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Agent Behavior Composition in Stochastic Settings

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Multi-Agent Systems (EUMAS 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14282))

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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. 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. 2.

    ltl\(_f\) is a variant of Linear Temporal Logic (ltl) interpreted over finite traces, instead of infinite ones [7].

References

  1. Berardi, D., Calvanese, D., De Giacomo, G., Hull, R., Mecella, M.: Automatic composition of transition-based semantic web services with messaging. In: VLDB, vol. 5, pp. 613–624 (2005)

    Google Scholar 

  2. Berardi, D., Calvanese, D., De Giacomo, G., Lenzerini, M., Mecella, M.: Automatic composition of E-services that export their behavior. In: Orlowska, M.E., Weerawarana, S., Papazoglou, M.P., Yang, J. (eds.) ICSOC 2003. LNCS, vol. 2910, pp. 43–58. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-24593-3_4

    Chapter  Google Scholar 

  3. Brafman, R.I., De Giacomo, G., Mecella, M., Sardina, S.: Service composition in stochastic settings. In: Esposito, F., Basili, R., Ferilli, S., Lisi, F. (eds.) AI*IA 2017. LNCS, vol. 10640, pp. 159–171. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70169-1_12

    Chapter  Google Scholar 

  4. De Giacomo, G., Favorito, M., Leotta, F., Mecella, M., Monti, F., Silo, L.: AIDA: a tool for resiliency in smart manufacturing. In: Cabanillas, C., Pérez, F. (eds.) CAiSE 2023. LNBIP, vol. 477, pp. 112–120. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-34674-3_14

    Chapter  Google Scholar 

  5. De Giacomo, G., Favorito, M., Leotta, F., Mecella, M., Silo, L.: Digital twins composition in smart manufacturing via Markov decision processes. Comput. Ind. 149, 103916 (2023)

    Google Scholar 

  6. De Giacomo, G., Patrizi, F., Sardina, S.: Automatic behavior composition synthesis. Artif. Intell. 196, 106–142 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  7. De Giacomo, G., Vardi, M.Y.: Linear temporal logic and linear dynamic logic on finite traces. In: IJCAI 2013 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence, pp. 854–860. ACM (2013)

    Google Scholar 

  8. Pesic, M., Schonenberg, H., Van der Aalst, W.M.: DECLARE: full support for loosely-structured processes. In: 11th IEEE International Enterprise Distributed Object Computing Conference (EDOC 2007), pp. 287–287. IEEE (2007)

    Google Scholar 

  9. Rabin, M.O., Scott, D.: Finite automata and their decision problems. IBM J. Res. Dev. 3(2), 114–125 (1959)

    Article  MathSciNet  MATH  Google Scholar 

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Correspondence to Luciana Silo .

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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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  • DOI: https://doi.org/10.1007/978-3-031-43264-4_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43263-7

  • Online ISBN: 978-3-031-43264-4

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