Correctness-guaranteed strategy synthesis and compression for multi-agent autonomous systems

Planning is a critical function of multi-agent autonomous systems, which includes path finding and task scheduling. Exhaustive search-based methods such as model checking and algorithmic game theory can solve simple instances of multi-agent planning. However, these methods suffer from state-space ex...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Science of computer programming Ročník 224; s. 102894
Hlavní autori: Gu, Rong, Jensen, Peter G., Seceleanu, Cristina, Enoiu, Eduard, Lundqvist, Kristina
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier B.V 01.12.2022
Predmet:
Multi-agent autonomous systems
Planning
Reinforcement learning
Strategy compression
Timed games
Multi-agent autonomous systems
Strategy compression
Planning
Reinforcement learning
Timed games
ISSN:0167-6423, 1872-7964, 1872-7964
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Planning is a critical function of multi-agent autonomous systems, which includes path finding and task scheduling. Exhaustive search-based methods such as model checking and algorithmic game theory can solve simple instances of multi-agent planning. However, these methods suffer from state-space explosion when the number of agents is large. Learning-based methods can alleviate this problem, but lack a guarantee of correctness of the results. In this paper, we introduce MoCReL, a new version of our previously proposed method that combines model checking with reinforcement learning in solving the planning problem. The approach takes advantage of reinforcement learning to synthesize path plans and task schedules for large numbers of autonomous agents, and of model checking to verify the correctness of the synthesized strategies. Further, MoCReL can compress large strategies into smaller ones that have down to 0.05% of the original sizes, while preserving their correctness, which we show in this paper. MoCReL is integrated into a new version of Uppaal Stratego that supports calling external libraries when running learning and verification of timed games models.
ISSN:0167-6423
1872-7964
1872-7964
DOI:10.1016/j.scico.2022.102894