Results 1  10
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42
Constraint propagation algorithms for temporal reasoning
 Readings in Qualitative Reasoning about Physical Systems
, 1986
"... Abstract: This paper revises and expands upon a paper presented by two of the present authors at AAAI 1986 [Vilain & Kautz 1986]. As with the original, this revised document considers computational aspects of intervalbased and pointbased temporal representations. Computing the consequences of tempo ..."
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Cited by 371 (4 self)
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Abstract: This paper revises and expands upon a paper presented by two of the present authors at AAAI 1986 [Vilain & Kautz 1986]. As with the original, this revised document considers computational aspects of intervalbased and pointbased temporal representations. Computing the consequences of temporal assertions is shown to be computationally intractable in the intervalbased representation, but not in the pointbased one. However, a fragment of the interval language can be expressed using the point language and benefits from the tractability of the latter. The present paper departs from the original primarily in correcting claims made there about the point algebra, and in presenting some closely related results of van Beek [1989]. The representation of time has been a recurring concern of Artificial Intelligence researchers. Many representation schemes have been proposed for temporal reasoning; of these, one of the most attractive is James Allen's algebra of temporal intervals [Allen 1983]. This representation scheme is particularly appealing for its simplicity and for its ease of implementation with constraint propagation algorithms. Reasoners based on
Efficient Computation of Equilibria for Extensive Twoperson Games
, 1996
"... The Nash equilibria of a twoperson, nonzerosum game are the solutions of a certain linear complementarity problem (LCP). In order to use this for solving a game in extensive form, the game must first be converted to a strategic description such as the normal form. The classical normal form, howev ..."
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Cited by 87 (7 self)
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The Nash equilibria of a twoperson, nonzerosum game are the solutions of a certain linear complementarity problem (LCP). In order to use this for solving a game in extensive form, the game must first be converted to a strategic description such as the normal form. The classical normal form, however, is often exponentially large in the size of the game tree. If the game has perfect recall, a linearsized strategic description is the sequence form. For the resulting small LCP, we show that an equilibrium is found efficiently by Lemke’s algorithm, a generalization of the Lemke–Howson method.
Solving RealWorld Linear Programs: A Decade and More of Progress
 Operations Research
, 2002
"... This paper is an invited contribution to the 50th anniversary issue of the journal Operations Research, published by the Institute of Operations Research and Management Science (INFORMS). It describes one persons perspective on the development of computational tools for linear programming. The pape ..."
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Cited by 59 (1 self)
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This paper is an invited contribution to the 50th anniversary issue of the journal Operations Research, published by the Institute of Operations Research and Management Science (INFORMS). It describes one persons perspective on the development of computational tools for linear programming. The paper begins with a short, personal history, followed by historical remarks covering the some 40 years of linearprogramming developments that predate my own involvement in this subject. It concludes with a more detailed look at the evolution of computational linear programming since 1987. 2
Efficient Computation of Behavior Strategies
 GAMES AND ECONOMIC BEHAVIOR
, 1996
"... We propose the sequence form as a new strategic description for an extensive game with perfect recall. It is similar to the normal form but has linear instead of exponential complexity, and allows a direct representation and efficient computation of behavior strategies. Pure strategies and their mix ..."
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Cited by 46 (8 self)
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We propose the sequence form as a new strategic description for an extensive game with perfect recall. It is similar to the normal form but has linear instead of exponential complexity, and allows a direct representation and efficient computation of behavior strategies. Pure strategies and their mixed strategy probabilities are replaced by sequences of consecutive choices and their realization probabilities. A zerosum game is solved by a corresponding linear program that has linear size in the size of the game tree. General twoperson games are studied in the paper by Koller, Megiddo, and von Stengel in this journal issue.
Minimal and Maximal Exposure Path Algorithms for Wireless Embedded Sensor Networks
 IN PROC. OF SENSYS
, 2003
"... Sensor networks not only have the potential to change the way we use, interact with, and view computers, but also the way we use, interact with, and view the world around us. In order to maximize the effectiveness of sensor networks, one has to identify, examine, understand, and provide solutions fo ..."
