Showing papers in "Artificial Intelligence in 1988"

TL;DR: This work describes an extrinsic force that applies constraints derived from profiles of monocularly viewed objects and generalizes this constraint force to incorporate profile information from multiple views and use it to exploit binocular image data.

TL;DR: A simple logical framework for default reasoning by treating defaults as predefined possible hypotheses is presented, and it is shown how this idea subsumes the intuition behind Reiter's default logic.

TL;DR: It is shown that the notion of inductive bias in concept learning can be quantified in a way that directly relates to learning performance in the framework recently introduced by Valiant.

TL;DR: It is argued that the inference of history arises from a newly discovered duality between curvature extrema and symmetry structure, and a formal grammar is developed by which someone can infer the processes that produced the second stage from the first.

TL;DR: This paper presents an approach to default reasoning based on an extension to classical first-order logic augmented with a "variable conditional" operator for representing default statements that is argued to be superior to the first.

TL;DR: This paper presents a logic suitable for supporting belief revision systems, discusses the properties that a belief revision system based on this logic will exhibit, and presents a particular implementation of the model of a belief revisions system.

TL;DR: In this article, a theoretical framework and algorithms are presented that provide a basis for the study of definite (Horn) clauses, which hinge on a natural extension of θ-subsumption that forms a strong model of generalization.

TL;DR: It is proven that a complete inference algorithm for the BACK system would be computationally intractable, and it is shown that terminological reasoning is intracted for any system using a nontrivial description language.

TL;DR: This work examines how to formulate some path planning problems precisely, and reports algorithms to solve certain special cases.

TL;DR: This new tree-growth procedure is based on “conspiracy numbers” as a measure of the accuracy of the root minimax value of an incomplete tree, and unlike B∗ search, nonuniform growth is achieved without any modification of the static-board evaluator.

TL;DR: Data normalization is applied to the problem of modeling polygonal regions in the plane, and the hidden variable method is used to calculate arrangements of lines.

TL;DR: This paper provides a partial decision procedure that reports useful results and runs in polynomial time as an alternative to a complete, but potentially exponential-time decision procedure.

TL;DR: In this paper, the authors present a formal framework for computing motion strategies which are guaranteed to succeed in the presence of all three kinds of uncertainty: sensing errors, control errors, and uncertainty in the geometric models of the environment and of the robot.

TL;DR: In this paper, the authors draw attention to certain aspects of causal reasoning which are pervasive in ordinary discourse yet, based on the author's scan of the literature, have not received due treatment by logical formalisms of common-sense reasoning.

TL;DR: A computational theory of cognitive maps is developed which can explain some of the current findings about cognitive maps in the psychological literature and which provides a coherent framework for future development.

TL;DR: The domain constraint approach to qualification, coupled with the possible worlds approach described earlier, has the remarkable property that essentially no computational resources are required to confirm that an action is unqualified.

TL;DR: It is argued that explanation-based generalisation as recently proposed in the machine learning literature is essentially equivalent to partial evaluation, a well-known technique in the functional and logic programming literature.

TL;DR: SeeK2 as mentioned in this paper is an automated approach to knowledge base refinement, an important aspect of knowledge acquisition, described using empirical performance analysis, SEEK2 extends the capabilities of its predecessor rule refinement system.

TL;DR: An approach to default reasoning based on possibility theory is discussed, recently outlined by Ron Yager, to address the question of fusing a default value with a piece of incomplete but certain information which may only partially contradict the default value.

TL;DR: A new approach to evaluation function learning using classical pattern classification methods based on Bayesian learning is presented, which resulted in dramatic improvements over a linear evaluation function that has performed at world championship level.

TL;DR: It is shown that modal logics are on no firmer ground than first-order ones when equally endowed with substitutive self-reference, suggesting that there may still be remedies in propositional attitudes, hinging in part upon a distinction between “dynamic” and “static” notions of provability and belief.

TL;DR: In this paper, the authors pointed out the errors in the PC-3 algorithm and gave a correct one, and analyzed the time complexity and space complexity of the revised algorithm, which is the fastest known algorithm.

TL;DR: The proposed solutions to two problems in formal temporal reasoning, the qualification problem and the extended prediction problem, which, as argued in [22], subsume the infamous frame problem, make use of nonmonotonic logics.

TL;DR: This research formalizes this process for proposing new conjectures about a target analogue based on facts known about a source analogue as a rule of plausible inference and presents heuristics which guide this process towards efficiently postulating useful new conjecture.

TL;DR: In this article, the authors survey how operationality is defined and assessed in several explanation-based systems, and then present a more comprehensive definition of operationality, and describe an implemented system that incorporates their new definition and overcomes some of the limitations exhibited by current operationality assessment schemes.

TL;DR: The paper happens to present a new (deductive) mechanism for solving constraint satisfaction problems that is of interest in its own right, characterized as a bottom-up, lazy-evaluation technique which reduces any such problem to the problem of evaluating a database expression typically involving a number of joins.

TL;DR: It is argued that problems such as the frame problem arise from the conflicting desires to reason both rigorously and efficiently about the future, and does not depend on the particular underlying temporal formalism.

TL;DR: It is shown that extending unification to allow for unifying two things if they are nonmonotonically equal does exactly what is needed in such cases and allows for a clean method of noun-phrase reference determination.

TL;DR: This paper shows how a toleranced part can be represented as a network of tolerance zones and datums connected by arcs to which inequality constraints are attached, and can be extended to deal with assemblies of parts.