Admissible Heuristics for Automated Planning

The problem of domain-independent automated planning has been a topic of research in Artificial Intelligence since the very beginnings of the field. Due to the desire not to rely on vast quantities of problem specific knowledge, the most widely adopted approach to automated planning is search. The topic of this thesis is the development of methods for achieving effective search control for domain-independent optimal planning through the construction of admissible heuristics. The particular planning problem considered is the so called “classical” AI planning problem, which makes several restricting assumptions. Optimality with respect to two measures of plan cost are considered: in planning with additive cost, the cost of a plan is the sum of the costs of the actions that make up the plan, which are assumed independent, while in planning with time, the cost of a plan is the total execution time – makespan – of the plan. The makespan optimization objective can not, in general, be formulated as a sum of independent action costs and therefore necessitates a problem model slightly different from the classical one. A further small extension to the classical model is made with the introduction of two forms of capacitated resources. Heuristics are developed mainly for regression planning, but based on principles general enough that heuristics for other planning search spaces can be derived on the same basis. The thesis describes a collection of methods, including the h, additive h and improved pattern database heuristics, and the relaxed search and boosting techniques for improving heuristics through limited search, and presents two extended experimental analyses of the developed methods, one comparing heuristics for planning with additive cost and the other concerning the relaxed search technique in the context of planning with time. Experiments aim at discovering the characteristics of problem domains that determine the relative effectiveness of the compared methods; results indicate that some plausible such characteristics have been found, but are not entirely conclusive. Parts of the material in this thesis has previously appeared in the following papers: Haslum, P., and Geffner, H. 2000. Admissible heuristics for optimal planning. In Proc. 5th International Conference on Artificial Intelligence Planning and Scheduling (AIPS’00), 140 – 149. AAAI Press. Haslum, P., and Geffner, H. 2001. Heuristic planning with time and resources. In Proc. 6th European Conference on Planning (ECP’01), 121 – 132. Haslum, P. 2004. Improving heuristics through search. In Proc. European Conference on AI (ECAI’04), 1031 – 1032. Haslum, P.; Bonet, B.; and Geffner, H. 2005. New admissible heuristics for domainindependent planning. In Proc. 20th National Conference on AI (AAAI’05), 1163 – 1168. Haslum, P. 2006. Improving heuristics through relaxed search – an analysis of TP4 and hspa in the 2004 planning competition. Journal of AI Research 25.