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The paper surveys work on the computational modeling of the origins and evolution of language. The main approaches are clarified and some example experiments from the domains of the evolution of communication, phonetics, lexicon formation, and syntax are discussed. 1 Introduction The paper surveys research in which software simulations and experiments with robotic agents are used to explore the viewpoint that language is a complex dynamical system. The main goal of the paper is to outline the approaches and show example experiments. Much more work needs to be done to arrive at a full-fledged theory of the origins of language and even about the work already done much more can be said than is possible in a single paper. Nevertheless, I hope to show that a new exciting approach to the study of the origins and evolution of language is taking shape. The rest of the paper is in four parts. The next section clarifies the notion of a complex system and the multi-agent perspective. Section 3...

There is increasing awareness amongst researchers using evolutionary algorithms that the use of coevolution can sometimes introduce as many problems as it solves. Many suggestions have been made about the causes of the failures but in these reports the mechanisms are always difficult to disentangle from the particulars of the problem domain. This paper utilizes a minimal substrate in which coevolutionary concepts, dynamics, and problems can be clarified. Specifically, we evolve scalar values and vectors under various coevolutionary setups. This substrate enables us to illustrate clearly several concepts important to coevolution and its sources of failure. 1 INTRODUCTION Coevolution has become increasingly popular in Evolutionary Algorithms research [Hillis 1992, Sims 1994, Juille 1996, Miller & Cliff 1994]. The basic idea behind the approach seems intuitive enough -- rather than evolve individuals against a fixed objective metric, we engage individuals in the task of im...

The fundamental distinction between ordinary evolutionary algorithms (EA) and co-evolutionary algorithms lies in the interaction between coevolving entities. We believe that this property is essentially game-theoretic in nature. Using game theory, we describe extensions that allow familiar mixing-matrix and Markov-chain models of EAs to address coevolutionary algorithm dynamics. We then employ concepts from evolutionary game theory to examine design aspects of conventional coevolutionary algorithms that are poorly understood.

We examine a simulated but realistic multi-robot transport task that suffers from spatial interference.

... Equation, a profound insight into the nature of selection and the basis for the modern theories of kin and group selection; (ii) the theory of games and animal behavior, based on the concept of the evolutionarily stable strategy; and (iii) the modern interpretation of Fisher’s fundamental theorem of natural selection, Fisher’s theorem being perhaps the most cited and least understood idea in the history of evolutionary genetics. This paper summarizes Price’s contributions and briefly outlines why, toward the end of his painful intellectual journey, he chose to focus his deep humanistic feelings and sharp,

The replicator equation used in evolutionary game theory (EGT) assumes that strategies reproduce in direct proportion to their payoffs; this is akin to the use of fitness-proportionate selection in an evolutionary algorithm (EA). In this paper, we investigate how various other selection methods commonly used in EAs can affect the discrete-time dynamics of EGT. In particular, we show that the existence of evolutionary stable strategies (ESS) is sensitive to the selection method used. Rather than maintain the dynamics and equilibria of EGT, the selection methods we test impose a fixed-point dynamic virtually unrelated to the payoffs of the game matrix, give limit cycles, or induce chaos. These results are significant to the field of evolutionary computation because EGT can be understood as a coevolutionary algorithm operating under ideal conditions: an infinite population, noiseless payoffs, and complete knowledge of the phenotype space. Thus, certain selection methods, which may operate effectively in simple evolution, are pathological in an ideal-world coevolutionary algorithm, and therefore du- bious under real-world conditions.

This paper describes necessary elements for constructing theoretical models of network and system administration. Armed with a theoretical model it becomes possible to determine best practices and optimal strategies in a way which objectively relates policies and assumptions to results obtained. It is concluded that a mixture of automation and human, or other intelligent incursion is required to fully implement system policy with current technology. Some aspects of the author’s immunity model for automated system administration are explained, as an example. A theoretical framework makes the prediction that the optimal balance between resource availability and garbage collection strategies is encompassed by the immunity model. 1

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Evolutionary computation is a useful technique for learning behaviors in multiagent systems. Among the several types of evolutionary computation, one natural and popular method is to coevolve multiagent behaviors in multiple, cooperating populations. Recent research has suggested that coevolutionary systems may favor stability rather than performance in some domains. In order to improve upon existing methods, this paper examines the idea of modifying traditional coevolution, biasing it to search for maximal rewards. We introduce a theoretical justification of the improved method and present experiments in three problem domains. We conclude that biasing can help coevolution find better results in some multiagent problem domains. 1