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122
Complexity Results about Nash Equilibria
, 2002
"... Noncooperative game theory provides a normative framework for analyzing strategic interactions. ..."
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Cited by 132 (10 self)
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Noncooperative game theory provides a normative framework for analyzing strategic interactions.
An introduction to collective intelligence
 Handbook of Agent technology. AAAI
, 1999
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Negotiation Among Selfinterested Computationally Limited Agents
, 1996
"... A Dissertation Presented by TUOMAS W. SANDHOLM ..."
Provably BoundedOptimal Agents
 Journal of Artificial Intelligence Research
, 1995
"... Since its inception, artificial intelligence has relied upon a theoretical foundation centred around perfect rationality as the desired property of intelligent systems. We argue, as others have done, that this foundation is inadequate because it imposes fundamentally unsatisfiable requirements. As a ..."
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Cited by 85 (2 self)
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Since its inception, artificial intelligence has relied upon a theoretical foundation centred around perfect rationality as the desired property of intelligent systems. We argue, as others have done, that this foundation is inadequate because it imposes fundamentally unsatisfiable requirements. As a result, there has arisen a wide gap between theory and practice in AI, hindering progress in the field. We propose instead a property called bounded optimality. Roughly speaking, an agent is boundedoptimal if its program is a solution to the constrained optimization problem presented by its architecture and the task environment. We show how to construct agents with this property for a simple class of machine architectures in a broad class of realtime environments. We illustrate these results using a simple model of an automated mail sorting facility. We also define a weaker property, asymptotic bounded optimality (ABO), that generalizes the notion of optimality in classical complexity th...
Multiagent reinforcement learning: a critical survey
, 2003
"... We survey the recent work in AI on multiagent reinforcement learning (that is, learning in stochastic games). We then argue that, while exciting, this work is flawed. The fundamental flaw is unclarity about the problem or problems being addressed. After tracing a representative sample of the recent ..."
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Cited by 59 (1 self)
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We survey the recent work in AI on multiagent reinforcement learning (that is, learning in stochastic games). We then argue that, while exciting, this work is flawed. The fundamental flaw is unclarity about the problem or problems being addressed. After tracing a representative sample of the recent literature, we identify four welldefined problems in multiagent reinforcement learning, single out the problem that in our view is most suitable for AI, and make some remarks about how we believe progress is tobemadeonthisproblem. 1
Algorithmic Knowledge
 Proc. Second Conference on Theoretical Aspects of Reasoning about Knowledge
, 1994
"... : The standard model of knowledge in multiagent systems suffers from what has been called the logical omniscience problem: agents know all tautologies, and know all the logical consequences of their knowledge. For many types of analysis, this turns out not to be a problem. Knowledge is viewed as be ..."
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Cited by 53 (10 self)
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: The standard model of knowledge in multiagent systems suffers from what has been called the logical omniscience problem: agents know all tautologies, and know all the logical consequences of their knowledge. For many types of analysis, this turns out not to be a problem. Knowledge is viewed as being ascribed by the system designer to the agents; agents are not assumed to compute their knowledge in any way, nor is it assumed that they can necessarily answer questions based on their knowledge. Nevertheless, in many applications that we are interested in, agents need to act on their knowledge. In such applications, an externally ascribed notion of knowledge is insufficient: clearly an agent can base his actions only on what he explicitly knows. Furthermore, an agent that has to act on his knowledge has to be able to compute this knowledge; we do need to take into account the algorithms available to the agent, as well as the "effort" required to compute knowledge. In this paper, we show...
Learning against opponents with bounded memory
 In IJCAI
, 2005
"... Recently, a number of authors have proposed criteria for evaluating learning algorithms in multiagent systems. While welljustified, each of these has generally given little attention to one of the main challenges of a multiagent setting: the capability of the other agents to adapt and learn as wel ..."
