Reduction-based Solvers for Multi-agent Pathfinding: Comparing Different Models
Redukční řešiče pro multiagentní plánování cest: porovnání modelů
bachelor thesis (DEFENDED)
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Permanent link
http://hdl.handle.net/20.500.11956/183059Identifiers
Study Information System: 254076
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- Kvalifikační práce [11241]
Author
Advisor
Referee
Barták, Roman
Faculty / Institute
Faculty of Mathematics and Physics
Discipline
Computer Science with specialisation in Artificial Intelligence
Department
Department of Theoretical Computer Science and Mathematical Logic
Date of defense
29. 6. 2023
Publisher
Univerzita Karlova, Matematicko-fyzikální fakultaLanguage
English
Grade
Excellent
Keywords (Czech)
multi-agent pathfinding|reduction-based solvers|SAT|makespanKeywords (English)
multi-agent pathfinding|reduction-based solvers|SAT|makespan- Samyuktha Ramesh Thesis Title: Reduction-based Solvers for Multi-agent Pathfinding: Comparing Different Models Multi-agent path finding (MAPF) is the problem of navigating a set of agents from their starting position to their respective goal position without any collisions. In this thesis, we provide an overview of the current approaches to solving MAPF. We implement six different encodings found in the literature using the Python programming language and the Glucose3 SAT solver. We run experiments on maps of different types and sizes to compare the performances of the encodings.