Issues in the Justification-Based Diagnosis of Planning Failures

Abstract

This chapter reviews the issues involved in the justification-based diagnosis of planning failures. A planner in the real world must constantly contend with uncertainty. Failure once in the run of plans as predicted by the planner is forgivable but a repetitive pattern of failures is intolerable. This argument constitutes the main motivation for a failure-driven approach to explanation-based learning in planning domains. A post hoc analysis of the failure(s), of which assumptions failed, and how and why they failed should be carried out automatically by the planner. To carry out such an analysis, the planner must first of all be able to determine the assumptions underlying its plan, and their role in the reasoning that led it to expect that the plan would perform as intended. The basic process of failure diagnosis, given such justification structures, is a simple recursive procedure. If an expectation fails, it is to be checked whether any of the antecedents of the rule that generated the expectation are contradicted by the facts of the case. If so, then the antecedent(s) that have been contradicted and recur have to be faulted. Otherwise, the problem lies in the assumptions associated with the rule. The chapter presents a test-bed system comprising mechanisms for inference and rule application, justification maintenance, expectation handlers, failure explanation, and rule patching.