Advances in Intelligent Computing - IPMU '94

Philosophical foundations for causal networks.- Non standard probabilistic and non probabilistic representations of uncertainty.- Argumentation systems and evidence theory.- Algebraic structures related to Dempster-Shafer theory.- Extension of lower probabilities and coherence of belief measures.- A note on multi-objective information measures.- Decision making using belief functions evaluation of information.- Hybrid propagation in junction trees.- Heuristic algorithms for the triangulation of graphs.- Computing marginals from the marginal representation in Markov trees.- Representation of Bayesian networks as relational databases.- Decision influence diagrams with fuzzy utilities.- Causal Networks and their toolkit in KSE.- Approximations of causal networks by polytrees: an empirical study.- Evidence propagation on influence diagrams and value of evidence.- Some varieties of qualitative probability.- The qualitative verification of quantitative uncertainty.- Uncertainty management using probability intervals.- Probabilistic consistency of conditional probability bounds.- Testing the convenience of a variate for stratification in estimating the Gini-Simpson diversity.- Fuzzy probabilities and their applications to statistical inference.- A bayesian functional approach to fuzzy system representation.- Information matrices associated to (h, ?)-divergence measures: Applications to testing hypotheses.- Informational energy test for equality of variances.- Compositive information measure of a fuzzy set.- Informational time.- Updating, transition constraints and possibilistic Markov chains.- Possibilistic logic as interpretability logic.- A numerical framework for possibilistic abduction.- Possibility theory and independence.- Handling hard rules and default rules in possibilistic logic.- A general possibilistic framework for reliability theory.- Possibilistic semantic nets.- An essay on name and extension of rule-given properties.- On non-alethic logic.- Marginal