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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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