Exploiting Domain Knowledge with a Concurrent Hierarchical Planner

Based on recent research about coordinating concurrent hierarchical plans (CHiPs), we introduce a sound and complete hierarchical planner that can better reason about precomputed conditions (summary information) of abstract plans to potentially make better re nement decisions than previous approaches. A reasonable criticism of this technique is that the summary information can grow exponentially as it is propagated up a plan hierarchy. This paper analyzes the complexity of the problem to show that in spite of this exponential growth, nding solutions at higher levels is still exponentially cheaper than at lower levels. In addition, this paper o ers heuristics, including \fewest threats rst" (FTF) and \expand most threats rst" (EMTF), that take advantage of summary information to smartly direct the search for a global plan. Experiments show that for a particular domain these heuristics could greatly improve the search for the optimal global plan compared to two other heuristics (FAF and ExCon) that have both been successful in Hierarchical Task Network (HTN) planning.