Showing papers in "Artificial Intelligence in 1987"

TL;DR: This paper proposes a logic for default reasoning, develops a complete proof theory and shows how to interface it with a top down resolution theorem prover, and provides criteria under which the revision of derived beliefs must be effected.

TL;DR: The theory accommodates diagnostic reasoning in a wide variety of practical settings, including digital and analogue circuits, medicine, and database updates, and reveals close connections between diagnostic reasoning and nonmonotonic reasoning.

TL;DR: SOAR as discussed by the authors is an implemented proposal for such an architecture, which is described in detail in the paper "SOAR: An Implementation of Cognitive Architecture for Artificial Intelligence" and demonstrated in the SOAR project.

TL;DR: The diagnostic procedure presented in this paper is model-based, inferring the behavior of the composite device from knowledge of the structure and function of the individual components comprising the device.

TL;DR: A new view of problem solving motivated by a new kind of truth maintenance system based on manipulating assumption sets is presented, which is possible to work effectively and efficiently with inconsistent information, context switching is free, and most backtracking is avoided.

TL;DR: It is argued that similar mechanisms and constraints form the basis for recognition in human vision.

TL;DR: A new and more symmetric version of the circumscription method of nonmonotonic reasoning first described in (McCarthy 1980) and some applications to formalizing common sense knowledge are presented.

TL;DR: Theorems that suggest that efficient general purpose planning with more expressive action representations is impossible are presented, and ways to avoid this problem are suggested.

TL;DR: A model and proof theories, a proof procedure, and applications for one non-monotonic logic are developed, and it is proved the completeness of the non-Monotonic predicate calculus and the decidability of theNon- monotonic sentential calculus.

TL;DR: In these logics, the set of beliefs of an agent does not necessarily contain all valid formulas, which makes them more suitable than traditional logics for modelling beliefs of humans (or machines) with limited reasoning capabilities.

TL;DR: This paper determines the completeness and running time of constraint propagation for various kinds of labels and constraints in AI systems of various kinds.

TL;DR: This paper identifies classes of problems that lend themselves to easy solutions, and develops algorithms that solve these problems optimally by generating heuristic advice based on both the sparseness found in the constraint network and the simplicity of tree-structured CSPs.

TL;DR: This paper provides axioms for a simple problem in temporal reasoning which has long been identified as a case of default reasoning, thus presumably amenable to representation in nonmonotonic logic, and finds that the logics considered are inherently incapable of representing this kind ofdefault reasoning.

TL;DR: An exact solution for the system of partial differential equations proposed by Horn and Schunck (1981) is given at gray value corners and extrema and results in a modification of the “oriented smoothness” approach of Nagel (1983) which thereby becomes considerably simpler.

TL;DR: Several general problems of natural-language processing that were faced in constructing the TEAM system are discussed, including quantifier scoping, various pragmatic issues, and verb acquisition.

TL;DR: It is presented that planning can be viewed as problem-solving search using subgoals, macro-operators, and abstraction as knowledge sources and an analysis of abstraction concludes that abstraction hierarchies can reduce exponential problems to linear complexity.

TL;DR: The method proposed renders stochastic simulation a powerful technique of coherent inferencing, especially suited for tasks involving complex, nondecomposable models where “ballpark” estimates of probabilities will suffice.

TL;DR: The system described in this paper extends classical predicate-calculus data bases, such as those used by PROLOG, to deal with time in an efficient and natural manner to perform the temporal analog of (static) reason maintenance.

TL;DR: The impact of “uncertain evidence” can be (formally) represented by Dempster conditioning, in Shafer's framework, in the framework of convex sets of classical probabilities by classical conditionalization.

TL;DR: This analysis gives an interpretive semantics to default logic, and yields insight into the nature of defaults in autoepistemic reasoning.

TL;DR: This paper provides a solution to the frame problem, and to the related problem that it is not always reasonable to explicitly specify all of the consequences of actions, by keeping a single model of the world that is updated when actions are performed.

TL;DR: In this article, the exact implementation of Dempster's rule in the case of hierarchical evidence is given, which is computationally efficient and makes the approximation suggested by Gordon and Shortliffe unnecessary.

TL;DR: This paper examines this method by looking closely at two of the more influential formalisms featuring reified propositions, those of Allen and McDermott, and presents a new formalism that does not suffer from these disadvantages.

TL;DR: Diagrams are of substantial benefit to WHISPER, a computer problem-solving system, in testing the stability of a “blocks world” structure and predicting the event sequences which occur as that structure collapses.

TL;DR: The heuristic search is described, with particular emphasis on the heuristic strategies that evaluate local geometric information for planning paths along level C-surfaces and their intersection manifolds, and for reasoning about motions with three degrees of rotational freedom.

TL;DR: It is shown that, in singly connected networks, the most satisfactory explanation can be found in linear time by a message-passing algorithm similar to the one used in belief updating.

TL;DR: An implemented learning system whose inputs are images of two-dimensional shapes is reported on, which can learn disjunctions, and can learn concepts using only positive examples, and discusses learnability and the pervasive importance of representational hierarchies.

TL;DR: Sierra is a program that learns procedures incrementally from examples, where an example is a sequence of actions, where a lesson is a set of examples that is guaranteed to introduce only one subprocedure.

TL;DR: It is argued that formal systems, such as Reiter's default logic, provide useful tools for the specification and description of nonmonotonic systems and that formalizing such systems may yield dividends in terms of both clarity and correctness.

TL;DR: An approach for representing knowledge about defaults and prototypical properties is presented by adding to first-order logic a “variable conditional” operator to express relations between entities and prototypicals properties of such entities.