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124
A Bayesian method for the induction of probabilistic networks from data
 Machine Learning
, 1992
"... 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 computerassisted hypothesis testing, automated scientific discovery, and automated construction of ..."
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Cited by 1079 (26 self)
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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 computerassisted 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.
Bayesian Networks Without Tears
 AI MAGAZINE
, 1991
"... I give an introduction to Bayesian networks for AI researchers with a limited grounding in probability theory. Over the last few years, this method of reasoning using probabilities has become popular within the AI probability and uncertainty community. Indeed, it is probably fair to say that Bayesia ..."
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Cited by 235 (2 self)
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I give an introduction to Bayesian networks for AI researchers with a limited grounding in probability theory. Over the last few years, this method of reasoning using probabilities has become popular within the AI probability and uncertainty community. Indeed, it is probably fair to say that Bayesian networks are to a large segment of the AIuncertainty community what resolution theorem proving is to the AIlogic community. Nevertheless, despite what seems to be their obvious importance, the ideas and techniques have not spread much beyond the research community responsible for them. This is probably because the ideas and techniques are not that easy to understand. I hope to rectify this situation by making Bayesian networks more accessible to the probabilistically unsophisticated.
A characterization of Markov equivalence classes for acyclic digraphs
, 1995
"... Undirected graphs and acyclic digraphs (ADGs), as well as their mutual extension to chain graphs, are widely used to describe dependencies among variables in multivariate distributions. In particular, the likelihood functions of ADG models admit convenient recursive factorizations that often allow e ..."
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Cited by 91 (7 self)
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Undirected graphs and acyclic digraphs (ADGs), as well as their mutual extension to chain graphs, are widely used to describe dependencies among variables in multivariate distributions. In particular, the likelihood functions of ADG models admit convenient recursive factorizations that often allow explicit maximum likelihood estimates and that are well suited to building Bayesian networks for expert systems. Whereas the undirected graph associated with a dependence model is uniquely determined, there may, however, be many ADGs that determine the same dependence ( = Markov) model. Thus, the family of all ADGs with a given set of vertices is naturally partitioned into Markovequivalence classes, each class being associated with a unique statistical model. Statistical procedures, such as model selection or model averaging, that fail to take into account these equivalence classes, may incur substantial computational or other inefficiencies. Here it is shown that each Markovequivalence class is uniquely determined by a single chain graph, the essential graph, that is itself simultaneously Markov equivalent to all ADGs in the equivalence class. Essential graphs are characterized, a polynomialtime algorithm for their construction is given, and their applications to model selection and other statistical
Multiply sectioned bayesian networks and junction forests for large knowledge based systems
 Computational Intelligence
, 1993
"... Abstract — We extend lazy propagation for inference in singleagent Bayesian networks to multiagent lazy inference in multiply sectioned Bayesian networks (MSBNs). Two methods are proposed using distinct runtime structures. We prove that the new methods are exact and efficient when domain structure ..."
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Cited by 79 (28 self)
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Abstract — We extend lazy propagation for inference in singleagent Bayesian networks to multiagent lazy inference in multiply sectioned Bayesian networks (MSBNs). Two methods are proposed using distinct runtime structures. We prove that the new methods are exact and efficient when domain structure is sparse. Both improve space and time complexity than the existing method, which allow multiagent probabilistic reasoning to be performed in much larger domains given the computational resource. Relative performance of the three methods are compared analytically and experimentally. I.
A Rigorous, Operational Formalization of Recursive Modeling
, 1995
"... We present a formalization of the Recursive Modeling Method, which we have previously, somewhat informally, proposed as a method that autonomous artificial agents can use for intelligent coordination and communication with other agents. Our formalism is closely related to models proposed in the area ..."
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Cited by 74 (15 self)
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We present a formalization of the Recursive Modeling Method, which we have previously, somewhat informally, proposed as a method that autonomous artificial agents can use for intelligent coordination and communication with other agents. Our formalism is closely related to models proposed in the area of game theory, but contains new elements that lead to a different solution concept. The advantage of our solution method is that always yields the optimal solution, which is the rational action of the agent in a multiagent environment, given the agent's state of knowledge and its preferences, and that it works in realistic cases when agents have only a finite amount of information about the agents they interact with. Introduction Since its initial conceptual development several years ago (Gmytrasiewicz, Durfee, & Wehe 1991a; 1991b), the Recursive Modeling Method (RMM) has provided a powerful decisiontheoretic underpinning for coordination and communication decisionmaking, including dec...
