Failure driven dynamic search control for partial order planners: An explanation based approach

Given the intractability of domain independent planning, the ability to control the search of a planner is vitally important. One way of doing this involves learning from search failures. This paper describes SNLP + EBL, the first implementation of an explanation based search control rule learning framework for a partial order (plan-space) planner. We will start by describing the basic learning framework of SNLP + EBL. We will then concentrate on SNLP + EBL's ability to learn from failures, and describe the results of empirical studies which demonstrate the effectiveness of the search control rules SNLP + EBL learns using our method. We then demonstrate the generality of our learning methodology by extending it to UCPOP (Penberthy and Weld, 1992), a descendant of SNLP that allows for more expressive domain theories. The resulting system, UCPOP + EBL, is used to analyze and understand the factors influencing the effectiveness of EBL. Specifically, we analyze the effect of (i) expressive action representations, (ii) domain specific failure theories and (iii) sophisticated backtracking strategies on the utility of EBL. Through empirical studies, we demonstrate that expressive action representations allow for more explicit domain representations which in turn increase the ability of EBL to learn from analytical failures, and obviate the need for domain specific failure theories. We also explore the strong affinity between dependency directed backtracking and EBL in planning.