On K* Search for Top-K Planning


  • Junkyu Lee IBM Research
  • Michael Katz IBM
  • Shirin Sohrabi IBM




Search In Goal-directed Problem Solving


Finding multiple high-quality plans is essential in many planning applications, and top-k planning asks for finding the k best plans, naturally extending cost-optimal classical planning. Several attempts have been made to formulate top-k classical planning as a k-shortest paths finding problem and apply K* search, which alternates between A* and Eppstein's algorithm. However, earlier work had shortcomings, among which were failing to handle inconsistent heuristics and degraded performance in Eppstein's algorithm implementations. As a result, existing evaluation results severely underrate the performance of the K* based approach to top-k planning. In this paper, we present a new top-k planner based on a novel variant of K* search. We address the following three aspects. First, we show an alternative implementation of Eppstein's algorithm for classical planning, which resolves a major bottleneck in earlier attempts. Second, we present a new strategy for alternating A* and Eppstein's algorithm, that improves the performance of K* on the classical planning benchmarks. Last, we introduce a simple mitigation of the limitation of K* to tasks with a single goal state, allowing us to preserve heuristic informativeness in face of imposed task reformulation. Empirical evaluation results show that the proposed approach achieves the state-of-the-art performance on the classical planning benchmarks. The code is available at https://github.com/IBM/kstar.