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48
A Bayesian method for the induction of probabilistic networks from data
 MACHINE LEARNING
, 1992
"... 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 probabili ..."
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Cited by 1369 (32 self)
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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.
On the Hardness of Approximate Reasoning
, 1996
"... Many AI problems, when formalized, reduce to evaluating the probability that a propositional expression is true. In this paper we show that this problem is computationally intractable even in surprisingly restricted cases and even if we settle for an approximation to this probability. We consider va ..."
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Cited by 284 (13 self)
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Many AI problems, when formalized, reduce to evaluating the probability that a propositional expression is true. In this paper we show that this problem is computationally intractable even in surprisingly restricted cases and even if we settle for an approximation to this probability. We consider various methods used in approximate reasoning such as computing degree of belief and Bayesian belief networks, as well as reasoning techniques such as constraint satisfaction and knowledge compilation, that use approximation to avoid computational difficulties, and reduce them to modelcounting problems over a propositional domain. We prove that counting satisfying assignments of propositional languages is intractable even for Horn and monotone formulae, and even when the size of clauses and number of occurrences of the variables are extremely limited. This should be contrasted with the case of deductive reasoning, where Horn theories and theories with binary clauses are distinguished by the e...
Rationality and its Roles in Reasoning
 Computational Intelligence
, 1994
"... The economic theory of rationality promises to equal mathematical logic in its importance for the mechanization of reasoning. We survey the growing literature on how the basic notions of probability, utility, and rational choice, coupled with practical limitations on information and resources, in ..."
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Cited by 111 (5 self)
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The economic theory of rationality promises to equal mathematical logic in its importance for the mechanization of reasoning. We survey the growing literature on how the basic notions of probability, utility, and rational choice, coupled with practical limitations on information and resources, influence the design and analysis of reasoning and representation systems. 1 Introduction People make judgments of rationality all the time, usually in criticizing someone else's thoughts or deeds as irrational, or in defending their own as rational. Artificial intelligence researchers construct systems and theories to perform or describe rational thought and action, criticizing and defending these systems and theories in terms similar to but more formal than those of the man or woman on the street. Judgments of human rationality commonly involve several different conceptions of rationality, including a logical conception used to judge thoughts, and an economic one used to judge actions or...
Decision Theory in Expert Systems and Artificial Intelligence
 International Journal of Approximate Reasoning
, 1988
"... Despite their different perspectives, artificial intelligence (AI) and the disciplines of decision science have common roots and strive for similar goals. This paper surveys the potential for addressing problems in representation, inference, knowledge engineering, and explanation within the decision ..."
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Cited by 100 (19 self)
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Despite their different perspectives, artificial intelligence (AI) and the disciplines of decision science have common roots and strive for similar goals. This paper surveys the potential for addressing problems in representation, inference, knowledge engineering, and explanation within the decisiontheoretic framework. Recent analyses of the restrictions of several traditional AI reasoning techniques, coupled with the development of more tractable and expressive decisiontheoretic representation and inference strategies, have stimulated renewed interest in decision theory and decision analysis. We describe early experience with simple probabilistic schemes for automated reasoning, review the dominant expertsystem paradigm, and survey some recent research at the crossroads of AI and decision science. In particular, we present the belief network and influence diagram representations. Finally, we discuss issues that have not been studied in detail within the expertsystems sett...
Recognizing Planned, Multiperson Action
 Computer Vision and Image Understanding
, 2001
"... This paper demonstrates how highly structured, multiperson action can be recognized from noisy perceptual data using visually grounded goalbased primitives and loworder temporal relationships that are integrated in a probabilistic framework. The representation, which is motivated by work in mo ..."
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Cited by 72 (2 self)
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This paper demonstrates how highly structured, multiperson action can be recognized from noisy perceptual data using visually grounded goalbased primitives and loworder temporal relationships that are integrated in a probabilistic framework. The representation, which is motivated by work in modelbased object recognition and probabilistic plan recognition, makes four principal assumptions: (1) the goals of individual agents are natural atomic representational units for specifying the temporal relationships between agents engaged in group activities, (2) a highlevel description of temporal structure of the action using a small set of loworder temporal and logical constraints is adequate for representing the relationships between the agent goals for highly structured, multiagent action recognition, (3) Bayesian networks provide a suitable mechanism for integrating multiple sources of uncertain visual perceptual feature evidence, and (4) an automatically generated Bayesian
Beyond the Repertory Grid: New Approaches to Constructivist Knowledge Acquisition Tool Development
 In K.M. Ford & J.M. Bradshaw (Ed.), Knowledge Acquisition as Modeling
, 1993
"... Personal construct theory provides both a plausible theoretical foundation for knowledge acquisition and a practical approach to modeling. Yet, only a fraction of the ideas latent in this theory have been tapped. Recently, several researchers have been taking a second look at the theory, to discover ..."
