Exploration among and within Plateaus in Greedy Best-First Search

Recent enhancements to greedy best-first search (GBFS) such as DBFS, -GBFS, Type-GBFS improve performance by occasionally adopting a non-greedy node expansion policy, resulting in more exploratory behavior. However, previous exploratory mechanisms do not address exploration within the space sharing the same heuristic estimate (plateau). In this paper, we show these two modes of exploration, which work across (inter-) and within (intra-) plateau, are complementary, and can be combined to yield superior performance. We also introduce IP-diversification, a method combining Minimum Spanning Tree and randomization, which addresses “breadth”-bias instead of the “depth”-bias addressed by the existing diversification methods. We evaluate IP-diversification for both intraand inter-plateau exploration, and show that it significantly improves performance in several domains. Finally, we show that combining diversification methods results in a planner which is competitive to the state-of-the-art for satisficing planning.