Open AccessProceedings Article
Relaxation heuristics for multiagent planning
Michal Štolba,Antonín Komenda +1 more
- pp 298-306
TLDR
This paper presents a unified view on distribution of delete relaxation heuristics for multiagent planning, and thoroughly experimentally evaluates properties of the distribution of additive, max and Fast-Forward relaxationHeuristics in a planner based on distributed Best-First Search.Abstract:
Similarly to classical planning, in MA-STRIPS multiagent planning, heuristics significantly improve efficiency of search-based planners. Heuristics based on solving a relaxation of the original planning problem are intensively studied and well understood. In particular, frequently used is the delete relaxation, where all delete effects of actions are omitted. In this paper, we present a unified view on distribution of delete relaxation heuristics for multiagent planning.
Until recently, the most common approach to adaptation of heuristics for multiagent planning was to compute the heuristic estimate using only a projection of the problem for a single agent. In this paper, we place such approach in the context of techniques which allow sharing more information among the agents and thus improve the heuristic estimates. We thoroughly experimentally evaluate properties of our distribution of additive, max and Fast-Forward relaxation heuristics in a planner based on distributed Best-First Search. The best performing distributed relaxation heuristics favorably compares to a state-of-the-art MA-STRIPS planner in terms of benchmark problem coverage. Finally, we analyze impact of limited agent interactions by means of recursion depth of the heuristic estimates.read more
Citations
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Cooperative Multi-Agent Planning: A Survey
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Cooperative Multi-Agent Planning: A Survey
TL;DR: The most relevant approaches to cooperative multi-agent planning (MAP) are reviewed in this paper, with a focus on the solvers that took part in the 2015 Competition of Distributed and Multi-Agent Planning.
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Collaborative privacy preserving multi-agent planning
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Privacy-concerned multiagent planning
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References
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The FF planning system: fast plan generation through heuristic search
Jörg Hoffmann,Bernhard Nebel +1 more
TL;DR: A novel search strategy is introduced that combines hill-climbing with systematic search, and it is shown how other powerful heuristic information can be extracted and used to prune the search space.
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Proceedings Article
Landmarks, critical paths and abstractions: what's the difference anyway?
Malte Helmert,Carmel Domshlak +1 more
TL;DR: A new admissible heuristic called the landmark cut heuristic is introduced, which compares favourably with the state of the art in terms of heuristic accuracy and overall performance.
Book ChapterDOI
Planning as Heuristic Search: New Results
Blai Bonet,Hector Geffner +1 more
TL;DR: Hspr is a heuristic search planner that searches backward from the goal rather than forward from the initial state, which allows hspr to compute the heuristic estimates only once, and can produce better plans, often in less time.
Proceedings Article
Flexible abstraction heuristics for optimal sequential planning
TL;DR: An approach to deriving consistent heuristics for automated planning, based on explicit search in abstract state spaces, which subsumes planning with pattern databases as a special case and shows that the approach is competitive with the state of the art.
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