Date of Award

1-1-2017

Document Type

Thesis

Degree Name

M.S.

Department

Computer Science

First Advisor

Nathan Sturtevant

Keywords

Heuristic, Heuristic Search, MAPF, Multi-Agent Pathfinding, Pathfinding, Path Planning

Abstract

Multi-Agent Pathfinding is an NP-Complete search problem with a branching factor that is exponential in the number of agents. Because of this exponential feature, it can be difficult to solve optimally using traditional search techniques, even for relatively small problems. Many recent optimal solvers have attempted to reduce the complexity of the problem by resolving the conflicts between agent paths separately. Very little of this research has focused on creating quality heuristics to help solve the problem. In this thesis, we create heuristics using sub-problems created by removing agents from a complete problem instance. We combine this with the Independence Detection technique for solving the problem by separating agents into independent (non-conflicting) groups. The results showed moderate improvements in state expansions and computation time in problems with a large number of conflicting agents.

Provenance

Recieved from ProQuest

Rights holder

Aaron R. Kraft

File size

73 p.

File format

application/pdf

Language

en

Discipline

Computer science

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