- Stefano Mariani ORCID:orcid.org/0000-0001-8921-815011,
- Pasquale Roseti ORCID:orcid.org/0000-0001-7066-846911 &
- Franco Zambonelli ORCID:orcid.org/0000-0002-6837-880611
Part of the book series:Lecture Notes in Computer Science ((LNAI,volume 13955))
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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
University of Modena and Reggio Emilia, 42122, Reggio Emilia, Italy
Stefano Mariani, Pasquale Roseti & Franco Zambonelli
- Stefano Mariani
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- Pasquale Roseti
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- Franco Zambonelli
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Correspondence toStefano Mariani.
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University of Lille, Lille, France
Philippe Mathieu
Umeå University, Umeå, Sweden
Frank Dignum
Universidade do Minho, Braga, Portugal
Paulo Novais
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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