AI and Cognitive Science ’91 pp 250-263 | Cite as
Generalized Knowledge Representation using Free Logic
Abstract
Declarative rule-based systems can be generalized to constraint-based systems. However, although conventional constraint systems require that the set of parameters which exist in a problem be known ab initio,there are some applications in which the existence of certain parameters is dependent on conditions whose truth or falsity can only be determined dynamically. In this paper, we show how this conditional existence of parameters can be handled in a mathematically well-founded fashion by viewing a constraint network as a set of sentences in free logic. Based on these ideas, we have developed, implemented and applied to a range of applications, a constraint language in which any sentence in full first-order free logic, about a many-sorted universe of discourse which subsumes the real numbers, is a well-formed constraint.
Keywords
Classical Logic Constraint Satisfaction Problem Predicate Symbol Possibility Distribution Constraint NetworkPreview
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References
- [1]Bowen J, and Bahler D, 1990, “Improving Ontological Expressiveness in Constraint Processing,” Technical Report, Department of Computer Science, North Carolina State University.Google Scholar
- [2]Bowen J, and Bahler D, 1991, “Free Logic in Constraint Processing,” Technical Report, Department of Computer Science, North Carolina State University. Submitted to Artificial Intelligence journal.Google Scholar
- [3]Bowen J, and Bahler D, 1991, “Conditional Existence of Variables in Generalized Constraint Networks,” in Proceedings of AAA I-91, the National Conference of the American Association for Artificial Intelligence, Anaheim CA, July 1991.Google Scholar
- [4]Bowen J, O’Grady P and Smith L, 1990, “A Constraint Programming Language for Life-Cycle Engineering,” International Journal for Artificial Intelligence in Engineering, 5 (4), 206–220.CrossRefGoogle Scholar
- [5]Bowen J, and Bahler D, 1992, “Compound Constraint Propagation,” Technical Report, Department of Computer Science, North Carolina State University.Google Scholar
- [6]Bowen J, Bahler D, and Dholakia A, 1990, “A DFT Advisor for Digital Circuit Design,” Technical Report, Department of Computer Science, North Carolina State University. To appear in Computers and Electrical Engineering, special issue on Artificial Intelligence in Engineering Design and Manufacturing.Google Scholar
- [7]Friedman G and Leondes C, 1969, “Constraint Theory, Part I: Fundamentals,” IEEE Transactions on Systems Science and Cybernetics, ssc-5, 1, 48–56.Google Scholar
- [8]Hirst G, 1989, “Ontological assumptions in knowledge representation,” Proceedings of the First International Conference on Principles of Knowledge Representation and Reasoning, 157–169.Google Scholar
- [9]Lambert K and van Fraassen B, 1972, Derivation and Counterexample: An Introduction to Philosophical Logic, Enrico, CA: Dickenson Publishing Company.Google Scholar
- [10]Mackworth A, 1987. “Constraint Satisfaction,” in S. Shapiro (ed.), The Encyclopedia of Artificial Intelligence, New York: Wiley, 205–211.Google Scholar
- [11]Mittal S and Falkenhainer B, 1990, “Dynamic Constraint Satisfaction Problems,” Proceedings of the Eighth National Conference on Artificial Intelligence, 25–32.Google Scholar
- [12]Mulder J, Mackworth A and Havens W, 1988, “Knowledge Structuring and Constraint Satisfaction: The Mapsee Approach,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 10 (6), 866–879.CrossRefGoogle Scholar
- [13]Nilsson N, 1991, “Logic and Artificial Intelligence,” Artificial Intelligence, 47, 31–56.MathSciNetCrossRefGoogle Scholar