CAUMEL: A Temporal Logic Based Language for Causal Maps to Explain Agent Behaviors

  • Aroua Hedhili Sbaï
  • Wided Lejouad Chaari
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 296)


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.


causal map explanation agent behavior temporal logic 


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© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Strategies of Optimization and Intelligent Computing Laboratory (SOIE)National School of Computer Studies (ENSI)-University of ManoubaManoubaTunisia

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