Distributed Algorithmic Mechanism Design (DAMD) combines theoretical computer science's traditional focus on computational tractability with its more recent interest in incentive compatibility and distributed computing. The Internet's decentralized nature, in which distributed computation and autonomous agents prevail, makes DAMD a very natural approach for many Internet problems. This paper first outlines the basics of DAMD and then reviews previous DAMD results on multicast cost sharing and interdomain routing. The remainder of the paper describes several promising research directions and poses some specific open problems.

We use simple learning models to track the behavior observed in experiments concerning three extensive form games with similar perfect equilibria. In only two of the games does observed behavior approach the perfect equilibrium as players gain experience. We examine a family of learning models which possess some of the robust properties of learning noted in the psychology literature. The intermediate term predictions of these models track well the observed behavior in all three games, even though the models considered differ in their very long term predictions. We argue that for predicting observed behavior the intermediate term predictions of dynamic learning models may be even more important than their asymptotic properties.

Lack of cooperation (free riding) is one of the key problems that confronts today's P2P systems. What makes this problem particularly difficult is the unique set of challenges that P2P systems pose: large populations, high turnover, asymmetry of interest, collusion, zero-cost identities, and traitors. To tackle these challenges we model the P2P system using the Generalized Prisoner's Dilemma (GPD), and propose the Reciprocative decision function as the basis of a family of incentives techniques. These techniques are fully distributed and include: discriminating server selection, maxflowbased subjective reputation, and adaptive stranger policies. Through simulation, we show that these techniques can drive a system of strategic users to nearly optimal levels of cooperation.

We consider "competitive coevolution," in which fitness is based on direct competition among individuals selected from two independently evolving populations of "hosts" and "parasites." Competitive coevolution can lead to an "arms race," in which the two populations reciprocally drive one another to increasing levels of performance and complexity. We use the games of Nim and 3-D Tic-Tac-Toe as test problems to explore three new techniques in competitive coevolution. "Competitive fitness sharing" changes the way fitness is measured, "shared sampling" provides a method for selecting a strong, diverse set of parasites, and the "hall of fame" encourages arms races by saving good individuals from prior generations. We provide several different motivations for these methods, and mathematical insights into their use. Experimental comparisons are done, and a detailed analysis of these experiments is presented in terms of testing issues, diversity, extinction, arms race progress measurements, a...

Following Tesauro's work on TD-Gammon, we used a 4000 parameter feed-forward neural network to develop a competitive backgammon evaluation function. Play proceeds by a roll of the dice, application of the network to all legal moves, and choosing the move with the highest evaluation. However, no back-propagation, reinforcement or temporal difference learning methods were employed. Instead we apply simple hill-climbing in a relative fitness environment. We start with an initial champion of all zero weights and proceed simply by playing the current champion network against a slightly mutated challenger and changing weights if the challenger wins. Surprisingly, this worked rather well. We investigate how the peculiar dynamics of this domain enabled a previously discarded weak method to succeed, by preventing suboptimal equilibria in a "meta-game" of self-learning. Keywords: coevolution, backgammon, reinforcement, temporal difference learning, self-learning Running Head: CO-EVOLUTIONARY LEA...

According to conventional wisdom, Nash equilibrium in a game “involves” common knowl-edge 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 knowl-edge of the conjectures yield Nash equilibrium when there is a common prior. Examples are provided showing these results to be sharp.

Co-evolution refers to the simultaneous evolution of two or more genetically distinct populations with coupled fitness landscapes. In this paper we consider "competitive co-evolution," in which the fitness of an individual in a "host" population is based on direct competition with individual(s) from a "parasite" population. Competitive coevolution is applied to three game-learning problems: Tic-Tac-Toe (TTT), Nim and a small version of Go. Two new techniques in competitive co-evolution are explored. "Competitive fitness sharing" changes the way fitness is measured, and "shared sampling" alters the way parasites are chosen for testing hosts. Experiments using TTT and Nim show a substantial improvement in performance when these methods are used. Preliminary results using co-evolution for the discovery of cellular automata rules for playing Go are presented. 1 Introduction Co-evolution refers to the simultaneous evolution of two or more genetically distinct populations with coupled fit...

Darwinian evolution is based on three fundamental principles, reproduction, mutation and selection, which describe how populations change over time and how new forms evolve out of old ones. There are numerous mathematical descriptions of the resulting evolutionary dynamics. In this paper, we show that apparently very different formulations are part of a single unified framework. At the center of this framework is the equivalence between the replicator–mutator equation and the Price equation. From these equations, we obtain as special cases adaptive dynamics, evolutionary game dynamics, the Lotka-Volterra equation of ecology and the quasispecies equation of molecular evolution.

The paper reports on experiments in which robotic agents and software agents are set up to originate language and meaning. The experiments test the hypothesis that mechanisms for generating complexity commonly found in biosystems, in particular self-organisation, co-evolution, and level formation, also may explain the spontaneous formation, adaptation, and growth in complexity of language.

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