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81
Operations for Learning with Graphical Models
 Journal of Artificial Intelligence Research
, 1994
"... This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Wellknown examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models ..."
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Cited by 253 (12 self)
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This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Wellknown examples of graphical models include Bayesian networks, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, and the manipulation of probability models from the exponential family. Two standard algorithm schemas for learning are reviewed in a graphical framework: Gibbs sampling and the expectation maximization algorithm. Using these operations and schemas, some popular algorithms can be synthesized from their graphical specification. This includes versions of linear regression, techniques for feedforward networks, and learning Gaussian and discrete Bayesian networks from data. The paper conclu...
A Guide to the Literature on Learning Probabilistic Networks From Data
, 1996
"... This literature review discusses different methods under the general rubric of learning Bayesian networks from data, and includes some overlapping work on more general probabilistic networks. Connections are drawn between the statistical, neural network, and uncertainty communities, and between the ..."
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Cited by 179 (0 self)
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This literature review discusses different methods under the general rubric of learning Bayesian networks from data, and includes some overlapping work on more general probabilistic networks. Connections are drawn between the statistical, neural network, and uncertainty communities, and between the different methodological communities, such as Bayesian, description length, and classical statistics. Basic concepts for learning and Bayesian networks are introduced and methods are then reviewed. Methods are discussed for learning parameters of a probabilistic network, for learning the structure, and for learning hidden variables. The presentation avoids formal definitions and theorems, as these are plentiful in the literature, and instead illustrates key concepts with simplified examples. Keywords Bayesian networks, graphical models, hidden variables, learning, learning structure, probabilistic networks, knowledge discovery. I. Introduction Probabilistic networks or probabilistic gra...
Probabilistic Assessment of User’s Emotions in Educational Games
 Journal of Applied Artificial Intelligence
, 2002
"... We present a probabilistic model to monitor a user’s emotions and engagement during the interaction with educational games. We illustrate how our probabilistic model assesses affect by integrating evidence on both possible causes of the user’s emotional arousal (i.e., the state of the interaction) a ..."
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Cited by 115 (5 self)
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We present a probabilistic model to monitor a user’s emotions and engagement during the interaction with educational games. We illustrate how our probabilistic model assesses affect by integrating evidence on both possible causes of the user’s emotional arousal (i.e., the state of the interaction) and its effects (i.e., bodily expressions that are known to be influenced by emotional reactions). The probabilistic model relies on a Dynamic Decision Network to leverage any indirect evidence on the user’s emotional state, in order to estimate this state and any other related variable in the model. This is crucial in a modeling task in which the available evidence usually varies with the user and with each particular interaction. The probabilistic model we present is to be used by decision theoretic pedagogical agents to generate interventions aimed at achieving the best tradeoff between a user’s learning and engagement during the interaction with educational games. 2 1.
Numerical Uncertainty Management in User and Student Modeling: An Overview of Systems and Issues
, 1996
"... . A rapidly growing number of user and student modeling systems have employed numerical techniques for uncertainty management. The three major paradigms are those of Bayesian networks, the DempsterShafer theory of evidence, and fuzzy logic. In this overview, each of the first three main sections fo ..."
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Cited by 111 (10 self)
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. A rapidly growing number of user and student modeling systems have employed numerical techniques for uncertainty management. The three major paradigms are those of Bayesian networks, the DempsterShafer theory of evidence, and fuzzy logic. In this overview, each of the first three main sections focuses on one of these paradigms. It first introduces the basic concepts by showing how they can be applied to a relatively simple user modeling problem. It then surveys systems that have applied techniques from the paradigm to user or student modeling, characterizing each system within a common framework. The final main section discusses several aspects of the usability of these techniques for user and student modeling, such as their knowledge engineering requirements, their need for computational resources, and the communicability of their results. Key words: numerical uncertainty management, Bayesian networks, DempsterShafer theory, fuzzy logic, user modeling, student modeling 1. Introdu...
Control of Selective Perception Using Bayes Nets and Decision Theory
, 1993
"... A selective vision system sequentially collects evidence to support a specified hypothesis about a scene, as long as the additional evidence is worth the effort of obtaining it. Efficiency comes from processing the scene only where necessary, to the level of detail necessary, and with only the neces ..."
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Cited by 107 (1 self)
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A selective vision system sequentially collects evidence to support a specified hypothesis about a scene, as long as the additional evidence is worth the effort of obtaining it. Efficiency comes from processing the scene only where necessary, to the level of detail necessary, and with only the necessary operators. Knowledge representation and sequential decisionmaking are central issues for selective vision, which takes advantage of prior knowledge of a domain's abstract and geometrical structure and models for the expected performance and cost of visual operators. The TEA1 selective vision system uses Bayes nets for representation and benefitcost analysis for control of visual and nonvisual actions. It is the highlevel control for an active vision system, enabling purposive behavior, the use of qualitative vision modules and a pointable multiresolution sensor. TEA1 demonstrates that Bayes nets and decision theoretic techniques provide a general, reusable framework for constructi...
