Truth maintenance systems and their application for verifying Expert System Knowledge Bases
- 135 Downloads
· Truth maintenance as a data base management facility, which was in fact the original intention of the TMS.
· Truth maintenance as an inference facility, which provides a way to extend the role of the TMS in solving problems.
· Truth maintenance as a verification facility, which illustrates a new and promising application of TMSs in the area of expert systems design.
This paper is not intended to provide a complete survey on TMSs, rather it aims to present the basic ideas and functionality of TMS, and to show how different kinds of TMS can be used as a meta-environment for testing Expert System Knowledge Bases, represented as sets of production rules, for anomalies.
The paper is addressed to two groups of readers: those who are looking for an introductory survey on TMSs, and those who are interested in non-conventional techniques for Expert System Knowledge Base verification.
Key WordsKnowledge-based Systems Verification of Rule-based Systems Nonmonotonic Reasoning Belief Revision Expert Systems Design
Unable to display preview. Download preview PDF.
- Brewka, G. (1990). On minimal change: A critique of the architecture of non-monotonic TMS. Technical report, GMD, Bonn.Google Scholar
- Brown, A. and Shoham, Y. (1989). New results on semantical nonmonotonic reasoning. In Proc. Second Int. Workshop on Non-monotonic Reasoning, pp. 19–26. Springer-Verlag.Google Scholar
- Buchanan, B. G. and Shortliffe, E. H. (1984). Human engineering of medical expert systems. In Buchanan, B. G. and Shortliffe, E. H. (Eds.), Rule-Based Expert Systems: the MYCIN Experiments of the Stanford Heuristic Programming Project, Ch. 32, pp. 599–612. Addison-Wesley, Reading MA.Google Scholar
- deKleer, J. (1986a). An assumption-based TMS. Artificial Intelligence (Netherlands), 28(2), 127–162.Google Scholar
- deKleer, J. (1986b). Problem solving with the ATMS. Artificial Intelligence (Netherlands), 28(2), 197–224.Google Scholar
- de Kleer, J. and Forbus, K. (1990). Truth Maintenance systems. AAAI. Tutorial on TMS presented at AAAI'90.Google Scholar
- Doyle, J. (1979). A truth maintenance system. Artificial Intelligence (Netherlands), 12, 231–272.Google Scholar
- Elkan, C. (1990). A rational reconstruction of nonmonotonic truth maintenance systems. Artificial Intelligence (Netherlands), 43, 219–234.Google Scholar
- Genesereth, M. and Nilsson, N. (1987). Logical Foundations of Artificial Intelligence. Morgan Kaufmann Pub.Google Scholar
- Ginsberg, A. (1987). A new approach to checking knowledge bases for inconsistency and redundancy. In Proc. 3rd Annual Expert Systems in Government Conference, pp. 102–111.Google Scholar
- Ginsberg, A. (1988). Knowledge-base reduction: A new approach to checking knowledge bases for inconsistency and redundancy. In Proc. 7th National Conference on Artificial Intelligence (AAAI 88), Vol. 2, pp. 585–589.Google Scholar
- Ginsberg, A. and Williamson, K. (1989). Checking quasi-first-order-logic rule-based systems for inconsistency and redundancy. Technical Report 11354–891229–02TM, AT & T Bell Laboratories, Holmdel, NJ.Google Scholar
- Goodwin, J. (1987). A Theory and System for Non-monotonic Reasoning. Ph.D. Thesis, Linkoping University, Sweden.Google Scholar
- Junker, U. and Konolige, K. (1990). Computing the extensions of autoepistemic and default theories with a truth maintenance system. In Proc. 8th National Conference on Artificial Intelligence (AAAI '90), pp. 278–283. Morgan Kaufmann Pub.Google Scholar
- Martins, J. (1990). The truth, the whole truth, and nothing but the truth. AI Magazine, Special Issue: 7–25.Google Scholar
- Martins, J. and Shapiro, S. (1988). A model for belief revision. Artificial Intelligence (Netherlands), 35(1), 25–79.Google Scholar
- McAllester, D. (1980). An outlook on truth maintenance. Technical report, AI Laboratory, Massachusetts Institute of Technology.Google Scholar
- Nebel, B. (1989). A knowledge level analysis of belief revision. In Proc. First International Conference on Principles of Knowledge Representation and Reasoning, pp. 301–311. Morgan Kaufmann Pub.Google Scholar
- Nguyen, T. A., Perkins, W. A., Laffey, T. J., and Pecora, D. (1985). Checking an expert systems knowledge base for consistency and completeness. In Proc. 9th International Joint Conference on Artificial Intelligence (IJCAI 85), Vol. 1, pp. 375–378. AAAI.Google Scholar
- Petrie, C. (1987). Revised dependency-directed backtracking for default reasoning. In Proc. 6th National Conference on Artificial Intelligence (AAAII '87), pp. 167–172. Morgan Kaufmann Pub.Google Scholar
- Popchev, I., Zlatareva, N., and Mircheva, M. (1990). A truth maintenance theory: An alternative approach. In Proc. 9th European Conference on AI (ECAI '90), pp. 509–514. Pitman Pub.Google Scholar
- Rao, A. and Foo, N. (1989). Formal theories of belief revision. In Proc. First International Conference on Principles of Knowledge Representation and Reasoning, pp. 369–380. Morgan Kaufmann Pub.Google Scholar
- Reinfrank, M. (1989). Fundamentals and Logical Foundations of Truth Maintenance. Ph.D. Thesis, Linkoping University, Sweden.Google Scholar
- Reinfrank, M. and Dressler, O. (1988). Rules and justifications, a uniform approach to reason maintenance and nonmonotonic inference. In Proc. International Conference on Fifth Generation Computer Systems '88.Google Scholar
- Reinfrank, M., Dressler, O., and Brewka, G. (1989). On the relationship between truth maintenance and autoepistemic logic. In Proc. 11th International Joint Conference on Artificial Intelligence (IJCAI '89), pp. 1206–1212. Morgan Kaufmann Pub.Google Scholar
- Zlatareva, N. (1990). Considerations on representing and handling human common-sense knowledge. TASSO-Report 10, FG Intellektik, Technische Hochschule Darmstadt, Germany.Google Scholar
- Zlatareva, N. (1991a). Distributed verification: A new formal approach for verifying knowledge-based systems. In Proc. World Congress on Expert Systems, pp. 1021–1029. Pergamon Press.Google Scholar
- Zlatareva, N. (1991b). Truth maintenance and verification of expert system knowledge bases. Technical report, CENPARMI, Concordia University, Montreal, Canada.Google Scholar