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Cited by 26 (7 self)
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Personal construct theory provides both a plausible theoretical foundation for knowledge acquisition and a practical approach to modeling. Yet, only a fraction of the ideas latent in this theory have been tapped. Recently, several researchers have been taking a second look at the theory, to discover new ways that it can shed light on the foundations and practice of knowledge acquisition. These efforts have led to the development of three “secondgeneration ” constructivist knowledge acquisition systems: DDUCKS, ICONKAT, and KSSn/KRS. These tools extend repertory grid techniques in various ways and integrate them with tools springing from complementary perspectives. New understandings of relationships between personal construct theory, assimilation theory, logic, semantic networks, and decision analysis have formed the underpinnings of these systems. Theoretical progress has fostered practical development in system architecture, analysis and induction techniques, and group use of knowledge acquisition tools.
Decision Support Systems
, 2000
"... this article focuses primarily on the core of a DSS, the part that directly supports modeling decision problems and identi es best alternatives. We will briey discuss the characteristics of decision problems and how decision making can be supported by computer programs. We then cover various compon ..."
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Cited by 17 (0 self)
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this article focuses primarily on the core of a DSS, the part that directly supports modeling decision problems and identi es best alternatives. We will briey discuss the characteristics of decision problems and how decision making can be supported by computer programs. We then cover various components of DSSs and the role that they play in decision support. We will also introduce an emergent class of normative systems (i.e., DSSs based on sound theoretical principles), and in particular, decision analytic DSSs. Finally, we will review issues related to user interfaces to DSSs and stress the importance of user interfaces to the ultimate quality of decisions aided by computer programs
Perspectives on Representation and Analysis of Negotiation,” Group Decision and Negotiation 4/5/6
, 1996
"... Abstract. The rapid expansion of Decision and Negotiation Support Systems has been built mainly on decision theoretic approaches. This has resulted in the decision maker being viewed through the lens of the problem. In this paper the focus is on the decision maker's view of the problem. Three l ..."
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Cited by 9 (1 self)
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Abstract. The rapid expansion of Decision and Negotiation Support Systems has been built mainly on decision theoretic approaches. This has resulted in the decision maker being viewed through the lens of the problem. In this paper the focus is on the decision maker's view of the problem. Three levels of problem articulation are described. Special emphasis is placed on the needs level and the implications it carries for the cognitive and instrumental levels. The three levels of articulation, the organizational model of making decision in social settings and the three basic approaches to decision making form the basis for computer support focused on understanding and change rather than preferences and outcomes. We argue that in the dynamic, interactive context characteristic of negotiations, a cognitive support system based on restructurable modelling provides a richer basis for support.
Problem formulation as the reduction of a decision model
 in Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence
, 1990
"... In this paper, we extend the QMRDT probabilistic model for the domain of internal medicine to include decisions about treatments. In addition, we describe how we can use the comprehensive decision model to construct a simpler decision model for a specific patient. In so doing, we transform the task ..."
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Cited by 8 (0 self)
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In this paper, we extend the QMRDT probabilistic model for the domain of internal medicine to include decisions about treatments. In addition, we describe how we can use the comprehensive decision model to construct a simpler decision model for a specific patient. In so doing, we transform the task of problem formulation to that of narrowing of a larger problem. 1
Decision Analytic Networks in Artificial Intelligence
, 1995
"... Researchers in artificial intelligence and decision analysis share a concern with the construction of formal models of human knowledge and expertise. Historically, however, their approaches to these problems have diverged. Members of these two communities have recently discovered common ground: a fa ..."
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Cited by 8 (0 self)
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Researchers in artificial intelligence and decision analysis share a concern with the construction of formal models of human knowledge and expertise. Historically, however, their approaches to these problems have diverged. Members of these two communities have recently discovered common ground: a family of graphical models of decision theory known as influence diagrams or as belief networks. These models are equally attractive to theoreticians, decision modelers, and designers of knowledgebased systems. From a theoretical perspective, they combine graph theory, probability theory and decision theory. From an implementation perspective, they lead to powerful automated systems. Although many practicing decision analysts have already adopted influence diagrams as modeling and structuring tools, they may remain unaware of the theoretical work that has emerged from the artificial intelligence community. This paper surveys the first decade or so of this work. Investment Technology Group, ...