Tailoring Pattern Databases for Unsolvable Planning Instances

Abstract

There has been an astounding improvement in domain-independent planning for solvable instances over the last decades and planners have become increasingly efficient at constructing plans. However, this advancement has not been matched by a similar improvement for identifying unsolvable instances. In this paper, we specialise pattern databases for dead-end detection and, thus, for detecting unsolvable instances. We propose two methods of constructing pattern collections and show that spending any more time constructing the pattern collection is likely to be unproductive. In other words, very few other pattern collections within the given space bounds are able to detect more dead-ends. We show this by carrying out a novel statistical analysis: a large computer cluster has been used to approximate the limit of pattern collections with respect to dead-end detection for many unsolvable instances, and this information is used in the analysis of the proposed methods. Consequently, further improvement must come from combining pattern databases with other techniques, such as mutexes. Furthermore, we explain why one of the proposed methods tends to find significantly more unsolvable variable projections, which is desirable since they imply that the instance is unsolvable. Finally, we compare the best proposed method with the winner and the runner up of the first unsolvability international planning competition, and show that the method is competitive.