Journal• ISSN: 0076-2032
Machine intelligence
About: Machine intelligence is an academic journal. The journal publishes majorly in the area(s): Computer science & Prolog. Over the lifetime, 67 publications have been published receiving 6638 citations.
Topics: Computer science, Prolog, Logic programming, Inductive logic programming, Inductive programming
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TL;DR: In this paper, the authors consider the problem of reasoning about whether a strategy will achieve a goal in a deterministic world and present a method to construct a sentence of first-order logic which will be true in all models of certain axioms if and only if a certain strategy can achieve a certain goal.
Abstract: A computer program capable of acting intelligently in the world must have a general representation of the world in terms of which its inputs are interpreted. Designing such a program requires commitments about what knowledge is and how it is obtained. Thus, some of the major traditional problems of philosophy arise in artificial intelligence. More specifically, we want a computer program that decides what to do by inferring in a formal language that a certain strategy will achieve its assigned goal. This requires formalizing concepts of causality, ability, and knowledge. Such formalisms are also considered in philosophical logic. The first part of the paper begins with a philosophical point of view that seems to arise naturally once we take seriously the idea of actually making an intelligent machine. We go on to the notions of metaphysically and epistemo-logically adequate representations of the world and then to an explanation of can, causes, and knows in terms of a representation of the world by a system of interacting automata. A proposed resolution of the problem of freewill in a deterministic universe and of counterfactual conditional sentences is presented. The second part is mainly concerned with formalisms within which it can be proved that a strategy will achieve a goal. Concepts of situation, fluent, future operator, action, strategy, result of a strategy and knowledge are formalized. A method is given of constructing a sentence of first-order logic which will be true in all models of certain axioms if and only if a certain strategy will achieve a certain goal. The formalism of this paper represents an advance over McCarthy (1963) and Green (1969) in that it permits proof of the correctness of strategies that contain loops and strategies that involve the acquisition of knowledge; and it is also somewhat more concise. The third part discusses open problems in extending the formalism of part 2. The fourth part is a review of work in philosophical logic in relation to problems of artificial intelligence and a discussion of previous efforts to program ‘general intelligence’ from the point of view of this paper.
3,920 citations
Journal Article•
TL;DR: This paper is a case study of how a linear arithmetic procedure was integrated into a heuristic theorem prover, and graphically illustrates the difference between a stand-alone decision procedure and one that is of use to a more powerful theoremProver.
Abstract: We discuss the problem of incorporating into a heuristic theorem prover a decision procedure for a fragment of the logic. An obvious goal when incorporating such a procedure is to reduce the search space explored by the heuristic component of the system, as would be achieved by eliminating from the system’s data base some explicitly stated axioms. For example, if a decision procedure for linear inequalities is added, one would hope to eliminate the explicit consideration of the transitivity axioms. However, the decision procedure must then be used in all the ways the eliminated axioms might have been. The difficulty of achieving this degree of integration is more dependent upon the complexity of the heuristic component than upon that of the decision procedure. The view of the decision procedure as a \"black box\" is frequently destroyed by the need pass large amounts of search strategic information back and forth between the two components. Finally, the efficiency of the decision procedure may be virtually irrelevant; the efficiency of the final system may depend most heavily on how easy it is to communicate between the two components. This paper is a case study of how we integrated a linear arithmetic procedure into a heuristic theorem prover. By linear arithmetic here we mean the decidable subset of number theory dealing with universally quantified formulas composed of the logical connectives, the identity relation, the Peano \"less than\" relation, the Peano addition and subtraction functions, Peano constants, and variables taking on natural values. We describe our system as it originally stood, and then describe chronologically the evolution of our linear arithmetic procedure and its interface to the heuristic theorem prover. We also provide a detailed description of our final linear arithmetic procedure and the use we make of it. This description graphically illustrates the difference between a stand-alone decision procedure and one that is of use to a more powerful theorem prover.
166 citations
Journal Article•
TL;DR: This paper addresses methods of specialising rst-order theories within the context of incremental learning systems and proposes the adoption of a specialisation scheme based on an existing non-monotonic logic formalism which overcomes the problems that arise with incrementallearning systems which employ classical logic.
Abstract: This paper addresses methods of specialising rst-order theories within the context of incremental learning systems. We demonstrate the shortcomings of existing rst-order incremental learning systems with regard to their specialisation mechanisms. We prove that these shortcomings are fundamental to the use of classical logic. In particular, minimal \\correct-ing\" specialisations are not always obtainable within this framework. We propose instead the adoption of a specialisation scheme based on an existing non-monotonic logic formalism. This approach overcomes the problems that arise with incremental learning systems which employ classical logic. As a side-eeect of the formal proofs developed for this paper we deene a function called \\deriv\" which turns out to be an improvement on an existing explanation-based-generalisation (EBG) algorithm. Prolog code and a description of the relationship between \\deriv\" and the previous EBG algorithm are described in an appendix.
80 citations
Journal Article•
TL;DR: An oil metering system for a rotary compressor of the sliding vane type in which a valve assembly continuously meters oil flow into the compressor during its operation and is effective to prohibit oil flow during its "off" cycle, thereby preventing reverse rotation of the compressor rotor previously caused by balancing or equalizing of the inlet and outlet pressures internally of the compressors.
Abstract: An oil metering system for a rotary compressor of the sliding vane type in which a valve assembly continuously meters oil flow into the compressor during its operation and is effective to prohibit oil flow into the compressor during its "off" cycle, thereby preventing reverse rotation of the compressor rotor previously caused by balancing or equalizing of the inlet and outlet pressures internally of the compressor.
79 citations
Journal Article•
48 citations
Related Journals (5)
Performance Metrics
| Year | Papers |
|---|---|
| 1996 | 15 |
| 1994 | 16 |
| 1991 | 14 |
| 1988 | 16 |
| 1987 | 1 |
| 1979 | 1 |