## Uncertainty in artificial intelligence: proceedings of the Fourteenth Conference (1998), July 24-26, 1998, University of Wisconsin, Madison, Wisconsin, USA |

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### Contents

Merging uncertain knowledge bases in a possibilistic logic framework | 8 |

Structured Reachability Analysis for Markov Decision Processes | 24 |

Empirical Analysis of Predictive Algorithms for Collaborative Filtering | 43 |

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30 other sections not shown

### Common terms and phrases

abstract action algorithm apply approach approximation argument argumentation framework Artificial Intelligence assume Bayesian networks belief network binary causal chain graph clique clusters complete compute conditional probability consider constraints construction context corresponding d-separation dataset decision problem decision tree defined definition denote discrete edge equations equivalent evaluation example expected exponential exponential family factor Figure finite given graphical models Heckerman hidden variables Hugin hypergraph independent inference influence diagrams input iteration join tree jointree junction tree knowledge base learning Lemma likelihood linear macros marginal marginal likelihood Markov method observed optimal paper parameters parents performance polytree possible posterior posterior probability potential prob probabilistic probability distribution propagation qualitative query random variables reachable representation represented rules score Section sensor Shachter solution space statistics stochastic strategy structure subset Theorem theory tion Uncertainty in Artificial update utility function value function vector