Abstract
Behavior composition problem is particularly relevant for multi-agent systems and aims at building a complex target behavior using several agent behaviors. In this work, we develop a framework that models the agent behaviors in stochastic settings both when the target is represented as a Finite State Machine and when it is represented as an ltl\(_f\) formula.
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Notes
- 1.
An MDP is a tuple \(\mathcal {M}= \langle S,A,T,R\rangle \) formed by: a set S of states, a set A of actions, a transition function \(T : S\times A \rightarrow Prob(S)\), and a reward function \(R : S\times A\rightarrow \mathbb {R}\).
- 2.
ltl\(_f\) is a variant of Linear Temporal Logic (ltl) interpreted over finite traces, instead of infinite ones [7].
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Silo, L. (2023). Agent Behavior Composition in Stochastic Settings. In: Malvone, V., Murano, A. (eds) Multi-Agent Systems. EUMAS 2023. Lecture Notes in Computer Science(), vol 14282. Springer, Cham. https://doi.org/10.1007/978-3-031-43264-4_45
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