Chapter 10. State Space Pruning
This chapter discusses learning approaches to prune the successor set(s). It studies the exclusion of forbidden states or move sequences, and localizing the search using the notion of relevance. The chapter distinguishes between on-the-fly and offline learning.
Keywords: admissible pruning, nonadmissible pruning, substring pruning, pruning dead-ends, penalty tables, symmetry reduction, macro problem solving, relevance cut, partial order reduction
One of the most effective approaches to tackle large problem spaces is to prune (i.e., cut off branches from) the search tree. There are multiple reasons for pruning. Some branches might not lead to a goal state, others lead to inferior solutions; some result in positions ...