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Multi-agent Learning of Causal Networks in the Internet of Things

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Abstract

In Internet of Things deployments, such as a smart home, building, or city, it is of paramount importance for software agents to be aware of thecausal model of the environment in which they operate (i.e. of the causal network relating actions to their effects and observed variables to each other). Yet, the complexity and dynamics of the environment can prevent to specify such model at design time, thus requiring agents tolearn its structure at run-time. Accordingly, we introduce a distributedmulti-agent protocol in which a set of agents, each with partial observability, cooperate to learn a coherent and accurate personal view of the causal network. We evaluate such protocol in the context of a smart home scenario and for a two-agents case, showing that it has superior accuracy in recovering the ground truth network.

Work supported by the MIUR PRIN 2017 Project “Fluidware” (N. 2017KRC7KT).

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Authors and Affiliations

  1. University of Modena and Reggio Emilia, 42122, Reggio Emilia, Italy

    Stefano Mariani, Pasquale Roseti & Franco Zambonelli

Authors
  1. Stefano Mariani

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  2. Pasquale Roseti

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  3. Franco Zambonelli

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Corresponding author

Correspondence toStefano Mariani.

Editor information

Editors and Affiliations

  1. University of Lille, Lille, France

    Philippe Mathieu

  2. Umeå University, Umeå, Sweden

    Frank Dignum

  3. Universidade do Minho, Braga, Portugal

    Paulo Novais

  4. University of Salamanca, Salamanca, Spain

    Fernando De la Prieta

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Mariani, S., Roseti, P., Zambonelli, F. (2023). Multi-agent Learning of Causal Networks in the Internet of Things. In: Mathieu, P., Dignum, F., Novais, P., De la Prieta, F. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Cognitive Mimetics. The PAAMS Collection. PAAMS 2023. Lecture Notes in Computer Science(), vol 13955. Springer, Cham. https://doi.org/10.1007/978-3-031-37616-0_14

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