Adapting Novelty to Classical Planning as Heuristic Search

Self Cite

In multi-agent planning, preserving the agents’ privacy has become an increasingly popular research topic. For preserving the agents’ privacy, agents jointly compute a plan that achieves mutual goals by keeping certain information private to the individual agents. Unfortunately, this can severely restrict the accuracy of the heuristic functions used while searching for solutions. It has been recently shown that, for centralized planning, the performance of goal oriented search can be improved by combining goal oriented search and width-based search. The combination of these techniques has been called best-first width search. In this paper, we investigate the usage of best-first width search in the context of (decentralised) multi-agent privacy-preserving planning, addressing the challenges related to the agents’ privacy and performance. In particular, we show that best-first width search is a very effective approach over several benchmark domains, even when the search is driven by heuristics that roughly estimate the distance from goal states, computed without using the private information of other agents. An experimental study analyses the effectiveness of our techniques and compares them with the state-of-the-art.

Section: Width-based Search For Ma Planningmentioning

confidence: 99%

Best-First Width Search for Multi Agent Privacy-Preserving Planning

Gerevini

Lipovetzky

Percassi

et al. 2021

Self Cite

“…More complex novelty measures have been devised that tie the novelty score to a heuristic value, and consider the novelty of a state only among other states with the same (Lipovetzky and Geffner 2017) or lower (Katz et al 2017) heuristic value. In best-first width search (BFWS), the function ordering the open list is a tie-breaking sequence of multiple evaluation functions, where the primary function is a novelty measure (Lipovetzky and Geffner 2017).…”

Section: Novelty Pruningmentioning

confidence: 99%

“…The simplest form prunes states that do not contain a fact (or a tuple of facts) that has not been contained in previously explored states. The novelty measure can also be tied to a heuristic (Lipovetzky and Geffner 2017;Katz et al 2017), preferring states that contain novel facts among states with the same heuristic value.…”

mentioning

confidence: 99%

A Novel Lookahead Strategy for Delete Relaxation Heuristics in Greedy Best-First Search

Fickert

2020

Best-first width search (BFWS) is a recent approach to satisficing planning that combines traditional heuristics with novelty measures to achieve a balance between exploration and effective search guidance (exploitation). One such novelty measure is based on counting the number of subgoals achieved on the path from a state in which a relaxed plan was computed. We introduce a new lookahead strategy for greedy best-first search based on this idea, where after each expansion, a bounded lookahead search is guided by relaxed subgoal counting. Furthermore, we combine this technique with partial delete relaxation heuristics to improve the subgoals. Using the hCFF heuristic with online-refinement of conjunctions, we obtain a planner that significantly outperforms the state of the art in satisficing planning on the IPC benchmarks.

“…Best-first width algorithms (BFWS) have been shown to achieve state-of-the-art performance by combining width and heuristic search (Lipovetzky and Geffner 2017;Katz et al 2017). BFWS(f ) with f = h, h 1 , .…”

Section: Best-first Width Searchmentioning

confidence: 99%

A Polynomial Planning Algorithm That Beats LAMA and FF

Lipovetzky

Geffner

2017

Self Cite

It has been shown recently that heuristic and width-based search can be combined to produce planning algorithms with a performance that goes beyond the state-of-the-art. Such algorithms are based on best-first width search (BFWS), a plain best-first search set with evaluations functions combined lexicographically to break ties, some of which express novelty based preferences. In BFWS(f5), for example, the evaluation function f5 weights nodes by a novelty measure, breaking ties by the number of non-achieved goals. BFWS(f5) is a best-first algorithm, and hence, it is complete but not polynomial, and its performance doesn’t match the state of the art. In this work we show, however, that incomplete versions of BFWS(f5) where nodes with novelty greater than k are pruned, are not only polynomial but have an empirical performance that is better than both BFWS(f5) and state-of-the-art planners. This is shown by considering all the international planning competition instances. This is the first time where polynomial algorithms with meaningful bounds are shown to achieve state-of-the-art performance in planning. Practical and theoretical implications of this empirical finding are briefly sketched.