Knowledge acquisition mechanisms for a logical knowledge base including hypotheses

A hypothesis-based reasoning system handles a knowledge base including complete (fact) and incomplete (hypothesis) knowledge. The handling of the incomplete knowledge plays an important role in realizing advanced AI functions such as commonsense, flexible matching, learning, etc. From both theoretical and practical viewpoints, the hypothesis-based logical reasoning system can be considered to be an important start point towards a next-generation knowledge base architecture. This paper describes an enhanced knowledge representation of the hypothetical reasoning system designed particularly for interactive diagnosis problems. The mechanisms of two knowledge acquisition modules developed for this knowledge base including hypotheses are then described. The first module, which is based on an inductive inference mechanism for the knowledge including hypotheses, enables multiple-concept formation from given examples. The key technology of this mechanism is a minimum generalization extended to the knowledge including hypotheses. The second module provides knowledge assimilation and management functions for the frame knowledge base constructed on the hypothetical reasoning system. That is, this module enables the following functions: (1) the system can assimilate new knowledge while maintaining the consistency and nonredundancy of the knowledge base; (2) the system rearranges the knowledge base when existing knowlege is deleted; and (3) the system can control the inheritance link of the frame knowledge base in response to an input indicating that the property inheritance from an upper frame is denied as an exceptional case. The nonmonotonicity of the hypothetical reasoning system is considered in these mechanisms. Shapiro's logical debugging algorithm is employed effectively to identify