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66
Saddlepoint Approximations
, 1995
"... representation of optimal strategies from influence diagrams ..."
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Cited by 86 (1 self)
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representation of optimal strategies from influence diagrams
Representing and Solving Decision Problems with Limited Information
 Management Science
, 2001
"... We introduce the notion of LImited Memory Influence Diagram (LIMID) to describe multistage decision problems where the traditional assumption of no forgetting is relaxed. This can be relevant in situations with multiple decision makers or when decisions must be prescribed under memory constraints, ..."
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Cited by 48 (3 self)
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We introduce the notion of LImited Memory Influence Diagram (LIMID) to describe multistage decision problems where the traditional assumption of no forgetting is relaxed. This can be relevant in situations with multiple decision makers or when decisions must be prescribed under memory constraints, such as e.g. in partially observed Markov decision processes (POMDPs). We give an algorithm for improving any given strategy by local computation of single policy updates and investigate conditions for the resulting strategy to be optimal. Key words: Local computation; message passing; optimal strategies; partially observed Markov decision process, single policy updating. To appear in Management Science. y Department of Mathematical Sciences, Aalborg University, Fredrik Bajers Vej 7G, DK9220 Aalborg, Denmark. 1 1
Robot Trajectory Optimization using Approximate Inference
"... The general stochastic optimal control (SOC) problem in robotics scenarios is often too complex to be solved exactly and in near real time. A classical approximate solution is to first compute an optimal (deterministic) trajectory and then solve a local linearquadraticgaussian (LQG) perturbation m ..."
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Cited by 41 (14 self)
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The general stochastic optimal control (SOC) problem in robotics scenarios is often too complex to be solved exactly and in near real time. A classical approximate solution is to first compute an optimal (deterministic) trajectory and then solve a local linearquadraticgaussian (LQG) perturbation model to handle the system stochasticity. We present a new algorithm for this approach which improves upon previous algorithms like iLQG. We consider a probabilistic model for which the maximum likelihood (ML) trajectory coincides with the optimal trajectory and which, in the LQG case, reproduces the classical SOC solution. The algorithm then utilizes approximate inference methods (similar to expectation propagation) that efficiently generalize to nonLQG systems. We demonstrate the algorithm on a simulated 39DoF humanoid robot. 1.
A Computational Theory of Decision Networks
 International Journal of Approximate Reasoning
, 1994
"... This paper is about how to represent and solve decision problems in Bayesian decision theory (e.g. [6]). A general representation named decision networks is proposed based on influence diagrams [10]. This new representation incorporates the idea, from Markov decision process (e.g. [5]), that a decis ..."
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Cited by 33 (2 self)
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This paper is about how to represent and solve decision problems in Bayesian decision theory (e.g. [6]). A general representation named decision networks is proposed based on influence diagrams [10]. This new representation incorporates the idea, from Markov decision process (e.g. [5]), that a decision may be conditionally independent of certain pieces of available information. It also allows multiple cooperative agents and facilitates the exploitation of separability in the utility function. Decision networks inherit the advantages of both influence diagrams and Markov decision processes, which makes them a better representation framework for decision analysis, planning under uncertainty, medical diagnosis and treatment.
Probabilistic Inference in Influence Diagrams
 Computational Intelligence
, 1998
"... This paper is about reducing influence diagram (ID) evaluation into Bayesian network (BN) inference problems. Such reduction is interesting because it enables one to readily use one's favorite BN inference algorithm to efficiently evaluate IDs. Two such reduction methods have been proposed pre ..."
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Cited by 32 (0 self)
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This paper is about reducing influence diagram (ID) evaluation into Bayesian network (BN) inference problems. Such reduction is interesting because it enables one to readily use one's favorite BN inference algorithm to efficiently evaluate IDs. Two such reduction methods have been proposed previously (Cooper 1988, Shachter and Peot 1992). This paper proposes a new method. The BN inference problems induced by the mew method are much easier to solve than those induced by the two previous methods.
Inference in Bayesian Networks
, 1999
"... A Bayesian network is a compact, expressive representation of uncertain relationships among parameters in a domain. In this article, I introduce basic methods for computing with Bayesian networks, starting with the simple idea of summing the probabilities of events of interest. The article introduce ..."
