Consolidating LAMA with Best-First Width Search

TL;DR

This paper explores combining the LAMA planner with best-first width search (BFWS) techniques, demonstrating that partial integration improves planning performance and sets new benchmarks in agile planning.

Contribution

It introduces a novel hybrid approach that combines elements of LAMA and BFWS, leading to a new state-of-the-art in agile planning performance.

Findings

Simple addition of BFWS open-list to LAMA degrades performance.

Partial combination of LAMA and BFWS components improves planning results.

The hybrid approach outperforms existing planners on benchmark tests.

Abstract

One key decision for heuristic search algorithms is how to balance exploration and exploitation. In classical planning, novelty search has come out as the most successful approach in this respect. The idea is to favor states that contain previously unseen facts when searching for a plan. This is done by maintaining a record of the tuples of facts observed in previous states. Then the novelty of a state is the size of the smallest previously unseen tuple. The most successful version of novelty search is best-first width search (BFWS), which combines novelty measures with heuristic estimates. An orthogonal approach to balance exploration-exploitation is to use several open-lists. These open-lists are ordered using different heuristic estimates, which diversify the information used in the search. The search algorithm then alternates between these open-lists, trying to exploit these different estimates. This is the approach used by LAMA, a classical planner that, a decade after its release, is still considered state-of-the-art in agile planning. In this paper, we study how to combine LAMA and BFWS. We show that simply adding the strongest open-list used in BFWS to LAMA harms performance. However, we show that combining only parts of each planner leads to a new state-of-the-art agile planner.