Causal Reasoning in Multi-Agent Systems

Causal knowledge involves many interacting concepts that make them difficult to deal with, and for which analytical techniques are inadequate. Usually, a causal map (CM) is employed to cope with this type of knowledge. Causal reasoning is important in multiagent environments because it allows to model interrelationships or causalities among a set of individual and social concepts. This provides a foundation to (1) test a model about the prediction of how agents will respond to (unexpected or not) events; (2) explain how agents have done specific actions; (3) make a decision in a distributed environment; (4) analyze and compare the agents' causal representations. All these aspects are important for coordination, conflict solving and the emergence of cooperation between autonomous agents.

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