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Cited by 31 (0 self)
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A Bayesian network is a compact, expressive representation of uncertain relationships among parameters in a domain. In this article, I introduce basic methods for computing with Bayesian networks, starting with the simple idea of summing the probabilities of events of interest. The article introduces major current methods for exact computation, briefly surveys approximation methods, and closes with a brief discussion of open issues.
Myopic Value of Information in Influence Diagrams
 IN UAI
, 1997
"... We present a method for calculation of myopic value of information in influence diagrams (Howard & Matheson, 1981) based on the strong junction tree framework (Jensen et al., 1994) . An influence diagram specifies a certain order of observations and decisions through its structure. This order is re ..."
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Cited by 30 (3 self)
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We present a method for calculation of myopic value of information in influence diagrams (Howard & Matheson, 1981) based on the strong junction tree framework (Jensen et al., 1994) . An influence diagram specifies a certain order of observations and decisions through its structure. This order is reflected in the corresponding junction trees by the order in which the nodes are marginalized. This order of marginalization can be changed by table expansion and use of control structures, and this facilitates for calculating the expected value of information for different information scenarios within the same junction tree. In effect, a strong junction tree with expanded tables may be used for calculating the value of information between several scenarios with different observationdecision order. We compare our method to other methods for calculating the value of information in influence diagrams.
DT Tutor: A DecisionTheoretic, Dynamic Approach for Optimal Selection of Tutorial Actions
 Proceedings of Intelligent Tutoring Systems, 5th International Conference, ITS2000
, 2000
"... DT Tutor uses a decisiontheoretic approach to select tutorial actions for coached problem solving that are optimal given the tutor's beliefs and objectives. ..."
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Cited by 24 (3 self)
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DT Tutor uses a decisiontheoretic approach to select tutorial actions for coached problem solving that are optimal given the tutor's beliefs and objectives.
Looking ahead to select tutorial actions: a decisiontheoretic approach
 INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE IN EDUCATION
, 2004
"... We propose and evaluate a decisiontheoretic approach for selecting tutorial actions by looking ahead to anticipate their effects on the student and other aspects of the tutorial state. The approach uses a dynamic decision network to consider the tutor's uncertain beliefs and objectives in adapting ..."
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Cited by 21 (4 self)
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We propose and evaluate a decisiontheoretic approach for selecting tutorial actions by looking ahead to anticipate their effects on the student and other aspects of the tutorial state. The approach uses a dynamic decision network to consider the tutor's uncertain beliefs and objectives in adapting to and managing the changing tutorial state. Prototype action selection engines for diverse domains calculus and elementary reading illustrate the approach. These applications employ a rich model of the tutorial state, including attributes such as the student's knowledge, focus of attention, affective state, and next action(s), along with task progress and the discourse state. For this study, neither of our action selection engines had been integrated into a complete ITS, so we used simulated students to evaluate their capabilities to select rational tutorial actions that emulate the behaviors of human tutors. We also evaluated their capability to select tutorial actions quickly enough for realworld tutoring applications.
Lazy Evaluation of Symmetric Bayesian Decision Problems
 In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence
, 1999
"... Solving symmetric Bayesian decision problems is a computationally intensive task to perform regardless of the algorithm used. In this paper we propose a method for improving the efficiency of algorithms for solving Bayesian decision problems. The method is based on the principle of lazy evaluation  ..."
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Cited by 18 (11 self)
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Solving symmetric Bayesian decision problems is a computationally intensive task to perform regardless of the algorithm used. In this paper we propose a method for improving the efficiency of algorithms for solving Bayesian decision problems. The method is based on the principle of lazy evaluation  a principle recently shown to improve the efficiency of inference in Bayesian networks. The basic idea is to maintain decompositions of potentials and to postpone computations for as long as possible. The efficiency improvements obtained with the lazy evaluation based method is emphasized through examples. Finally, the lazy evaluation based method is compared with the Hugin and valuationbased systems architectures for solving symmetric Bayesian decision problems. 1 INTRODUCTION Bayesian decision theory provides a solid foundation for assessing and thinking about actions under uncertainty. A symmetric Bayesian decision problem is specified with a set of decision variables, a set of chance...