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Fundamental Concepts of Qualitative Probabilistic Networks
 ARTIFICIAL INTELLIGENCE
, 1990
"... Graphical representations for probabilistic relationships have recently received considerable attention in A1. Qualitative probabilistic networks abstract from the usual numeric representations by encoding only qualitative relationships, which are inequality constraints on the joint probability dist ..."
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Cited by 119 (6 self)
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Graphical representations for probabilistic relationships have recently received considerable attention in A1. Qualitative probabilistic networks abstract from the usual numeric representations by encoding only qualitative relationships, which are inequality constraints on the joint probability distribution over the variables. Although these constraints are insufficient to determine probabilities uniquely, they are designed to justify the deduction of a class of relative likelihood conclusions that imply useful decisionmaking properties. Two types of qualitative relationship are defined, each a probabilistic form of monotonicity constraint over a group of variables. Qualitative influences describe the direction of the relationship between two variables. Qualitative synergies describe interactions among influences. The probabilistic definitions chosen justify sound and efficient inference procedures based on graphical manipulations of the network. These procedures answer queries about qualitative relationships among variables separated in the network and determine structural properties of optimal assignments to decision variables.
Efficient DecisionTheoretic Planning: Techniques and Empirical Analysis
 In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence
, 1995
"... This paper discusses techniques for performing efficient decisiontheoretic planning. We give an overview of the drips decisiontheoretic refinement planning system, which uses abstraction to efficiently identify optimal plans. We present techniques for automatically generating search control informa ..."
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Cited by 48 (10 self)
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This paper discusses techniques for performing efficient decisiontheoretic planning. We give an overview of the drips decisiontheoretic refinement planning system, which uses abstraction to efficiently identify optimal plans. We present techniques for automatically generating search control information, which can significantly improve the planner's performance. We evaluate the efficiency of drips both with and without the search control rules on a complex medical planning problem and compare its performance to that of a branchandbound decision tree algorithm. 1 Introduction In the framework of decisiontheoretic planning, uncertainty in the state of the world and in the effects of actions are represented with probabilities; and the planner 's goals, as well as tradeoffs among them, are represented with a utility function over outcomes. Given this representation, the objective is to find an optimal or near optimal plan. Finding the optimal plan requires comparing the expected utilit...
Path Planning under TimeDependent Uncertainty
 In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence
, 1995
"... Standard algorithms for finding the shortest path in a graph require that the cost of a path be additive in edge costs, and typically assume that costs are deterministic. We consider the problem of uncertain edge costs, with potential probabilistic dependencies among the costs. Although these depend ..."
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Cited by 30 (3 self)
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Standard algorithms for finding the shortest path in a graph require that the cost of a path be additive in edge costs, and typically assume that costs are deterministic. We consider the problem of uncertain edge costs, with potential probabilistic dependencies among the costs. Although these dependencies violate the standard dynamicprogramming decomposition, we identify a weaker stochastic consistency condition that justifies a generalized dynamicprogramming approach based on stochastic dominance. We present a revised pathplanning algorithm and prove that it produces optimal paths under timedependent uncertain costs. We illustrate the algorithm by applying it to a model of stochastic bus networks, and present sample performance results comparing it to some alternatives. For the case where all or some of the uncertainty is resolved during path traversal, we extend the algorithm to produce optimal policies. This report is based on a paper presented at the Eleventh Conference on Unc...
Probabilistic Reasoning in Decision Support Systems: From Computation to Common Sense
, 1993
"... Most areas of engineering, science, and management use important tools based on probabilistic methods. The common thread of the entire spectrum of these tools is aiding in decision making under uncertainty: the choice of an interpretation of reality or the choice of a course of action. Although the ..."
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Cited by 26 (14 self)
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Most areas of engineering, science, and management use important tools based on probabilistic methods. The common thread of the entire spectrum of these tools is aiding in decision making under uncertainty: the choice of an interpretation of reality or the choice of a course of action. Although the importance of dealing with uncertainty in decision making is widely acknowledged, dissemination of probabilistic and decisiontheoretic methods in Artificial Intelligence has been surprisingly slow. Opponents of probability theory have pointed out three major obstacles to applying it in computerized decision aids: (1) the counterintuitiveness of probabilistic inference, which makes it hard for system builders, experts, and users to translate knowledge into probabilistic form, create knowledge bases, and to interpret results; (2) the quantitative character of probability theory, which implies collection or assessment of vast quantities of numbers and, since these are not always readily available, raises questions about their quality; and (3) closely related to its quantitative character, the computational complexity of probabilistic inference. Its proponents, on the other hand, point
Frontiers of stochastically nondominated portfolios
 Econometrica
, 2003
"... Abstract. We consider the problem of constructing a portfolio of finitely many assets whose returns are described by a discrete joint distribution. We propose mean–risk models which are solvable by linear programming and generate portfolios whose returns are nondominated in the sense of secondorder ..."
