Lessons Learned from the Effort to Solve Cooperative Path-Finding Optimally: Reductions to Propositional Satisfiability

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F14%3A10287452" target="_blank" >RIV/00216208:11320/14:10287452 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Lessons Learned from the Effort to Solve Cooperative Path-Finding Optimally: Reductions to Propositional Satisfiability
Original language description
This paper addresses makespan optimal solving of cooperative path-finding problem (CPF) by translating it to propositional satisfiability (SAT). The task in CPF is to relocate a set of agents to given goal locations so that they do not collide with eachother. Recent findings indicate that a simple direct encoding outperforms the more elaborate encodings based on binary encodings of multi-value state variables. The direct encoding is further improved by a hierarchical build-up that uses auxiliary variables to reduce its size in this work. The conducted experimental evaluation shown that the simple design of the encoding together with new improvements which reduced its size significantly are key enablers for faster solving of the encoded CPFs than withexisting encodings. It has been also shown that the SAT based methods dominates over A* based methods in envi-ronments with high occupancy by agents.
Czech name
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Czech description
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Project
<a href="/en/project/GAP103%2F10%2F1287" target="_blank" >GAP103/10/1287: PlanEx: Bridging Planning and Execution</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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