Artificial Intelligence Logic Research Articles

Handbook of logic in artificial intelligence and logic programming: v.1: Logical foundations; v.2: Deduction methodologies; v.3: Nonmonotonic reasoning and uncertain reasoning

Handbook of logic in artificial intelligence and logic programming: v.1: Logical foundations; v.2: Deduction methodologies; v.3: Nonmonotonic reasoning and uncertain reasoning

Artificial Intelligence through Logic?

The arguments for and against the use of logic in artificial intelligence are discussed, including the argument on the declarative versus procedural view of knowledge. The author sees that both arguments regarding the suitability or unsuitability of logic for AI have their merits. A position that integrates both arguments is presented which includes the paradigm of logic programming. Beyond symbolic AI, we have also incorporated connectionism in the integration.

FLAI'93: 8th Austrian Conference on Fuzzy Logic in Artificial Intelligence

FLAI'93: 8th Austrian Conference on Fuzzy Logic in Artificial Intelligence

Automated reasoning and nonclassical logics: Introduction

In the mid-1980s researchers in the computer science and artificial intelligence communities began to take an interest in logics studied by logicians and philosophers since at least Aristotle in the fourth century BC. These are logics such as deontic, epistemic, intuitionistic, modal, paraconsistent, relevant, and temporal logics, all of which are usually collected under the somewhat unsatisfactory name of nonclassical, or nonstandard, logics. During the past several years, the interest in nonclassical logics in these communities has increased enormously, as anyone remotely familiar with them will know. To the reader unfamiliar with this territory we recommend [3] for a masterful historical survey of it from the time of Aristotle. A huge amount of modem work has been done in the field during this century, and the total literature would probably exceed 4000 items. Central research directions in this field have been surveyed in many places, but in our opinion the definitive work is Gabbay and Guenthner's multivolume Handbook of Philosophical Logic, the first volume of which was published in 1983 by Reidel. Recent research done on nonclassical logics in computer science and artificial intelligence is to be the subject of two further multivolume series, currently in preparation by Gabbay and others, called the Handbook of Logic in Computer Science and the Handbook of Logic in Artificial Intelligence and Logic Programming. These will be published over the next few years. Sophisticated automated reasoning systems based on classical logic have been in use for over 20 years. However, similar systems for nonclassical logics have only recently made an appearance. This special issue of the Journal of Automated Reasoning addresses the use of automated reasoning systems to solve problems in a ntmaber of nonclassical logics. Indeed, a common theme to all of the papers in this issue is that they discuss the implementations of various automated reasoning systems. Here we have in mind automated reasoning systems in possibly a broader sense than is usual. Automated reasoning systems tend to be equated with automated theorem-proving systems. At least two of the papers in this special issue, those of Slaney and Pritchard, are concerned with the automation of methods for finding non-theorems.

Raymond Turner. Logics for artificial intelligence. Ellis Horwood series in artificial intelligence. Ellis Horwood, Chichester 1984, also distributed by Halsted Press, New York, 121 pp.

Raymond Turner. Logics for artificial intelligence. Ellis Horwood series in artificial intelligence. Ellis Horwood, Chichester 1984, also distributed by Halsted Press, New York, 121 pp.

A review of temporal logics

AbstractA series of temporal reasoning tasks are identified which motivate the consideration and application of temporal logics in artificial intelligence. There follows a discussion of the broad issues involved in modelling time and constructing a temporal logic. The paper then presents a detailed review of the major approaches to temporal logics: first-order logic approaches, modal temporal logics and reified temporal logics. The review considers the most significant exemplars within the various approaches, including logics due to Russell, Hayes and McCarthy, Prior, McDermott, Allen, Kowalski and Sergot. The logics are compared and contrasted, particularly in their treatments of change and action, the roles they seek to fulfil and the underlying models of time on which they rest. The paper concludes with a brief consideration of the problem of granularity—a problem of considerable significance in temporal reasoning, which has yet to be satisfactorily treated in a temporal logic.

Some uses and limitations of fuzzy logic in artificial intelligence reasoning for reactor control

This paper describes some potential uses for Fuzzy Logic as well as its limitations based on experience designing a small prototype expert system that can be used in a computer laboratory to study a government research reactor. The expert system designed in this study diagnoses problems in the interface between the heat exchanger and the core. Engineers who had first-hand experience with the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory suggested logical relations incorporated in the knowledge base. The expert system has a production rule backward-chaining-based architecture, and the knowledge base incorporates four kinds of information. First, the structural relationship between causes and consequences are given by nuclear engineering experts. Second, numerical values for the initiating events can be taken from observed performance of the HFIR during normal conditions. Third, the causes of particular events are ordinally ranked by their expected chance of occuring based on a combination of knowledge about the reactor design and actual experiences with the reactor in operation. Fourth, Bellman-Zadeh Fuzzy Logic is introduced to maintain truth values for expert system parameter values that can be true with some degree of certainty.

Logics for Artificial Intelligence

Logics for Artificial Intelligence

Logics for Artificial Intelligence (Artificial Intelligence Series) by Raymond Turner Ellis Horwood, Chichester, 1984, 121 pp. (£16.50)

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