Decision theory in expert systems and artificial intelligence

Horvitz, Eric; Breese, John S.; Henrion, Max

doi:10.1016/0888-613x(88)90120-x

Cited by 264 publications

(122 citation statements)

References 35 publications

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“…Arcs are represented as conditional-probability distributions. Belief networks, also known as probabilistic influence diagrams, causal probabilistic networks, and Bayesian networks, are described in more detail in (Cooper, 1989;Horvitz et al, 1988;Pearl, 1988).…”

Section: Introductionmentioning

confidence: 99%

“…Reasoning under uncertainty in belief networks takes place according to a paradigm consistent with probability theory (Horvitz et al, 1988). Therefore, the theoretical foundation for the conclusions of a system based on belief networks is strong, unlike the situation in systems that obtain results by heuristic methods (Cooper, 1989 (Heckerman and Horvitz, 1987;Heckerman, 1990a), which may ultimately affect our ability to obtain user confidence in the system's conclusions.…”

Section: Introductionmentioning

confidence: 99%

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An Evaluation of Explanations of Probabilistic Inference

Suermondt

Cooper²

1993

Computers and Biomedical Research

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Evaluation of Explanations of Probabilistic Inference

Suermondt

Cooper²

1993

Computers and Biomedical Research

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“…Based on Equation 4, in order to define the choice probability, only the difference between the utilities matters. The specification of the utility functions represents the modeler's mean to add her prior knowledge on the choice process (a similar interpretation of the decision theoretic approach can be found in [11]). In this sense, the DCM approach is similar to graphical probabilistic models, such as belief networks and random fields, where the graph topology embeds the prior knowledge, helping designing causal relationships.…”

Section: Discrete Choice Modelsmentioning

confidence: 99%

Discrete Choice Models for Static Facial Expression Recognition

Antonini

Sorci

Bierlaire

et al. 2006

Advanced Concepts for Intelligent Vision Systems

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Abstract. In this paper we propose the use of Discrete Choice Analysis (DCA) for static facial expression classification. Facial expressions are described with expression descriptive units (EDU), consisting in a set of high level features derived from an active appearance model (AAM). The discrete choice model (DCM) is built considering the 6 universal facial expressions plus the neutral one as the set of the available alternatives. Each alternative is described by an utility function, defined as the sum of a linear combination of EDUs and a random term capturing the uncertainty. The utilities provide a measure of likelihood for a combinations of EDUs to represent a certain facial expression. They represent a natural way for the modeler to formalize her prior knowledge on the process. The model parameters are learned through maximum likelihood estimation and classification is performed assigning each test sample to the alternative showing the maximum utility. We compare the performance of the DCM classifier against Linear Discriminant Analysis (LDA), Generalized Discriminant Analysis (GDA), Relevant Component Analysis (RCA) and Support Vector Machine (SVM). Quantitative preliminary results are reported, showing good and encouraging performance of the DCM approach both in terms of recognition rate and discriminatory power.

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“…A Bayesian belief-network structure Bs is a directed acyclic graph in which nodes represent domain variables and arcs between nodes represent probabilistic dependencies (Cooper, 1989;Horvitz, Breese, & Henrion, 1988;Lauritzen & Spiegelhalter, 1988;Neapolitan, 1990;Pearl, 1986;Pearl, 1988;Shachter, 1988). A variable in a Bayesian belief-network structure may be continuous (Shachter & Kenley, 1989) or discrete.…”

Section: Introductionmentioning

confidence: 99%

Untitled

1992

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Abstract. This paper presents a Bayesian method for constructing probabilistic networks from databases. In particular, we focus on constructing Bayesian belief networks. Potential applications include computer-assisted hypothesis testing, automated scientific discovery, and automated construction of probabilistic expert systems. We extend the basic method to handle missing data and hidden (latent) variables. We show how to perform probabilistic inference by averaging over the inferences of multiple belief networks. Results are presented of a preliminary evaluation of an algorithm for constructing a belief network from a database of cases. Finally, we relate the methods in this paper to previous work, and we discuss open problems.

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Decision theory in expert systems and artificial intelligence

Cited by 264 publications

References 35 publications

An Evaluation of Explanations of Probabilistic Inference

An Evaluation of Explanations of Probabilistic Inference

Discrete Choice Models for Static Facial Expression Recognition

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