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Cited by 43 (4 self)
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Recently, a number of authors have proposed criteria for evaluating learning algorithms in multiagent systems. While welljustified, each of these has generally given little attention to one of the main challenges of a multiagent setting: the capability of the other agents to adapt and learn as well. We propose extending existing criteria to apply to a class of adaptive opponents with bounded memory which we describe. We then show an algorithm that provably achieves an ɛbest response against this richer class of opponents while simultaneously guaranteeing a minimum payoff against any opponent and performing well in selfplay. This new algorithm also demonstrates strong performance in empirical tests against a variety of opponents in a wide range of environments. 1
Breeding hybrid strategies: Optimal behaviour for oligopolists
 Journal of Evolutionary Economics
, 1992
"... Abstract. Oligopolistic pricing decisions in which the choice variable is not dichotomous as in the simple prisoner's dilemma but continuous have been modeled as a generalized prisoner's dilemma (GPD) by Fader and Hauser, who sought, in the two MIT Computer Strategy Tournaments, to obtai ..."
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Cited by 35 (9 self)
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Abstract. Oligopolistic pricing decisions in which the choice variable is not dichotomous as in the simple prisoner's dilemma but continuous have been modeled as a generalized prisoner's dilemma (GPD) by Fader and Hauser, who sought, in the two MIT Computer Strategy Tournaments, to obtain an effective generalization of Rapoport's Tit for Tat for the threeperson repeated game. Holland's genetic algorithm and Axelrod's representation of contingent strategies provide a means of generating new strategies in the computer, through machine learning, without outside submissions. The paper discusses how findings from twoperson tournaments can be extended to the GPD, in particular how the author's winning strategy in the Second MIT Competitive Strategy Tournament could be bettered. The paper provides insight into how oligopolistic pricing competitors can successfully compete, and underlines the importance of "niche " strategies, successful against a particular environment of competitors. Bootstrapping, or breeding strategies against their peers, provides a means of
Information theory  the bridge connecting bounded rational game theory and statistical physics
 Statistical Physics
, 2004
"... A longrunning difficulty with conventional game theory has been how to modify it to accommodate the bounded rationality of all realworld players. A recurring issue in statistical physics is how best to approximate joint probability distributions with decoupled (and therefore far more tractable) di ..."
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Cited by 26 (10 self)
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A longrunning difficulty with conventional game theory has been how to modify it to accommodate the bounded rationality of all realworld players. A recurring issue in statistical physics is how best to approximate joint probability distributions with decoupled (and therefore far more tractable) distributions. This paper shows that the same information theoretic mathematical structure, known as Product Distribution (PD) theory, addresses both issues. In this, PD theory not only provides a principled formulation of bounded rationality and a set of new types of mean field theory in statistical physics; it also shows that those topics are fundamentally one and the same. 1
The complexity of game dynamics: Bgp oscillations, sink equilibria, and beyond
 In SODA ’08: Proceedings of the nineteenth annual ACMSIAM symposium on Discrete algorithms
, 2008
"... We settle the complexity of a wellknown problem in networking by establishing that it is PSPACEcomplete to tell whether a system of path preferences in the BGP protocol [25] can lead to oscillatory behavior; one key insight is that the BGP oscillation question is in fact one about Nash dynamics. W ..."
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Cited by 24 (4 self)
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We settle the complexity of a wellknown problem in networking by establishing that it is PSPACEcomplete to tell whether a system of path preferences in the BGP protocol [25] can lead to oscillatory behavior; one key insight is that the BGP oscillation question is in fact one about Nash dynamics. We show that the concept of sink equilibria proposed recently in [11] is also PSPACEcomplete to analyze and approximate for graphical games. Finally, we propose a new equilibrium concept inspired by game dynamics, unit recall equilibria, which we show to be close to universal (exists with high probability in a random game) and algorithmically promising. We also give a relaxation thereof, called componentwise unit recall equilibria, which we show to be both tractable and universal (guaranteed to exist in every game).