Probabilistic Cognitive Maps Semantics of a Cognitive Map when the Values are Assumed to be Probabilities

摘要

Cognitive maps are a knowledge representation model that describes influences between concepts by a graph, where each influence is quantified by a value. The values are generally not formally defined. In this paper, we introduce a new cognitive map model, the probabilistic cognitive maps. In such maps, the values of the influences are interpreted as probability values. We define formally the semantics of this model. We also provide an operation to compute the global influence of a concept on any other one, called the probabilistic propagated influence. To show that our model is valid, we propose a procedure to represent a probabilistic cognitive map as a Bayesian network. This new model strengthens cognitive maps by giving them strong semantics. Moreover, it acts as a bridge between cognitive maps and Bayesian networks.