The Sensitivity of Belief Networks to Imprecise Probabilities: An Experimental Investigation
, 1995
"... bilities may not impair diagnostic performance significantly, and that simple binary representations may often be adequate. These findings of robustness suggest that belief networks are a practical representation without requiring undue precision. Probabilistic Reasoning, Bayesian Networks als ..."
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Cited by 58 (2 self)
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bilities may not impair diagnostic performance significantly, and that simple binary representations may often be adequate. These findings of robustness suggest that belief networks are a practical representation without requiring undue precision. Probabilistic Reasoning, Bayesian Networks also Section on Medical Informatics, Stanford University, Stanford, CA 94305. also EngineeringEconomic Systems, Stanford University, Stanford, CA 94305. 1 The Tradeoff Between Accuracy and Cost sufficiently accurate 1.1 Experiments on Belief Networks Each knowledge representation or model is, by definition, a simplification of reality. When the representation is derived from a human expert, it is a simplification even of the expert's perception of reality. The question in choosing a representation is not whether the representation is completely accurateit cannot bebut whether the model is for the purposes for which it is designed. This question is the one tha
Optimization by learning and simulation of Bayesian and Gaussian networks
, 1999
"... Estimation of Distribution Algorithms (EDA) constitute an example of stochastics heuristics based on populations of individuals every of which encode the possible solutions to the optimization problem. These populations of individuals evolve in succesive generations as the search progresses  organ ..."
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Cited by 46 (6 self)
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Estimation of Distribution Algorithms (EDA) constitute an example of stochastics heuristics based on populations of individuals every of which encode the possible solutions to the optimization problem. These populations of individuals evolve in succesive generations as the search progresses  organized in the same way as most evolutionary computation heuristics. In opposition to most evolutionary computation paradigms which consider the crossing and mutation operators as essential tools to generate new populations, EDA replaces those operators by the estimation and simulation of the joint probability distribution of the selected individuals. In this work, after making a review of the different approaches based on EDA for problems of combinatorial optimization as well as for problems of optimization in continuous domains, we propose new approaches based on the theory of probabilistic graphical models to solve problems in both domains. More precisely, we propose to adapt algorit...
Principles and applications of continual computation
 Artificial Intelligence J
"... Automated problem solving is viewed typically as the allocation of computational resources to solve one or more problems passed to a reasoning system. In response to each problem received, eort is applied in real time to generate a solution and problem solving ends when a solution is rendered. We ..."
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Cited by 42 (9 self)
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Automated problem solving is viewed typically as the allocation of computational resources to solve one or more problems passed to a reasoning system. In response to each problem received, eort is applied in real time to generate a solution and problem solving ends when a solution is rendered. We examine continual computation, reasoning policies that capture a broader conception of problem by considering the proactive allocation of computational resources to potential future challenges. We explore policies for allocating idle time for several settings and present applications that highlight opportunities for harnessing continual computation in realworld tasks.
Graphical Models for Discovering Knowledge
, 1995
"... There are many different ways of representing knowledge, and for each of these ways there are many different discovery algorithms. How can we compare different representations? How can we mix, match and merge representations and algorithms on new problems with their own unique requirements? This cha ..."
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Cited by 29 (2 self)
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There are many different ways of representing knowledge, and for each of these ways there are many different discovery algorithms. How can we compare different representations? How can we mix, match and merge representations and algorithms on new problems with their own unique requirements? This chapter introduces probabilistic modeling as a philosophy for addressing these questions and presents graphical models for representing probabilistic models. Probabilistic graphical models are a unified qualitative and quantitative framework for representing and reasoning with probabilities and independencies. 4.1 Introduction Perhaps one common element of the discovery systems described in this and previous books on knowledge discovery is that they are all different. Since the class of discovery problems is a challenging one, we cannot write a single program to address all of knowledge discovery. The KEFIR discovery system applied to health care by Matheus, PiatetskyShapiro, and McNeill (199...
Models of Continual Computation
 Fourteenth National Conference on Artificial Intelligence
, 1997
"... Automated problem solving is viewed typically as the expenditure of computation to solve one or more problems passed to a reasoning system. In response to each problem received, effort is applied to generate a solution and problem solving ends when the solution is rendered. We discuss the notion of ..."
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Cited by 25 (2 self)
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Automated problem solving is viewed typically as the expenditure of computation to solve one or more problems passed to a reasoning system. In response to each problem received, effort is applied to generate a solution and problem solving ends when the solution is rendered. We discuss the notion of continual computation that addresses a broader conception of problem by considering the ideal use of the idle time between problem instances. The time is used to develop solutions proactively to one or more expected challenges in the future. We consider analyses for traditional allornothing algorithms as well as more flexible computational procedures. After exploring the allocation of idle time for several settings, we generalize the analysis to consider the case of shifting computation from a current problem to solve future challenges. Finally, we discuss a sample application of the use of continual computation in the setting of diagnostic reasoning. 1 Introduction Computational proble...