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- ArticleJuly 1998
Flexible and approximate computation through state-space reduction
UAI'98: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligenceJuly 1998, Pages 531–538In the real world, insufficient information, limited computation resources, and complex problem structures often force an autonomous agent to make a decision in time less than that required to solve the problem at hand completely. Flexible and ...
- ArticleJuly 1998
Planning with partially observable Markov decision processes: advances in exact solution method
UAI'98: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligenceJuly 1998, Pages 523–530There is much interest in using partially observable Markov decision processes (POMDPs) as a formal model for planning in stochastic domains. This paper is concerned with finding optimal policies for POMDPs. We propose several improvements to ...
- ArticleJuly 1998
Probabilistic inference in influence diagrams
UAI'98: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligenceJuly 1998, Pages 514–522This paper is about reducing influence diagram (ID) evaluation into Bayesian network (BN) inference problems. Such reduction is interesting because it enables one to readily use one's favorite BN inference algorithm to efficiently evaluate IDS. Two such ...
- ArticleJuly 1998
Decision theoretic foundations of graphical model selection
UAI'98: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligenceJuly 1998, Pages 464–471This paper describes a decision theoretic formulation of learning the graphical structure of a Bayesian Belief Network from data. This framework subsumes the standard Bayesian approach of choosing the model with the largest posterior probability as the ...
- ArticleJuly 1998
Empirical evaluation of approximation algorithms for probabilistic decoding
UAI'98: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligenceJuly 1998, Pages 455–463It was recently shown that the problem of decoding messages transmitted through a noisy channel can be formulated as a belief updating task over a probabilistic network [14]. Moreover, it was observed that iterative application of the (linear time) ...
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- ArticleJuly 1998
Context-specific approximation in probabilistic inference
UAI'98: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligenceJuly 1998, Pages 447–454There is evidence that the numbers in probabilistic inference don't really matter. This paper considers the idea that we can make a probabilistic model simpler by making fewer distinctions. Unfortunately, the level of a Bayesian network seems too coarse;...
- ArticleJuly 1998
Logarithmic time parallel Bayesian inference
UAI'98: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligenceJuly 1998, Pages 431–438I present a parallel algorithm for exact probabilistic inference in Bayesian networks. For polytree networks with n variables, the worstcase time complexity is O(logn) on a CREW PRAM (concurrent-read, exclusive-write parallel random-access machine) with ...
- ArticleJuly 1998
Flexible decomposition algorithms for weakly coupled Markov decision problems
UAI'98: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligenceJuly 1998, Pages 422–430This paper presents two new approaches to decomposing and solving large Markov decision problems (MDPs), a partial decoupling method and a complete decoupling method. In these approaches, a large, stochastic decision problem is divided into smaller ...
- ArticleJuly 1998
From likelihood to plausibility
UAI'98: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligenceJuly 1998, Pages 396–403Several authors have explained that the likelihood ratio measures the strength of the evidence represented by observations in statistical problems. This idea works fine when the goal is to evaluate the strength of the available evidence for a simple ...
- ArticleJuly 1998
Magic inference rules for probabilistic deduction under taxonomic knowledge
UAI'98: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligenceJuly 1998, Pages 354–361We present locally complete inference rules for probabilistic deduction from taxonomic and probabilistic knowledge-bases over conjunctive events. Crucially, in contrast to similar inference rules in the literature, our inference rules are locally ...
- ArticleJuly 1998
Using qualitative relationships for bounding probability distributions
UAI'98: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligenceJuly 1998, Pages 346–353We exploit qualitative probabilistic relationships among variables for computing bounds of conditional probability distributions of interest in Bayesian networks. Using the signs of qualitative relationships, we can implement abstraction operations that ...
- ArticleJuly 1998
Incremental tradeoff resolution in qualitative probabilistic networks
UAI'98: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligenceJuly 1998, Pages 338–345Qualitative probabilistic reasoning in a Bayesian network often reveals tradeoffs: relationships that are ambiguous due to competing qualitative influences. We present two techniques that combine qualitative and numeric probabilistic reasoning to ...
- ArticleJuly 1998
A comparison of Lauritzen-Spiegelhalter, Hugin, and Shenoy-Shafer architectures for computing marginals of probability distributions
UAI'98: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligenceJuly 1998, Pages 328–337In the last decade, several architectures have been proposed for exact computation of marginals using local computation. In this paper, we compare three architectures--Lauritzen-Spiegelhalter, Hugin, and Shenoy-Shafer--from the perspective of graphical ...
- ArticleJuly 1998
Mixture representations for inference and learning in Boltzmann machines
UAI'98: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligenceJuly 1998, Pages 320–327Boltzmann machines are undirected graphical models with two-state stochastic variables, in which the logarithms of the clique potentials are quadratic functions of the node states. They have been widely studied in the neural computing literature, ...
- ArticleJuly 1998
Large deviation methods for approximate probabilistic inference
UAI'98: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligenceJuly 1998, Pages 311–319We study two-layer belief networks of binary random variables in which the conditional probabilities Pr [child|parents] depend monotonically on weighted sums of the parents. In large networks where exact probabilistic inference is intractable, we show ...
- ArticleJuly 1998
Implementing resolute choice under uncertainty
UAI'98: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligenceJuly 1998, Pages 282–288The adaptation to situations of sequential choice under uncertainty of decision criteria which deviate from (subjective) expected utility raises the problem of ensuring the selection of a nondominated strategy. In particular, when following the ...
- ArticleJuly 1998
Measure selection: notions of rationality and representation independence
UAI'98: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligenceJuly 1998, Pages 274–281We take another look at the general problem of selecting a preferred probability measure among those that comply with some given constraints. The dominant role that entropy maximization has obtained in this context is questioned by arguing that the ...
- ArticleJuly 1998
Any time probabilistic reasoning for sensor validation
UAI'98: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligenceJuly 1998, Pages 266–273For many real time applications, it is important to validate the information received from the sensors before entering higher levels of reasoning. This paper presents an any time probabilistic algorithm for validating the information provided by ...
- ArticleJuly 1998
An anytime algorithm for decision making under uncertainty
UAI'98: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligenceJuly 1998, Pages 246–255We present an anytime algorithm which computes policies for decision problems represented as multi-stage influence diagrams. Our algorithm constructs policies incrementally, starting from a policy which makes no use of the available information. The ...
- ArticleJuly 1998
Evaluating las vegas algorithms: pitfalls and remedies
UAI'98: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligenceJuly 1998, Pages 238–245Stochastic search algorithms are among the most sucessful approaches for solving hard combinatorial problems. A large class of stochastic search approaches can be cast into the framework of Las Vegas Algorithms (LVAs). As the run-time behavior of LVAs ...