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Cited by 45 (3 self)
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Sensor networks not only have the potential to change the way we use, interact with, and view computers, but also the way we use, interact with, and view the world around us. In order to maximize the effectiveness of sensor networks, one has to identify, examine, understand, and provide solutions for the fundamental problems related to wireless embedded sensor networks. We believe that one of such problems is to determine how well the sensor network monitors the instrumented area. These problems are usually classified as coverage problems. There already exist several methods that have been proposed to evaluate a sensor network's coverage. We start from
Efficient utility functions for ceteris paribus preferences
 In Proceedings of the Eighteenth National Conference on Artificial Intelligence
, 2002
"... Although ceteris paribus preference statements concisely represent one natural class of preferences over outcomes or goals, many applications of such preferences require numeric utility function representations to achieve computational efficiency. We provide algorithms, complete for finite universes ..."
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Cited by 42 (3 self)
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Although ceteris paribus preference statements concisely represent one natural class of preferences over outcomes or goals, many applications of such preferences require numeric utility function representations to achieve computational efficiency. We provide algorithms, complete for finite universes of binary features, for converting a set of qualitative ceteris paribus preferences into quantitative utility functions.
Harmonic grammar with linear programming: From linear . . .
, 2009
"... Harmonic Grammar (HG) is a model of linguistic constraint interaction in which wellformedness is calculated as the sum of weighted constraint violations. We show how linear programming algorithms can be used to determine whether there is a weighting for a set of constraints that fits a set of ling ..."
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Cited by 29 (8 self)
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Harmonic Grammar (HG) is a model of linguistic constraint interaction in which wellformedness is calculated as the sum of weighted constraint violations. We show how linear programming algorithms can be used to determine whether there is a weighting for a set of constraints that fits a set of linguistic data. The associated software package OTHelp provides a practical tool for studying large and complex linguistic systems in the HG framework and comparing the results with those of OT. We first describe the translation from Harmonic Grammars to systems solvable by linear programming algorithms. We then develop an HG analysis of ATR harmony in Lango that is, we argue, superior to the existing OT and rulebased treatments. We further highlight the usefulness of OTHelp, and the analytic power of HG, with a set of studies of the predictions HG makes for phonological typology.
Learning Subjective Functions with Large Margins
 Stanford University
, 2000
"... In many optimization and decision problems the objective function can be expressed as a linear combination of competing criteria, the weights of which specify the relative importance of the criteria for the user. We consider the problem of learning such a "subjective" function from preference ..."
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Cited by 24 (1 self)
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In many optimization and decision problems the objective function can be expressed as a linear combination of competing criteria, the weights of which specify the relative importance of the criteria for the user. We consider the problem of learning such a "subjective" function from preference judgments collected from traces of user interactions. We propose a new algorithm for that task based on the theory of Support Vector Machines. One advantage of the algorithm is that prior knowledge about the domain can easily be included to constrain the solution. We demonstrate the algorithm in a route recommendation system that adapts to the driver's route preferences. We present experimental results on real users that show that the algorithm performs well in practice. 1.
Use of dynamic trees in a network simplex algorithm for the maximum flow problem
, 1991
"... Goldfarb and Hao (1990) have proposed a pivot rule for the primal network simplex algorithm that will solve a maximum flow problem on an nvertex, marc network in at most nm pivots and O(n²m) time. In this paper we describe how to extend the dynamic tree data structure of Sleator and Tarjan (1983, ..."
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Cited by 20 (5 self)
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Goldfarb and Hao (1990) have proposed a pivot rule for the primal network simplex algorithm that will solve a maximum flow problem on an nvertex, marc network in at most nm pivots and O(n²m) time. In this paper we describe how to extend the dynamic tree data structure of Sleator and Tarjan (1983, 1985) to reduce the running time of this algorithm to O(nm log n). This bound is less than a logarithmic factor larger than those of the fastest known algorithms for the problem. Our extension of dynamic trees is interesting in its own right and may well have additional applications.