Admissible Heuristics for Automated Planning
2006 (English)Doctoral thesis, monograph (Other academic)
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 hm, additive hm 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, aimed 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.
Place, publisher, year, edition, pages
Institutionen för datavetenskap, 2006. , 164 p.
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1004
AI planning, optimal planning, temporal planning, heuristic search
National CategoryComputer Science
IdentifiersURN: urn:nbn:se:liu:diva-6042ISBN: 91-85497-28-2OAI: oai:DiVA.org:liu-6042DiVA: diva2:21613
2006-03-15, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 13:15 (English)
Long, Derek, Dr.