Results 1  10
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535
Grounding in communication
 In
, 1991
"... We give a general analysis of a class of pairs of positive selfadjoint operators A and B for which A + XB has a limit (in strong resolvent sense) as h10 which is an operator A, # A! Recently, Klauder [4] has discussed the following example: Let A be the operator(d2/A2) + x2 on L2(R, dx) and let ..."
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We give a general analysis of a class of pairs of positive selfadjoint operators A and B for which A + XB has a limit (in strong resolvent sense) as h10 which is an operator A, # A! Recently, Klauder [4] has discussed the following example: Let A be the operator(d2/A2) + x2 on L2(R, dx) and let B = 1 x 1s. The eigenvectors and eigenvalues of A are, of course, well known to be the Hermite functions, H,(x), n = 0, l,... and E, = 2n + 1. Klauder then considers the eigenvectors of A + XB (A> 0) by manipulations with the ordinary differential equation (we consider the domain questions, which Klauder ignores, below). He finds that the eigenvalues E,(X) and eigenvectors &(A) do not converge to 8, and H, but rather AO) + (en 4 Ho+, J%(X)+ gn+1 I n = 0, 2,..., We wish to discuss in detail the general phenomena which Klauder has uncovered. We freely use the techniques of quadratic forms and strong resolvent convergence; see e.g. [3], [5]. Once one decides to analyze Klauder’s phenomenon in the language of quadratic forms, the phenomenon is quite easy to understand and control. In fact, the theory is implicit in Kato’s book [3, VIII.31.
Evolution of Conventions
 Econometrica
, 1993
"... Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at ..."
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Cited by 358 (9 self)
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The dynamics of reinforcement learning in cooperative multiagent systems
 IN PROCEEDINGS OF NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE (AAAI98
, 1998
"... Reinforcement learning can provide a robust and natural means for agents to learn how to coordinate their action choices in multiagent systems. We examine some of the factors that can influence the dynamics of the learning process in such a setting. We first distinguish reinforcement learners that a ..."
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Cited by 323 (1 self)
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Reinforcement learning can provide a robust and natural means for agents to learn how to coordinate their action choices in multiagent systems. We examine some of the factors that can influence the dynamics of the learning process in such a setting. We first distinguish reinforcement learners that are unaware of (or ignore) the presence of other agents from those that explicitly attempt to learn the value of joint actions and the strategies of their counterparts. We study (a simple form of) Qlearning in cooperative multiagent systems under these two perspectives, focusing on the influence of that game structure and exploration strategies on convergence to (optimal and suboptimal) Nash equilibria. We then propose alternative optimistic exploration strategies that increase the likelihood of convergence to an optimal equilibrium.
Epistemic conditions for Nash equilibrium
, 1991
"... According to conventional wisdom, Nash equilibrium in a game “involves” common knowledge of the payoff functions, of the rationality of the players, and of the strategies played. The basis for this wisdom is explored, and it turns out that considerably weaker conditions suffice. First, note that if ..."
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Cited by 169 (6 self)
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According to conventional wisdom, Nash equilibrium in a game “involves” common knowledge of the payoff functions, of the rationality of the players, and of the strategies played. The basis for this wisdom is explored, and it turns out that considerably weaker conditions suffice. First, note that if each player is rational and knows his own payoff function, and the strategy choices of the players are mutually known, then these choices form a Nash equilibrium. The other two results treat the mixed strategies of a player not as conscious randomization of that player, but as conjectures of the other players about what he will do. When n = 2, mutual knowledge of the payoff functions, of rationality, and of the conjectures yields Nash equilibrium. When n ≥ 3, mutual knowledge of the payoff functions and of rationality, and common knowledge of the conjectures yield Nash equilibrium when there is a common prior. Examples are provided showing these results to be sharp.
Sequential optimality and coordination in multiagent systems
 In International Joint Conference on Artificial Intelligence
, 1999
"... Coordination of agent activities is a key problem in multiagent systems. Set in a larger decision theoretic context, the existence of coordination problems leads to difficulty in evaluating the utility of a situation. This in turn makes defining optimal policies for sequential decision processes pro ..."
