the application domain of decision tree learning and extends it towards the first order logic context of Inductive Logic Programming.

TL;DR: The Remote Agent is described, a specific autonomous agent architecture based on the principles of model-based programming, on-board deduction and search, and goal-directed closed-loop commanding, that takes a significant step toward enabling this future of space exploration.

TL;DR: A logical model of the mental states of the agents based on a representation of their beliefs, desires, intentions, and goals is presented and a general Automated Negotiation Agent is implemented, based on the logical model.

TL;DR: In this paper, the ontological categories for social action, structure, and mind are introduced, and different kinds of coordination (reactive versus anticipatory; unilateral versus bilateral; selfish versus collaborative) are characterised.

TL;DR: This work presents three model selection criteria, using information theoretic entropy in the spirit of the minimum description length principle, based on the principle of indifference combined with the maximum entropy principle, thus keeping external model assumptions to a minimum.

TL;DR: It is shown that in general, the reasoning problem for recursive carin - A LCNR knowledge bases is undecidable, and the constructors of ALCNR causing the undecidability is identified.

TL;DR: Artificial intelligence will have less difficulty in modelling the generation of new ideas than in automating their evaluation, and will be able to make transformations that enable thegeneration of previously impossible ideas.

TL;DR: This paper vindicates Belnap's thesis by showing that the logical role that the four-valued structure has among Ginsberg's bilattices is similar to the roles that the two-valued algebra has among Boolean algebras.

TL;DR: A simple algebraic property is described which characterises all possible constraint types for which strong k-consistency is sufficient to ensure global consistency, for each k > 2.

TL;DR: It is argued that similarity must be augmented by deeper, adaptation knowledge about whether a case can be easily modified to fit a target problem, and implemented in a new technique, called adaptation-guided retrieval (AGR), which provides a direct link between retrieval similarity and adaptation needs.

TL;DR: Computational methods for the rough analysis of databases, a relatively new mathematical tool for use in computer applications in circumstances which are characterized by vagueness and uncertainty, are discussed.

TL;DR: A new algorithm, called Complete Karmarkar-Karp (CKK), is presented, that optimally solves the general number-partitioning problem, and significantly outperforms the best previously-known algorithms for large problem instances.

TL;DR: An experimental setup is introduced for concretising and validating specific mechanisms based on a set of principles and a general architecture that may explain how language and meaning may originate and complexify in a group of physically grounded distributed agents.

TL;DR: In this article, the authors describe a multimodal presentation system WIP which allows the generation of alternate presentations of the same content taking into account various contextual factors, and discuss how the plan-based approach to presentation design can be exploited so that graphics generation influences the production of text.

TL;DR: It is shown that the epistemic operator formalizes procedural rules, as provided in many knowledge representation systems, and enables sophisticated query formulation, including various forms of closed-world reasoning.

TL;DR: The computation on a general Bayesian network with convex sets of conditional distributions is formalized as a global optimization problem and it is shown that such a problem can be reduced to a combinatorial problem, suitable to exact algorithmic solutions.

TL;DR: This paper shows that approximating MAPs with a constant ratio bound is also NP-hard, and applies to networks with constrained in-degree and out-degree, applies to randomized approximation, and even applies if the ratio bound, instead of being constant, is allowed to be a polynomial function of various aspects of the network topology.

TL;DR: This work identifies restrictions on the underlying state-transition graph which can tractably be tested and presents a planning algorithm which is correct and runs in polynomial time under these restrictions, and presents an exhaustive map of the complexity results for planning under all combinations of four previously studied syntactical restrictions and five new structural restrictions.

TL;DR: It is shown that equilibrium point strategies for optimal play exist for this model, and an algorithm capable of computing such strategies is defined, and this model allows for clearly state the limitations of such architectures in producing expert analysis.

TL;DR: The results show that using bilingual corpora for automated extraction of term equivalences in context outperforms dictionarybased methods and is comparable to that of other statistical corpus-based methods.

TL;DR: In this paper, it is shown how probabilistic conditionals allow a new and constructive approach to the principle of minimum cross-entropy, and four principles that describe their handling in a reasonable and consistent way are developed.

TL;DR: Sketch IT as discussed by the authors is a program that transforms a single sketch of a mechanical device into multiple families of new designs using a parametric model augmented with constraints that ensure the desired behavior.

TL;DR: A framework in which different dimensions for temporal model-based diagnosis can be analyzed at the knowledge level is defined, pointing out which are the alternatives along each dimension and showing in which cases each one of these alternatives is adequate.

TL;DR: In this article, the authors define a physical event space over which probabilities are defined, and then introduce an identity criterion, which selects those events that correspond to identity between observed objects, and compute the probability that any two objects are the same, given a stream of observations of many objects.

TL;DR: A model-based Averagereward Reinforcement Learning method called H-learning is introduced and it is shown that it converges more quickly and robustly than its discounted counterpart in the domain of scheduling a simulated Automatic Guided Vehicle (AGV).

TL;DR: It is proved that most approaches to tractable temporal constraint reasoning can be encoded as Horn DLRs, including the ORD-Horn algebra by Nebel and Burckert and the simple temporal constraints by Dechter et al.

TL;DR: ‘4-D approach’ integrating expectation-based methods from systems dynamics and control engineering with methods from AI has allowed to create vehicles with unprecedented capabilities in the technical realm.