Abstract
Causal maps are a powerful tools, used to deal with causal relations between events. They are frequently developed for specific issues such as decision analysis and problems diagnostic. The approach described in this paper underlines their novel utility providing a foundation to explain how agents have done actions. In fact, Multi-Agent Systems (MAS) are considered as complex systems, in which agent actions are affected by several factors as uncertain beliefs, intentions of other agents, high interaction, and the dynamic aspect of the environment. Thus, we believe that it is crucial to elucidate the agent system’s behavior. To address the explanation of agent behaviors, this research presents, summarily, our method to build the causal map that corresponds to observed events during agent activities. Then, it focuses on a formal logic theory to interpret the built causal map, which includes causation between temporally ordered actions.
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Hedhili Sbaï, A., Chaari, W.L. (2014). CAUMEL: A Temporal Logic Based Language for Causal Maps to Explain Agent Behaviors. In: Jezic, G., Kusek, M., Lovrek, I., J. Howlett, R., Jain, L. (eds) Agent and Multi-Agent Systems: Technologies and Applications. Advances in Intelligent Systems and Computing, vol 296. Springer, Cham. https://doi.org/10.1007/978-3-319-07650-8_14
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DOI: https://doi.org/10.1007/978-3-319-07650-8_14
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07649-2
Online ISBN: 978-3-319-07650-8
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