Date of Award

1-1-2017

Document Type

Masters Thesis

Degree Name

M.S.

Organizational Unit

Daniel Felix Ritchie School of Engineering and Computer Science

First Advisor

Nathan Sturtevant, Ph.D.

Second Advisor

Mario Lopez

Third Advisor

Jun Zhang

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.

Publication Statement

Copyright is held by the author. User is responsible for all copyright compliance.

Rights Holder

Aaron R. Kraft

Provenance

Received from ProQuest

File Format

application/pdf

Language

en

File Size

73 p.

Discipline

Computer Science

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