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84
Rationality and intelligence
 Artificial Intelligence
, 1997
"... The longterm goal of our field is the creation and understanding of intelligence. Productive research in AI, both practical and theoretical, benefits from a notion of intelligence that is precise enough to allow the cumulative development of robust systems and general results. This paper outlines a ..."
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Cited by 106 (1 self)
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The longterm goal of our field is the creation and understanding of intelligence. Productive research in AI, both practical and theoretical, benefits from a notion of intelligence that is precise enough to allow the cumulative development of robust systems and general results. This paper outlines a gradual evolution in our formal conception of intelligence that brings it closer to our informal conception and simultaneously reduces the gap between theory and practice. 1 Artificial Intelligence AI is a field in which the ultimate goal has often been somewhat illdefined and subject to dispute. Some researchers aim to emulate human cognition, others aim at the creation of
A Bayesian Approach to Causal Discovery
, 1997
"... We examine the Bayesian approach to the discovery of directed acyclic causal models and compare it to the constraintbased approach. Both approaches rely on the Causal Markov assumption, but the two differ significantly in theory and practice. An important difference between the approaches is that t ..."
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Cited by 100 (1 self)
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We examine the Bayesian approach to the discovery of directed acyclic causal models and compare it to the constraintbased approach. Both approaches rely on the Causal Markov assumption, but the two differ significantly in theory and practice. An important difference between the approaches is that the constraintbased approach uses categorical information about conditionalindependence constraints in the domain, whereas the Bayesian approach weighs the degree to which such constraints hold. As a result, the Bayesian approach has three distinct advantages over its constraintbased counterpart. One, conclusions derived from the Bayesian approach are not susceptible to incorrect categorical decisions about independence facts that can occur with data sets of finite size. Two, using the Bayesian approach, finer distinctions among model structuresboth quantitative and qualitativecan be made. Three, information from several models can be combined to make better inferences and to better ...
Update rules for parameter estimation in Bayesian networks
, 1997
"... This paper reexamines the problem of parameter estimation in Bayesian networks with missing values and hidden variables from the perspective of recent work in online learning [12]. We provide a unified framework for parameter estimation that encompasses both online learning, where the model is co ..."
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Cited by 59 (2 self)
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This paper reexamines the problem of parameter estimation in Bayesian networks with missing values and hidden variables from the perspective of recent work in online learning [12]. We provide a unified framework for parameter estimation that encompasses both online learning, where the model is continuously adapted to new data cases as they arrive, and the more traditional batch learning, where a preaccumulated set of samples is used in a onetime model selection process. In the batch case, our framework encompassesboth the gradient projection algorithm [2, 3] and the EM algorithm [14] for Bayesian networks. The framework also leads to new online and batch parameter update schemes, including a parameterized version of EM. We provide both empirical and theoretical results indicating that parameterized EM allows faster convergence to the maximum likelihood parameters than does standard EM. 1 Introduction Over the past few years, there has been a growing interest in the problem of le...
Towards a Bayesian Model for Keyhole Plan Recognition in Large Domains
 In Proceedings of the Sixth International Conference on User Modeling
, 1997
"... . We present an approach to keyhole plan recognition which uses a Dynamic Belief Network to represent features of the domain that are needed to identify users' plans and goals. The structure of this network was determined from analysis of the domain. The conditional probability distributions ar ..."
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Cited by 51 (3 self)
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. We present an approach to keyhole plan recognition which uses a Dynamic Belief Network to represent features of the domain that are needed to identify users' plans and goals. The structure of this network was determined from analysis of the domain. The conditional probability distributions are learned during a training phase, which dynamically builds these probabilities from observations of user behaviour. This approach allows the use of incomplete, sparse and noisy data during both training and testing. We present experimental results of the application of our system to a MultiUser Dungeon adventure game with thousands of possible actions and positions. These results show a high degree of predictive accuracy and indicate that this approach will work in other domains with similar features. 1 Introduction To date, research in plan recognition has focused on three main areas: (1) inferring plans during cooperative interactions, (2) understanding stories, and (3) recognising the plans...
Sequential Update of Bayesian Network Structure
 In Proc. 13th Conference on Uncertainty in Artificial Intelligence (UAI’97
, 1997
"... There is an obvious need for improving the performance and accuracy of a Bayesian network as new data is observed. Because of errors in model construction and changes in the dynamics of the domains, we cannot afford to ignore the information in new data. While sequential update of parameters for a f ..."
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Cited by 50 (4 self)
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There is an obvious need for improving the performance and accuracy of a Bayesian network as new data is observed. Because of errors in model construction and changes in the dynamics of the domains, we cannot afford to ignore the information in new data. While sequential update of parameters for a fixed structure can be accomplished using standard techniques, sequential update of network structure is still an open problem. In this paper, we investigate sequential update of Bayesian networks were both parameters and structure are expected to change. We introduce a new approach that allows for the flexible manipulation of the tradeoff between the quality of the learned networks and the amount of information that is maintained about past observations. We formally describe our approach including the necessary modifications to the scoring functions for learning Bayesian networks, evaluate its effectiveness through and empirical study, and extend it to the case of missing data. 1 Introductio...
