Towards Optimal Cooperative Path Planning in Hard Setups through Satisfiability Solving

Surynek, Pavel

doi:10.1007/978-3-642-32695-0_50

Cited by 82 publications

(53 citation statements)

References 6 publications

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“…The technique has been suggested in Ref. 12. To be able to use SAT solvers in this way we need to obtain some (sub-optimal) solution to the PMG instance first.…”

Section: Sat Based Solution Improvements: An Overviewsupporting

confidence: 39%

Redundancy Elimination in Highly Parallel Solutions of Motion Coordination Problems

Surynek

2013

Int. J. Artif. Intell. Tools

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Problems of motion coordination of multiple entities are addressed in this paper. These problems are dealt on the abstract level where they can be viewed as tasks of constructing a spatial-temporal plan for a set of identical mobile entities. The entities reside in a certain environment where they can move. Each entity need to reach a given goal position supposed it is starting from some initial position. The most abstract formal representations of coordinated motion problems are known as "pebble motion on a graph" and "multi-robot path planning". The existent algorithms for pebble motion and multi-robot problems were suspected of generating solutions containing redundancies. This hypothesis eventually confirmed in this work. We present several techniques for identifying and eliminating redundancies from solutions generated by existent algorithms. An extensive experimental evaluation was performed and it showed that the quality of generated solutions can be improved up to the order of magnitude. We also identify parameters characterizing instances of problems where a significant improvement is expectable.

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“…The technique has been suggested in Ref. 12. To be able to use SAT solvers in this way we need to obtain some (sub-optimal) solution to the PMG instance first.…”

Section: Sat Based Solution Improvements: An Overviewsupporting

confidence: 39%

Redundancy Elimination in Highly Parallel Solutions of Motion Coordination Problems

Surynek

2013

Int. J. Artif. Intell. Tools

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“…In these cases the pattern of improvement is fixed which may cause that redundancies of a priori unknown pattern remain in the solution. Applying SAT-based mechanisms to improve solutions of CPF problems, where sub-solutions are replaced by makespan-optimal ones, has been proposed in [14]. This approach is trying to overcome the limitation imposed by the fixed set of improvement patterns.…”

Section: Introductionsupporting

confidence: 41%

Shortening Plans by Local Re-planning

Balyo

Barták

et al. 2012

2012 IEEE 24th International Conference on Tools With Artificial Intelligence

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There exist planning algorithms that can quickly find sub-optimal plans even for large problems and planning algorithms finding optimal plans but only for smaller problems. In this paper we attempt to integrate both approaches. We present an anytime technique for improving plan quality, in particular for decreasing the plan makespan, via substituting parts of the plan by makespan-optimal sub-plans. The technique guarantees optimality, though it is primarily intended to quickly improve plan quality. We experimentally compare various approaches to local improvements and we show that our method has significantly better makespan score than the SASE planner, which is one of the best optimal planners.

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“…(2) Reduction-based solvers. By contrast, many recent optimal solvers reduce MAPF to known problems such as CSP [20], SAT [21], Inductive Logic Programming [22] and Answer Set Programming [23].…”

Section: Related Workmentioning

confidence: 99%

Finding Optimal Solutions to Token Swapping by Conflict-Based Search and Reduction to SAT

Surynek

2018

2018 IEEE 30th International Conference on Tools With Artificial Intelligence (ICTAI)

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We study practical approaches to solving the token swapping (TSWAP) problem optimally in this short paper. In TSWAP, we are given an undirected graph with colored vertices. A colored token is placed in each vertex. A pair of tokens can be swapped between adjacent vertices. The goal is to perform a sequence of swaps so that token and vertex colors agree across the graph. The minimum number of swaps is required in the optimization variant of the problem. We observed similarities between the TSWAP problem and multi-agent path finding (MAPF) where instead of tokens we have multiple agents that need to be moved from their current vertices to given unique target vertices. The difference between both problems consists in local conditions that state transitions (swaps/moves) must satisfy. We developed two algorithms for solving TSWAP optimally by adapting two different approaches to MAPF -CBS and MDD-SAT. This constitutes the first attempt to design optimal solving algorithms for TSWAP. Experimental evaluation on various types of graphs shows that the reduction to SAT scales better than CBS in optimal TSWAP solving.

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Towards Optimal Cooperative Path Planning in Hard Setups through Satisfiability Solving

Cited by 82 publications

References 6 publications

Redundancy Elimination in Highly Parallel Solutions of Motion Coordination Problems

Redundancy Elimination in Highly Parallel Solutions of Motion Coordination Problems

Shortening Plans by Local Re-planning

Finding Optimal Solutions to Token Swapping by Conflict-Based Search and Reduction to SAT

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