Published online by Cambridge University Press: 31 January 2023
We describe a general problem solving mechanism that is especially suited for performing a particular form of abductive inference, or best-explanation finding. A problem solver embodying this mechanism synthesizes composite hypotheses. It does so by by combining hypothesis parts as a means to the satisfaction of explanatory goals. In this way it is able to arrive at complex, integrated conclusions which are not pre-stored.
The intent is to present a computationally-feasible, task-specific problem solver for a particular information processing task which is nevertheless of very great generality. The task is that of synthesizing coherent composite explanatory hypotheses based upon a prestored, and possibly vast collection of hypothesis-generating “concepts”. The authors’ claim is nothing less than to have shown, in a new sense, and surpassing all other work in this area, how it is computationally possible for an agent to come to “know”, based upon the evidence of the case.
This work has been supported in various stages by NSF Grant MCS-8305032, and NIH Grant R01 LM 04298 from the National Library of Medicine. Dr. Jack W. Smith, Jr. is supported by NLM Career Development Award K04 LM00083.
Praise is due Tom Bylander for showing that the task of producing a consistent composite is NP complete. Thanks are also due to Tom Bylander for his helpful comments on a previous draft, to Bill Punch and Dean Allemang for their discussions and encouragement of the approach to abduction, and to Jon Sticklen for arguing until things were better justified. Also thanks to the members of the recent graduate seminar at Ohio State on diagnostic reasoning for their helpful comments, and to two anonymous reviewers of an earlier draft, who, by their failure to understand what was being presented, pointed the way to an improved explication.