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Learning in the Santa Fe Bar Problem
"... This paper investigates learning in the Santa Fe (El Farol) bar problem (sfbp). It is argued that rationality together with beliefbased learning (e.g., Bayesian updating) yields unstable behavior in this game. More specifically ..."
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This paper investigates learning in the Santa Fe (El Farol) bar problem (sfbp). It is argued that rationality together with beliefbased learning (e.g., Bayesian updating) yields unstable behavior in this game. More specifically
Fair and Efficient Solutions to the Santa Fe Bar Problem
 In Proceedings of the Grace Hopper Celebration of Women in Computing 2002
, 2002
"... This paper asks the question: can adaptive, but not necessarily rational, agents learn Nash equilibrium behavior in the Santa Fe Bar Problem? To answer this question, three learning algorithms are simulated: fictitious play, noregret learning, and Qlearning. Conditions under which these algorithms ..."
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Cited by 9 (0 self)
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This paper asks the question: can adaptive, but not necessarily rational, agents learn Nash equilibrium behavior in the Santa Fe Bar Problem? To answer this question, three learning algorithms are simulated: fictitious play, noregret learning, and Qlearning. Conditions under which
Integral transforms, spectral representation and the dbar problem
"... An alternative de nition of the spectrum of a linear di¬erential operator is introduced and a constructive procedure for nding the associated spectral representation is given. This construction is based on the solution of a Riemann{Hilbert or of a dbar problem. The general theory is illustrated b ..."
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Cited by 5 (4 self)
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An alternative de nition of the spectrum of a linear di¬erential operator is introduced and a constructive procedure for nding the associated spectral representation is given. This construction is based on the solution of a Riemann{Hilbert or of a dbar problem. The general theory is illustrated
The English Noun Phrase in its Sentential Aspect
 PH.D. DISSERTATION MIT
, 1987
"... This dissertation is a defense of the hypothesis that the noun phrase is headed by a functional element (i.e., "nonlexical" category) D, identified with the determiner. In this way, the structure of the noun phrase parallels that of the sentence, which is headed by Infl(ection), under ass ..."
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Cited by 532 (4 self)
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phrases. The problem of capturing this dual aspect of the Possing construction is heightened by current restrictive views of Xbar theory, which, in particular, rule out the obvious structure for Possing, [ NP NP VP ing ], by virtue of its exocentricity. Consideration of languages in which nouns, even
Monotone Complexity
, 1990
"... We give a general complexity classification scheme for monotone computation, including monotone spacebounded and Turing machine models not previously considered. We propose monotone complexity classes including mAC i , mNC i , mLOGCFL, mBWBP , mL, mNL, mP , mBPP and mNP . We define a simple ..."
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Cited by 2825 (11 self)
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simple notion of monotone reducibility and exhibit complete problems. This provides a framework for stating existing results and asking new questions. We show that mNL (monotone nondeterministic logspace) is not closed under complementation, in contrast to Immerman's and Szelepcs &apos
Automating the Design of Graphical Presentations of Relational Information
 ACM Transactions on Graphics
, 1986
"... The goal of the research described in this paper is to develop an applicationindependent presentation tool that automatically designs effective graphical presentations (such as bar charts, scatter plots, and connected graphs) of relational information. Two problems are raised by this goal: The codi ..."
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Cited by 559 (9 self)
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The goal of the research described in this paper is to develop an applicationindependent presentation tool that automatically designs effective graphical presentations (such as bar charts, scatter plots, and connected graphs) of relational information. Two problems are raised by this goal
Loopy belief propagation for approximate inference: An empirical study. In:
 Proceedings of Uncertainty in AI,
, 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" the use of Pearl's polytree algorithm in a Bayesian network with loops can perform well in the context of errorcorrecting codes. The most dramatic instance of this is the near Shannonlimit performanc ..."
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Cited by 676 (15 self)
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the real QMR network to converge if the priors were sampled randomly in the range [0, Small priors are not the only thing that causes oscil lation. Small weights can, too. The effect of both The exact marginals are represented by the circles; the ends of the "error bars" represent the loopy
The sliderpinning problem
 CCCG
, 2007
"... A Laman mechanism is a flexible planar barandjoint framework with m ≤ 2n − 3 edges and exactly k = 2n − m degrees of freedom. The sliderpinning problem is to eliminate all the degrees of freedom of a Laman mechanism, in an optimal fashion, by individually fixing x or y coordinates of vertices. We ..."
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Cited by 384 (7 self)
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A Laman mechanism is a flexible planar barandjoint framework with m ≤ 2n − 3 edges and exactly k = 2n − m degrees of freedom. The sliderpinning problem is to eliminate all the degrees of freedom of a Laman mechanism, in an optimal fashion, by individually fixing x or y coordinates of vertices
Abstract Fair and Efficient Solutions to the Santa Fe Bar Problem
"... This paper asks the question: can adaptive, but not necessarily rational, agents learn Nash equilibrium behavior in the Santa Fe Bar Problem? To answer this question, three learning algorithms are simulated: fictitious play, noregret learning, and Élearning. Conditions under which these algorithms ..."
Abstract
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This paper asks the question: can adaptive, but not necessarily rational, agents learn Nash equilibrium behavior in the Santa Fe Bar Problem? To answer this question, three learning algorithms are simulated: fictitious play, noregret learning, and Élearning. Conditions under which
The Santa Fe Bar Problem: A Study in Multiagent Learning
"... We study the process of multiagent learning in the context of the "Santa Fe Bar Problem" (Arthur 1994). We imagine a system of bounded rational agents taking decisions within a repeated game. Each agent has a finite number of decision procedures with which to reason, and access to the enti ..."
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We study the process of multiagent learning in the context of the "Santa Fe Bar Problem" (Arthur 1994). We imagine a system of bounded rational agents taking decisions within a repeated game. Each agent has a finite number of decision procedures with which to reason, and access
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