Ariel Felner

Senior Academic

Variants of independence detection in SAT-based optimal multi-agent path finding

Pavel Surynek, Jiří Švancara, Ariel Felner, Eli Boyarski

In multi-agent path finding (MAPF) on graphs, the task is to find paths for distinguishable agents so that each agent reaches its unique goal vertex from the given start while collisions between agents are forbidden. A cumulative objective function is often minimized in MAPF. The main contribution of this paper consists in integrating independence detection technique (ID) into a compilation-based MAPF solver that translates MAPF instances into propositional satisfiability (SAT). The independence detection technique in search-based solvers tries to decompose a given MAPF instance into instances consisting of small groups of agents with no interaction across groups. After the decomposition phase, small instances are solved independently and the solution of the original instance is combined from individual solutions to small instances. The presented experimental evaluation indicates significant reduction of the size of instances translated to the target SAT formalism and positive impact on the overall performance of the solver.

Publication language English
Pages 116-136
Publication status Published - 01.01.2018

Keywords

Cost optimality
Independence detection (ID)
Makespan optimality
Multi-agent path-finding (MAPF)
Path-finding on grids
Propositional satisfiability (SAT)
SAT encodings
Sum-of-costs optimality

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science
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Link to publication in Scopus