"Is This Document Relevant? ...Probably": A Survey of Probabilistic Models in Information Retrieval
, 2001
"... This article surveys probabilistic approaches to modeling information retrieval. The basic concepts of probabilistic approaches to information retrieval are outlined and the principles and assumptions upon which the approaches are based are presented. The various models proposed in the developmen ..."
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Cited by 63 (14 self)
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This article surveys probabilistic approaches to modeling information retrieval. The basic concepts of probabilistic approaches to information retrieval are outlined and the principles and assumptions upon which the approaches are based are presented. The various models proposed in the development of IR are described, classified, and compared using a common formalism. New approaches that constitute the basis of future research are described
Dynamic Belief Networks for Discrete Monitoring
 IEEE Transactions on Systems, Man, and Cybernetics
, 1994
"... We describe the development of a monitoring system which uses sensor observation data about discrete events to construct dynamically a probabilistic model of the world. This model is a Bayesian network incorporating temporal aspects, which we call a Dynamic Belief Network; it is used to reason under ..."
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Cited by 54 (7 self)
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We describe the development of a monitoring system which uses sensor observation data about discrete events to construct dynamically a probabilistic model of the world. This model is a Bayesian network incorporating temporal aspects, which we call a Dynamic Belief Network; it is used to reason under uncertainty about both the causes and consequences of the events being monitored. The basic dynamic construction of the network is datadriven. However the model construction process combines sensor data about events with externally provided information about agents' behaviour, and knowledge already contained within the model, to control the size and complexity of the network. This means that both the network structure within a time interval, and the amount of history and detail maintained, can vary over time. We illustrate the system with the example domain of monitoring robot vehicles and people in a restricted dynamic environment using lightbeam sensor data. In addition to presenting a ...
A decisiontheoretic approach to coordinating multiagent interactions
 In Proceedings of the Twelfth International Joint Conference on Artificial Intelligence
, 1991
"... We describe a decisiontheoretic method that an autonomous agent can use to model multiagent situations and behave rationally based on its model. Our approach, which we call the Recursive Modeling Method, explicitly accounts for the recursive nature of multiagent reasoning. Our method lets an agent ..."
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Cited by 54 (20 self)
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We describe a decisiontheoretic method that an autonomous agent can use to model multiagent situations and behave rationally based on its model. Our approach, which we call the Recursive Modeling Method, explicitly accounts for the recursive nature of multiagent reasoning. Our method lets an agent recursively model another agent's decisions based on probabilistic views of how that agent perceives the multiagent situation, which in turn are derived from hypothesizing how that other agent perceives the initial agent's possible decisions, and so on. Further, we show how the possibility of multiple interactions can affect the decisions of agents, allowing cooperative behavior to emerge as a rational choice of selfish agents that otherwise might behave uncooperatively.
Tailoring the Interaction with Users in Web Stores
 Interaction
, 2001
"... . We describe the user modeling and personalization techniques adopted in SETA, a prototype toolkit for the construction of adaptive Web stores which customize the interaction with users. The Web stores created using SETA suggest the items best fitting the customers' needs and adapt the layout and t ..."
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Cited by 43 (16 self)
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. We describe the user modeling and personalization techniques adopted in SETA, a prototype toolkit for the construction of adaptive Web stores which customize the interaction with users. The Web stores created using SETA suggest the items best fitting the customers' needs and adapt the layout and the description of the store catalog to their preferences and expertise. SETA uses stereotypical information to handle the user models and applies personalization rules to dynamically generate the hypertextual pages presenting products. The system adapts the graphical aspect, length and terminology used in the descriptions to parameters like the user's receptivity, expertise and interests. Moreover, it maintains a model associated with each person the goods are selected for; in this way, multiple criteria can be applied for tailoring the selection of items to the preferences of their beneficiaries. Keywords: user modeling, personalized information presentation, customization of Web stores, ...
Dynamic construction of belief networks
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1990
"... AbstractWe describe a method for incrementally constructing belief networks, which are directed acyclic graph representations for probability distributions. We have developed a networkconstruction language (FRAIW), which is similar to a fonvardchaining language using data dependencies but has add ..."
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Cited by 42 (1 self)
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AbstractWe describe a method for incrementally constructing belief networks, which are directed acyclic graph representations for probability distributions. We have developed a networkconstruction language (FRAIW), which is similar to a fonvardchaining language using data dependencies but has additional features for specifying distributions. A particularly important feature of this language is that it allows the user to conveniently specify conditional probability matrices using stereotyped models of intercausal interaction. Using FRAIW, one can define parameterized classes of probabilistic models. These parameterized models make it possible to apply probabilistic reasoning to problems for which it is impractical to have a single large, static model.