Showing papers on "Abductive reasoning published in 1988"
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.
790 citations
07 Jun 1988
TL;DR: An approach to abductive inference developed in the TACITUS project has resulted in a dramatic simplification of how the problem of interpreting texts is conceptualized and suggests an elegant and thorough integration of syntax, semantics, and pragmatics.
Abstract: An approach to abductive inference developed in the TACITUS project has resulted in a dramatic simplification of how the problem of interpreting texts is conceptualized. Its use in solving the local pragmatics problems of reference, compound nominals, syntactic ambiguity, and metonymy is described and illustrated. It also suggests an elegant and thorough integration of syntax, semantics, and pragmatics.
267 citations
24 Jul 1988
TL;DR: The authors formulate the general task of abduction as a nonlinear nonmonotonic constrained optimization problem, and propose a neural network for solving it, which finds that representing the abductive problem as minimization of an energy function requires a network of order greater than two.
Abstract: The authors formulate the general task of abduction as a nonlinear nonmonotonic constrained optimization problem. They then consider a linear monotonic version of the general abductive problem, and propose a neural network for solving it. The neurons in this network represent the elementary explanatory hypotheses and the connections between them are symmetric. It is found that representing the abductive problem as minimization of an energy function requires a network of order greater than two. The authors outline a second 'neural' architecture that reflects the structure of the abductive problem. In this model, the constraints of the problem are represented explicitly, the network is composed of functional modules, and the connections between the 'neurons' are asymmetric. Suggestions are made as to how this second-order network can accommodate certain interactions between the elementary hypotheses. >
54 citations
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4 citations
Journal Article•
TL;DR: Anne Gardner is both a lawyer and a coruputer scientist, and her book is a revision of her 1984 dissertation submitted to Stanford's Department of Computer Science.
Abstract: Anne Gardner is both a lawyer and a coruputer scientist. She obtained her J.D. from Stanford in 1958, ar:d her book is a revision of her 1984 dissertation submitted to Stanford's Department of Computer Science. She plays, in part, the role of pioneer; artificial intelligence (\"AI\") techniques have not yet been widely applied to perform legal tasks. Therefore Gardner, and this review, first describe and define the field, then demonstrate a working model in the domain of contract offer and acceptance.
2 citations
22 Aug 1988
TL;DR: It is demonstrated that a natural language understanding system using the integrated parsing engine as a subsystem can pursue a guided search for most plausible interpretation by making use of syntax, semantics, and contextual information.
Abstract: In this paper, we present an inference mechanism called the integrated parsing engine which provides a uniform abductive inference mechanism for natural language understanding. It can (1) make plausible assumptions, (2) reason with multiple alternatives, (3) switch the search process to the maximally plausible alternative, (4) detect contradiction and tame conclutions which depend on inconsistent assumptions, and (5) update plausibility factor of each belief based on new observations. We demonstrate that a natural language understanding system using the integrated parsing engine as a subsystem can pursue a guided search for most plausible interpretation by making use of syntax, semantics, and contextual information.
2 citations
01 Jan 1988
TL;DR: Mathematical epidemiological models do not take into account that diagnostic reasoning plays a key role in Statistical Medical Data Production.
Abstract: Mathematical epidemiological models do not take into account that diagnostic reasoning plays a key role in Statistical Medical Data(SMD) Production.
08 Aug 1988
TL;DR: This paper constructs an incomplete reasoning model for expert systems, utilizing the chniques of inexact reasoning, and gives a practical AIR model basing on MYCIN's CF theory, which is implemented in a real expert system, i.e. Diesel Failure Diagnosis (DFD) expert system.
Abstract: "Incomplete reasoning, is such a kind of reasoning that we could gain a conclusion with some premises omitted. The conclusions thus acquired are, in fact, conjectures. They need to be verified or refuted. We call the verification or refutation process "regression". In contrast to the complete reasoning of formal logic, incomplete reasoning tallys with our habit of reasoning more closely. In fact, studies show that any reasoning process is a kind of incomplete reasoning. In this paper, we construct an incomplete reasoning model (AIR model) for expert systems, utilizing the chniques of inexact reasoning. We give a practical AIR model basing on MYCIN's CF theory, which is implemented in a real expert system, i.e. Diesel Failure Diagnosis (DFD) expert system. Studies show that the incomplete reasoning makes the reasoner focus his (its) attention, and so raises the efficiency of reasoning. The mixture of incomplete reasoning with inexact reasoning in expert systems could deal with not only the inexactness of reasoning introduced by expert knowledges or initial evidences, but also the incompletity of reasoning caused by system's active conjecturing.
01 Jan 1988
TL;DR: This paper constructs an incomplete reasoning and calls the verification or refutation process "regression", which shows that any reasoning process is a kind of incomplete reasoning.
Abstract: "Incomplete reasoning" is such a kind of reasoning that we could gain a conclusion with some premises omitted. The conclusions thus acquired are, in fact, conjectures. They need to he verified or refuted. We call the verification or refutation process "regression". In contrast to the complete reasoning of formal logic, incomplete reasoning tallys with our habit of reasoning more closaly. In fact, studies show that any reasoning process is a kind of incomplete reasoning. In this paper, we construct an incomplete reasoning