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Cited by 151 (3 self)
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Coordination of agent activities is a key problem in multiagent systems. Set in a larger decision theoretic context, the existence of coordination problems leads to difficulty in evaluating the utility of a situation. This in turn makes defining optimal policies for sequential decision processes problematic. We propose a method for solving sequential multiagent decision problems by allowing agents to reason explicitly about specific coordination mechanisms. We define an extension of value iteration in which the system’s state space is augmented with the state of the coordination mechanism adopted, allowing agents to reason about the short and long term prospects for coordination, the long term consequences of (mis)coordination, and make decisions to engage or avoid coordination problems based on expected value. We also illustrate the benefits of mechanism generalization. 1
The Electronic Mail Game: Strategic Behavior under 'Almost Common Knowledge
 American Economic Review
, 1989
"... prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, noncommercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtai ..."
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Cited by 140 (0 self)
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prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, noncommercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at
Saying what you mean in dialogue: A study in conceptual and semantic coordination
 Cognition
, 1987
"... This paper explores how conversants coordinate their use and interpretation of language in a restricted context. It revolves around the analysis of the spatial descriptions which emerge during the course of 56 dialogues, elicited in the laboratory using a specially designed computer maze game. Two ..."
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Cited by 133 (7 self)
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This paper explores how conversants coordinate their use and interpretation of language in a restricted context. It revolves around the analysis of the spatial descriptions which emerge during the course of 56 dialogues, elicited in the laboratory using a specially designed computer maze game. Two types of analysis are reported. The first is a semantic analysis of the various types of description, which indicates how pairs of speakers develop different language schemes associated with different mental models of the maze configuration. The second analysis concerns how the communicants coordinate in developing their description schemes. The results from this study would suggest that language processing in dialogue may be governed by local principles of interaction which have received little attention in the psychological and linguistic literature to date.
Information Structure in Discourse: Towards an Integrated Formal Theory of Pragmatics
, 1998
"... For many linguists interested in pragmatics, including the Prague School theorists, ..."
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Cited by 125 (2 self)
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For many linguists interested in pragmatics, including the Prague School theorists,
Planning, learning and coordination in multiagent decision processes
 In Proceedings of the Sixth Conference on Theoretical Aspects of Rationality and Knowledge (TARK96
, 1996
"... There has been a growing interest in AI in the design of multiagent systems, especially in multiagent cooperative planning. In this paper, we investigate the extent to which methods from singleagent planning and learning can be applied in multiagent settings. We survey a number of different techniq ..."
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Cited by 102 (1 self)
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There has been a growing interest in AI in the design of multiagent systems, especially in multiagent cooperative planning. In this paper, we investigate the extent to which methods from singleagent planning and learning can be applied in multiagent settings. We survey a number of different techniques from decisiontheoretic planning and reinforcement learning and describe a number of interesting issues that arise with regard to coordinating the policies of individual agents. To this end, we describe multiagent Markov decision processes as a general model in which to frame this discussion. These are special nperson cooperative games in which agents share the same utility function. We discuss coordination mechanisms based on imposed conventions (or social laws) as well as learning methods for coordination. Our focus is on the decomposition of sequential decision processes so that coordination can be learned (or imposed) locally, at the level of individual states. We also discuss the use of structured problem representations and their role in the generalization of learned conventions and in approximation. 1
The Negotiation and Acquisition of Recursive Grammars as a Result of Competition Among Exemplars
 Linguistic Evolution through Language Acquisition: Formal and Computational Models
, 1999
"... this paper is an investigation of how recursive communication systems can come to be. In particular, the investigation explores the possibility that such a system could emerge among the members of a population as the result of a process I characterize as "negotiation," because each individ ..."
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Cited by 69 (0 self)
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this paper is an investigation of how recursive communication systems can come to be. In particular, the investigation explores the possibility that such a system could emerge among the members of a population as the result of a process I characterize as "negotiation," because each individual both contributes to, and conforms with, the system 1