Computing the Scope of Applicability for Acquired Task Knowledge in Experience-Based Planning Domains
This addresses the challenge of efficiently reusing acquired knowledge in AI planning systems, though it appears incremental as it extends prior work on TVLA.
The paper tackled the problem of determining when learned task knowledge (activity schemata) can be applied in experience-based planning by using Three-Valued Logic Analysis to generate conditions for scope of applicability, resulting in an automated method validated in two classical planning domains.
Experience-based planning domains have been proposed to improve problem solving by learning from experience. They rely on acquiring and using task knowledge, i.e., activity schemata, for generating solutions to problem instances in a class of tasks. Using Three-Valued Logic Analysis (TVLA), we extend previous work to generate a set of conditions that determine the scope of applicability of an activity schema. The inferred scope is a bounded representation of a set of problems of potentially unbounded size, in the form of a 3-valued logical structure, which is used to automatically find an applicable activity schema for solving task problems. We validate this work in two classical planning domains.