Explicit Control of Reasoning

The construction of expert problem-solving systems requires the development of techniques for using modular representations of knowledge without encountering combinatorial explosions in the solution effort. This report describes an approach to dealing with this problem based on making some knowledge which is usually implicitly part of an expert problem solver explicit, thus allowing this knowledge about control to be manipulated and reasoned about. The basic components of this approach involve using explicit representations of the control structure of the problem solver, and linking this and other knowledge manipulated by the expert by means of explicit data dependencies. ° Fannie and John Hertz Foundation Fellow °°NSF Fellow This research was conducted at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the Laboratory’s artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research contract number N00014-75-C-0643. de Kleer, Doyle, Steele & Sussman 2 Explicit Control of Reasoning Acknowledgements: We thank Charles Rich, Marvin Minsky, Drew McDermott, Richard Stallman, Marilyn McLennan, Richard Brown, Peter Szolovits, and Gerald Roylance for suggestions, ideas and comments used in this paper. Jon Doyle is supported by a Fannie and John Hertz Foundation graduate fellowship. Guy Steele is supported by a National Science Foundation graduate fellowship. This research was conducted at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the Laboratory’s artificial Intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research contract number N00014-75-C-0643. This paper will be presented at the ACM Conference on Artificial Intel— ligence and Programming Languages, Rochester, New York, August 1977.