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Proof planning: A methodology for developing AI systems incorporating design issues

Published online by Cambridge University Press:  27 February 2009

Helen Lowe
Helen Lowe
Affiliation:
Department of Computer Studies, Napier University, Craiglockhart Campus, Edinburgh EH 14 1DJ, United Kingdom
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Abstract

In cases where a domain theory can be successfully expressed in a logical formalism and can be used to formulate a task in that domain in mathematical terms, the task of building sound knowledge-based systems is greatly facilitated. However, it is not immediately obvious how the design aspects of such tasks, where these are an important feature of problem solving, can be incorporated in this approach. Design issues differ from search problems in that there may be several choices, each valid in some sense, but not (necessarily) equally good or equally appropriate in the current context. A case study is described in which a methodology is used based on the development of proof plans. The ability to conduct research according to the Popperian framework of hypothesis, validation, testing, and modification in response to empirical evidence – the hypothetico-deductive approach – seems essential to any rigorous scientific endeavor. It is believed that proof planning is a method which readily exploits this inherently incrementalist approach and could prove to be a powerful tool in designing AI systems.

Keywords

Proof PlanningConfigurationIntelligent Knowledge-based SystemsMethodology
Type
Articles
Information
AI EDAM , Volume 8 , Issue 4 , Fall 1994 , pp. 307 - 317
Copyright
Copyright © Cambridge University Press 1994

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References

REFERENCES

Alden, J. & Morgan, R. (1974). Regional Planning: A Comprehensive View. Leonard Hill Books, Leighton Buzzard.Google Scholar
Bachant, J. & Soloway, E. (1989). The engineering of XCON. Communications of the ACM 32(3), 311–317.Google Scholar
Barker, V. E. & O’Conner, D. E. (1989). Expert system for configuration at digital: XCON and beyond. Communications of the ACM 32(3), 298–310.CrossRefGoogle Scholar
Bates, J-L. & Constable, R. L. (1985). Proofs as programs. ACM Transactions on Programming Languages and System 7(1), 113–36.CrossRefGoogle Scholar
Bundy, A. (1987). AI bridges and dreams. Research Paper 300 Dept. of Artificial Intelligence, Edinburgh; also in AI and Society 1(1), 62–71.CrossRefGoogle Scholar
Bundy, A. (1988). The use of explicit plans to guide inductive proofs. In 9th Conf. Automated Deduction, 111–120. (Longer version available as Research Paper 349, Dept. of Artificial Intelligence, Edinburgh.)CrossRefGoogle Scholar
Bundy, A. (1990). Catalogue of Artificial Intelligence Techniques. Springer-Verlag, Berlin.CrossRefGoogle Scholar
Bundy, A. (1991). A Science of reasoning. In Computational Logic: Essays in Honor of Alan Robinson, (Lassez, J-L. and Plotkin, G., Eds.), MIT Press, Cambridge, Massachusetts. (Also available as Research Paper 445, Dept. of Artificial Intelligence, Edinburgh.)Google Scholar
Bundy, A.van Harmelen, F., Hesketh, J., Smaill, A., & Stevens, A. (1989). A rational reconstruction and extension of recursion analysis. In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, (Sridharan, N. S., Ed.), pp. 359–365. Morgan Kaufman, Los Altos, CA. (Available as Research Paper 419, Dept. of Artificial Intelligence, Edinburgh.)Google Scholar
Bundy, A.Stevens, A., van Harmelen, F.Ireland, A. & Smaill, A. (1991 a). Rippling: A heuristic for guiding inductive proofs. (Available as Research Paper 567, Dept. of Artificial Intelligence, Edinburgh). Artificial Intelligence, 26, 185–253.Google Scholar
Bundy, A., van Harmelen, F., Hesketh, J., & Smaill, A. (1991 b). Experiments with proof plans for inductions. J. Automat. Reasoning 7, 303–324. (Earlier version available as Research Paper 413, Dept. of Artificial Intelligence, Edinburgh.)CrossRefGoogle Scholar
De Millo, R. A., Lipton, R. J., & Perlis, A. J. (1979). Social Processes and proofs of theorems and programs. Communications of the ACM 22, 271–280.CrossRefGoogle Scholar
Frayman, F., & Mittal, S. (1987). Cossack: A constraints-based expert system for configuration tasks. In Proceedings of the 2nd International Conference on Application of AI to Engineering, Boston, Massachusetts.Google Scholar
Ireland, A. (1992). The use of planning critics in mechanizing inductive proofs. In International Conference of Logic Programming and Automated Reasoning – LPAR 92, St. Petersburg, (Voronkov, A. Ed.), Lecture Notes in Artificial Intelligence No. 624, pp. 178–189. Springer-Verlag, Berlin.CrossRefGoogle Scholar
Kuhn, T. (1970). The Structure of Scientific Revolutions, 2nd ed. University of Chicago Press, Chicago.Google Scholar
Lowe, H. (1991). The use of theorem proving techniques in expert systems for configuration. In Proceedings of the Eleventh International Workshop on Expert System and their Applications, Avignon, (Rault, J.-C., Ed.), pp. 547–557. EC2, Paris. (Also available from Edinburgh as Research Paper 536.)Google Scholar
Lowe, H. (1993 a). The Application of Proof Plans to Computer Configuration Problems. Ph. D. Thesis, University of Edinburgh.Google Scholar
Lowe, H. (1993 b). The CLEM configuration system, user manual and programmer manual. Technical Paper, 20, Dept. of Artificial Intelligence, Edinburgh.Google Scholar
Pierick, J. (1986). A knowledge representation system for systems dealing with hardware configuration In Proceedings of the Fifth National Conf on Artificial Intelligence, (Kehler, T., & Rosenschein, s., Eds.), pp. 991–995. AAAI, Philadelphia.Google Scholar
Popper, K. R. (1961). The Poverty of Historicism. Routledge & Kegan Paul, London (revised version with some corrections).Google Scholar
Popper, K. R. (1989). Conjectures and Refutations. Routledge & Kegan Paul, LondonGoogle Scholar
Popper, K. R. (1990). The Logic of Scientific Discovery. Unwin Hyman, LondonGoogle Scholar
Ritchie, G.D., & Hanna, F.K. (1984). AM: A case study in AI methodology. Artificial Intelligence 23 249–268.CrossRefGoogle Scholar
Searls, D. B. & Norton, L. M. (1990). Logic-based configuration with a semantic network. J. Logic Programming I, 53–73.CrossRefGoogle Scholar
Soloway, E., Bachant, J. & Jensen, K. (June 1987). Assessing the maintainability of XCON-in-RIME: coping with the problems of a very large rule-base. In 6th National Conference on Artificial Intelligence, Philadelphia, sponsored by the American Association for Artificial Intelligence.Google Scholar