On K* Search for Top-k Planning

Junkyu Lee, Michael Katz, Shirin Sohrabi

Keywords: classical planning, heuristic search, top-k planning

TL;DR: We re-introduce K* for top-k planning to the planning community, improve its performance to make it competitive with the state of the art.

Abstract: Finding multiple high-quality plans is essential in many planning applications, and top-k planning asks for finding k best plans, naturally extending cost-optimal classical planning. Several attempts have been made to formulate top-k classical planning as k-shortest paths finding problem and apply K* search, which alternates 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 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, which improves the performance of K* on classical planning benchmark. 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 benchmark.