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Cited by 15 (3 self)
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Abstract. We consider the problem of constructing a portfolio of finitely many assets whose returns are described by a discrete joint distribution. We propose mean–risk models which are solvable by linear programming and generate portfolios whose returns are nondominated in the sense of secondorder stochastic dominance. Next, we develop a specialized parametric method for recovering the entire mean–risk efficient frontiers of these models and we illustrate its operation on a large data set involving thousands of assets and realizations. 1.
MetaLevel Control for DecisionTheoretic Planners
, 1996
"... MetaLevel Control Agents plan in order to improve their performance, but planning takes time and other resources that can degrade performance. To plan effectively, an agent needs to be able to create high quality plans efficiently. Artificial intelligence planning techniques provide methods for gen ..."
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Cited by 9 (1 self)
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MetaLevel Control Agents plan in order to improve their performance, but planning takes time and other resources that can degrade performance. To plan effectively, an agent needs to be able to create high quality plans efficiently. Artificial intelligence planning techniques provide methods for generating plans, whereas decision theory offers expected utility as a measure for assessing plan quality, taking the value of each outcome and its likelihood into account. The benefits of combining artificial intelligence planning techniques and decision theory have long been recognized. However, these benefits will remain unrealized if the resulting decisiontheoretic planners cannot generate plans with high expected utility in a timely fashion. In this dissertation, we address the metalevel control problem of allocating computation to make decisiontheoretic planning efficient and effective. For efficiency, decisiontheoretic planners iteratively approximate the complete solution to a decision problem: planners generate partially elaborated, abstract plans; only promising plans are further refined, and execution may begin before a plan with the highest expected
Using StochasticDominance Relationships for Bounding Travel Times in Stochastic Networks
, 1999
"... We consider stochastic networks' in which link travel times are dependent, discrete random variables. We present methods' for computing bounds' on path travel times using stochastic dominance relationships among link travel times, and discuss techniques for controlling tightness of the bounds'. We a ..."
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Cited by 7 (5 self)
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We consider stochastic networks' in which link travel times are dependent, discrete random variables. We present methods' for computing bounds' on path travel times using stochastic dominance relationships among link travel times, and discuss techniques for controlling tightness of the bounds'. We apply these methods' to shortestpath problems, show that the proposed algorithm can provide bounds' on the recommended path, and elaborate on extensions of the algorithm for demonstrating the anytime property.
Advances in Applying StochasticDominance Relationships to Bounding Probability Distributions in Bayesian Networks
"... Bounds of probability distributions are useful for many reasoning tasks, including resolving the qualitative ambiguities in qualitative probabilistic networks and searching the best path in stochastic transportation networks. This paper investigates a subclass of the statespace abstraction methods ..."
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Bounds of probability distributions are useful for many reasoning tasks, including resolving the qualitative ambiguities in qualitative probabilistic networks and searching the best path in stochastic transportation networks. This paper investigates a subclass of the statespace abstraction methods that are designed to approximately evaluate Bayesian networks. Taking advantage of particular stochasticdominance relationships among random variables, these special methods aggregate states of random variables to obtain bounds of probability distributions at much reduced computational costs, thereby achieving high responsiveness of the overall system.
Consistent Testing for . . . Subsampling Approach
, 2002
"... We study a very general setting, and propose a procedure for estimating the critical values of the extended KolmogorovSmirnov tests of First and Second Order Stochastic Dominance due to McFadden (1989) in the general kprospect case. We allow for the observations to be generally serially dependent ..."
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We study a very general setting, and propose a procedure for estimating the critical values of the extended KolmogorovSmirnov tests of First and Second Order Stochastic Dominance due to McFadden (1989) in the general kprospect case. We allow for the observations to be generally serially dependent and, for the first time, we can accommodate general dependence amongst the prospects which are to be ranked. Also, the prospects may be the residuals from certain conditional models, opening the way for conditional ranking. We also propose a test of Prospect Stochastic Dominance. Our method is based on subsampling and we show that the resulting tests are consistent.