Eleven contributions : 1: Personalistic Bayesianism; C. Howson. 2: On Higher Order Beliefs; N.-E. Sahlin. 3: On the Logic of Relevance; P. Gr̃denfors. 4: Diverging Distributions; D. Miller. 5: Inductive Logic Revisited; J.-P. Dubucs. 6: Probability and Utility; J.M. Vickers. 7: What has Probability to Do with Strength of Belief; L.J. Cohen. 8: Randomness, Unpredictability and Absence of Order: the Identification by the Theory of Recursivity of the Mathematical Notion of Random Sequence; J.-P. Delahaye. 9: A Glance at Non-Standard Models and Logics of Uncertainty and Vaguness; D. Dubois, H. Prade. 10: Causal Laws are Objectifications of Inductive Schemes; W. Spohn. 11. Probabilistic Inference in Artificial Intelligence: the Method of Bayesian networks; J.-L. Golmard. – They intend to provide a comprehensive introduction to theoretical issues that occupy a central position in disciplines ranging from philosophy of mind and epistemology to cognitive science, decision theory and artificial intelligence. Some contributions shed new light on the standard conceptions of probability (such as Bayesianism, logical and computational theories); others offer detailed analyses of two important topics in the field of cognitive science : – the meaning and the representation of (partial) belief, and – the management of uncertaincy . This multidisciplinary approach to probability (the authors are philosophers as well as computer scientists) is designed to illuminate the intricacies of the problems in the domain of cognitive inquiry. M.-M. V.