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Testing Truthfulness on Discrete Domains
"... This work initiates the study of algorithms for the testing of monotonicity of economic mechanisms on discrete domains. Such testers are useful in the search for dominant strategy mechanisms, and more importantly for a verification by the participating agents. 1 ..."
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This work initiates the study of algorithms for the testing of monotonicity of economic mechanisms on discrete domains. Such testers are useful in the search for dominant strategy mechanisms, and more importantly for a verification by the participating agents. 1
Testing Truthfulness on Discrete Domains
"... This work initiates the study of algorithms for the testing of monotonicity of economic mechanisms on discrete domains. Such testers are useful in the search for dominant strategy mechanisms, and more importantly for a verification by the participating agents. 1 ..."
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This work initiates the study of algorithms for the testing of monotonicity of economic mechanisms on discrete domains. Such testers are useful in the search for dominant strategy mechanisms, and more importantly for a verification by the participating agents. 1
Characterizing Truthfulness In Discrete Domains
"... Algorithmic mechanism design [9; 10] focuses on the design of algorithms that aim to achieve global objectives in settings in which the “input ” is provided by selfinterested strategic players2. This necessitates the design of algorithms that are incentivecompatible (a.k.a. truthful 3) in the sens ..."
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Algorithmic mechanism design [9; 10] focuses on the design of algorithms that aim to achieve global objectives in settings in which the “input ” is provided by selfinterested strategic players2. This necessitates the design of algorithms that are incentivecompatible (a.k.a. truthful 3) in the sense that players are incentivized via payments to behave as instructed. The most natural approach to designing incentivecompatible algorithms is coming up with an algorithm and an explicit payment scheme that guarantees its incentivecompatibility. However, finding appropriate payments is often a difficult, settingspecific, task, which is mostly achievable for very simple types of algorithms. A more general approach is the following: Any algorithm that interacts with selfish players and then outputs an outcome, can be regarded as computing a function, called a socialchoice function, from the players ’ “input ” to some outcome space. Certain properties of socialchoice functions are known to imply their implementability, that is, the existence of a payment scheme that guarantees incentivecompatibility. Hence, instead of explicitly dealing with payments, the problem of
Mechanism Design Over Discrete Domains
"... Often, we wish to design incentivecompatible algorithms for settings in which the players ’ private information is drawn from discrete domains (e.g., integer values). Our main result is identifying discrete settings in which an algorithm can be made incentivecompatible iff the function it computes ..."
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Cited by 5 (1 self)
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Often, we wish to design incentivecompatible algorithms for settings in which the players ’ private information is drawn from discrete domains (e.g., integer values). Our main result is identifying discrete settings in which an algorithm can be made incentivecompatible iff the function
Discrete Domain Representation for Shape Conceptualization
, 2000
"... This paper presents a solution for discrete domain representations of 3D geometric models, and techniques for shape instance extraction from a distribution domain. The discrete domain representation captures modality, impreciseness and uncertainty. It facilitates both shape conceptualization and com ..."
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Cited by 6 (5 self)
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This paper presents a solution for discrete domain representations of 3D geometric models, and techniques for shape instance extraction from a distribution domain. The discrete domain representation captures modality, impreciseness and uncertainty. It facilitates both shape conceptualization
GOLOG: A Logic Programming Language for Dynamic Domains
, 1994
"... This paper proposes a new logic programming language called GOLOG whose interpreter automatically maintains an explicit representation of the dynamic world being modeled, on the basis of user supplied axioms about the preconditions and effects of actions and the initial state of the world. This allo ..."
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Cited by 628 (74 self)
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for applications in high level control of robots and industrial processes, intelligent software agents, discrete event simulation, etc. It is based on a formal theory of action specified in an extended version of the situation calculus. A prototype implementation in Prolog has been developed.
The Contourlet Transform: An Efficient Directional Multiresolution Image Representation
 IEEE TRANSACTIONS ON IMAGE PROCESSING
"... The limitations of commonly used separable extensions of onedimensional transforms, such as the Fourier and wavelet transforms, in capturing the geometry of image edges are well known. In this paper, we pursue a “true” twodimensional transform that can capture the intrinsic geometrical structure t ..."
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Cited by 513 (20 self)
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that is key in visual information. The main challenge in exploring geometry in images comes from the discrete nature of the data. Thus, unlike other approaches, such as curvelets, that first develop a transform in the continuous domain and then discretize for sampled data, our approach starts with a discretedomain
Learning mixtures of product distributions over discrete domains
 SIAM J. Comput
"... Abstract. We consider the problem of learning mixtures of product distributions over discrete domains in the distribution learning framework introduced by Kearns et al. [Proceedings of the 26th Annual Symposium on Theory of Computing (STOC), Montréal, QC, 1994, ACM, New York, pp. 273–282]. We give a ..."
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Cited by 45 (5 self)
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Abstract. We consider the problem of learning mixtures of product distributions over discrete domains in the distribution learning framework introduced by Kearns et al. [Proceedings of the 26th Annual Symposium on Theory of Computing (STOC), Montréal, QC, 1994, ACM, New York, pp. 273–282]. We give
Efficient Inference in Large Discrete Domains
 In Proceeding of Nineteenth Conf. on Uncertainity in Artificial Intelligence (UAI03
, 2003
"... In this paper we examine the problem of inference in Bayesian Networks with discrete random variables that have very large or even unbounded domains. For example, in a domain where we are trying to identify a person, we may have variables that have as domains, the set of all names, the set of ..."
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In this paper we examine the problem of inference in Bayesian Networks with discrete random variables that have very large or even unbounded domains. For example, in a domain where we are trying to identify a person, we may have variables that have as domains, the set of all names, the set
Probabilistic Inference with Large Discrete Domains
, 2006
"... c ○ Rita Sharma, 2006cedurally, in terms of predicates and functions. We present an inference algorithm, Large Domain VE, for the CPD language that uses this representation to partitions the domains of the variables dynamically. The partitions depend on what is observed and what is queried. We apply ..."
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c ○ Rita Sharma, 2006cedurally, in terms of predicates and functions. We present an inference algorithm, Large Domain VE, for the CPD language that uses this representation to partitions the domains of the variables dynamically. The partitions depend on what is observed and what is queried. We
Results 1  10
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8,804