Learning in order to avoid search in logic programming

This paper discusses learning in the context of a diagnostic expert system. The diagnostic expert system is an example of a generate-and-test problem solver. Fault diagnosis heuristics (i.e. logical implications representing association between unusual features and failed components) hypothesize potential faults. The potential faults are verified or denied by comparing the predictions of a qualitative simulation to observe data. Learning in this context consists of modifying the fault diagnosis heuristics. This paper describes how heuristic rules and device models can be represented and revised in a logic programming framework. In addition, we demonstrate how logic programming can be extended to perform abductive reasoning in addition to deductive reasoning. Finally, we compare failure-driven learning and learning from successes for acquiring fault diagnosis heuristics via explanation-based learning.