Utilizing multiple queues in Greedy Best-First Search (GBFS) has been proven to be a very effective approach to satisficing planning. Successful techniques include extra queues based on Helpful Actions (or Preferred Operators), as well as using Multiple Heuristics. One weakness of all standard GBFS algorithms is their lack of exploration. All queues used in these methods work as priority queues sorted by heuristic values. Therefore, misleading heuristics, especially early in the search process, can cause the search to become ineffective. Type systems, as introduced for heuristic search by Lelis et al, are a development of ideas for exploration related to the classic stratified sampling approach. The current work introduces a search algorithm that utilizes type systems in a new way – for exploration within a GBFS multiqueue framework in satisficing planning. A careful case study shows the benefits of such exploration for overcoming deficiencies of the heuristic. The proposed new baseline algorithm Type-GBFS solves almost 200 more problems than baseline GBFS over all International Planning Competition problems. Type-LAMA, a new planner which integrates Type-GBFS into LAMA-2011, solves 36.8 more problems than LAMA-2011.

Greedy best-first search (GBFS) is a popular and effective algorithm in satisficing planning and is incorporated into high-performance planners. GBFS in planning decides its search direction with automatically generated heuristic functions. However, if the heuristic functions evaluate nodes inaccurately, GBFS may be misled into a valueless search direction, thus resulting in performance degradation. This paper presents a simple but effective algorithm considering a diversity of search directions to avoid the errors of heuristic information. Experimental results in solving a variety of planning problems show that our approach is successful.