Abstract
In recent work, a generalised form of characteristic function games has been introduced, where certain sequences of coalition structures (and not a single one) are considered as solutions. Such games have later been extended to allow valuation structures to be used to restrict the allowed solutions for each game in the sequence; the resulting game is called SEQVS. This paper introduces an algorithm to solve instances of SEQVS based on Monte Carlo Tree Search. We experimentally evaluate the algorithm by comparing its performance against a heuristic algorithm appearing in the literature. We show that in settings containing many constraints, our algorithm outperforms the existing heuristic approach.
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Krausburg, T., Dix, J., Bordini, R.H. (2023). An MCTS-Based Algorithm to Solve Sequential CFGs on Valuation Structures. In: Aydoğan, R., Criado, N., Lang, J., Sanchez-Anguix, V., Serramia, M. (eds) PRIMA 2022: Principles and Practice of Multi-Agent Systems. PRIMA 2022. Lecture Notes in Computer Science(), vol 13753. Springer, Cham. https://doi.org/10.1007/978-3-031-21203-1_24
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