Learning Bayesian Nets that Perform Well
 In UAI97
, 1997
"... A Bayesian net (BN) is more than a succinct way to encode a probabilistic distribution; it also corresponds to a function used to answer queries. A BN can therefore be evaluated by the accuracy of the answers it returns. Many algorithms for learning BNs, however, attempt to optimize another criterio ..."
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Cited by 46 (16 self)
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A Bayesian net (BN) is more than a succinct way to encode a probabilistic distribution; it also corresponds to a function used to answer queries. A BN can therefore be evaluated by the accuracy of the answers it returns. Many algorithms for learning BNs, however, attempt to optimize another criterion (usually likelihood, possibly augmented with a regularizing term), which is independent of the distribution of queries that are posed. This paper takes the "performance criteria" seriously, and considers the challenge of computing the BN whose performance  read "accuracy over the distribution of queries"  is optimal. We show that many aspects of this learning task are more difficult than the corresponding subtasks in the standard model. To appear in Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI97), Providence, RI, August 1997. 1 INTRODUCTION Many tasks require answering questions; this model applies, for example, to both expert systems th...
Learning Probabilistic Networks
 THE KNOWLEDGE ENGINEERING REVIEW
, 1998
"... A probabilistic network is a graphical model that encodes probabilistic relationships between variables of interest. Such a model records qualitative influences between variables in addition to the numerical parameters of the probability distribution. As such it provides an ideal form for combini ..."
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Cited by 44 (2 self)
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A probabilistic network is a graphical model that encodes probabilistic relationships between variables of interest. Such a model records qualitative influences between variables in addition to the numerical parameters of the probability distribution. As such it provides an ideal form for combining prior knowledge, which might be limited solely to experience of the influences between some of the variables of interest, and data. In this paper, we first show how data can be used to revise initial estimates of the parameters of a model. We then progress to showing how the structure of the model can be revised as data is obtained. Techniques for learning with incomplete data are also covered.
Irrelevance and parameter learning in Bayesian networks
 Artificial Intelligence
, 1996
"... Bayesian network classifiers have been widely used for classification problems. Given a fixed Bayesian network structure, parameters learning can take two different approaches: generative and discriminative learning. While generative parameter learning is more efficient, discriminative parameter lea ..."
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Cited by 39 (2 self)
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Bayesian network classifiers have been widely used for classification problems. Given a fixed Bayesian network structure, parameters learning can take two different approaches: generative and discriminative learning. While generative parameter learning is more efficient, discriminative parameter learning is more effective. In this paper, we propose a simple, efficient, and effective discriminative parameter learning method, called Discriminative Frequency Estimate (DFE), which learns parameters by discriminatively computing frequencies from data. Empirical studies show that the DFE algorithm integrates the advantages of both generative and discriminative learning: it performs as well as the stateoftheart discriminative parameter learning method ELR in accuracy, but is significantly more efficient. 1.
Locally Bayesian Learning with Applications to Retrospective Revaluation and Highlighting
 Psychological Review
, 2006
"... A scheme is described for locally Bayesian parameter updating in models structured as successions of component functions. The essential idea is to backpropagate the target data to interior modules, such that an interior component’s target is the input to the next component that maximizes the probab ..."
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Cited by 36 (7 self)
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A scheme is described for locally Bayesian parameter updating in models structured as successions of component functions. The essential idea is to backpropagate the target data to interior modules, such that an interior component’s target is the input to the next component that maximizes the probability of the next component’s target. Each layer then does locally Bayesian learning. The approach assumes online trialbytrial learning. The resulting parameter updating is not globally Bayesian but can better capture human behavior. The approach is implemented for an associative learning model that first maps inputs to attentionally filtered inputs and then maps attentionally filtered inputs to outputs. The Bayesian updating allows the associative model to exhibit retrospective revaluation effects such as backward blocking and unovershadowing, which have been challenging for associative learning models. The backpropagation of target values to attention allows the model to show trialorder effects, including highlighting and differences in magnitude of forward and backward blocking, which have been challenging for Bayesian learning models.
When do Numbers Really Matter?
 Journal of Artificial Intelligence Research
, 2002
"... Common wisdom has it that small distinctions in the probabilities (parameters) quantifying a belief network do not matter much for the results of probabilistic queries. Yet, one can develop realistic scenarios under which small variations in network parameters can lead to significant changes in c ..."
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Cited by 34 (7 self)
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Common wisdom has it that small distinctions in the probabilities (parameters) quantifying a belief network do not matter much for the results of probabilistic queries. Yet, one can develop realistic scenarios under which small variations in network parameters can lead to significant changes in computed queries. A pending theoretical question is then to analytically characterize parameter changes that do or do not matter. In this paper, we study the sensitivity of probabilistic queries to changes in network parameters and prove some tight bounds on the impact that such parameters can have on queries. Our analytic results pinpoint some interesting situations under which parameter changes do or do not matter. These results are important for knowledge engineers as they help them identify influential network parameters. They also help explain some of the previous experimental results and observations with regards to network robustness against parameter changes.