Authors:
Aymeric Le Dorze
1
;
Béatrice Duval
1
;
Laurent Garcia
1
;
David Genest
1
;
Philippe Leray
2
and
Stéphane Loiseau
1
Affiliations:
1
Université d’Angers, France
;
2
École Polytechnique and Université de Nantes, France
Keyword(s):
Cognitive Map, Probabilities, Causality, Bayesian Network.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Bayesian Networks
;
Cognitive Systems
;
Computational Intelligence
;
Enterprise Information Systems
;
Evolutionary Computing
;
Knowledge Representation and Reasoning
;
Soft Computing
;
Symbolic Systems
Abstract:
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.