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. In this paper, we investigate the issue of using cognitive maps (CMs) in multiagent environments. Firstly, we explain through different examples how and why these maps are useful in those environments. Then, we present a formal model which establishes the mathematical basis for the manipulation of (CMs). The formal model is used to derive a cognitive map from a set of assertions, to determine the total effect of any concept variable on any other concept variable.