There has been increased research interest in systems composed of multiple autonomous mobile robots exhibiting collective behavior. Groups of mobile robots are constructed, with an aim to studying such issues as group architecture, resource conflict, origin of cooperation, learning, and geometric problems. As yet, few applications of collective robotics have been reported, and supporting theory is still in its formative stages. In this paper, we give a critical survey of existing works and discuss open problems in this field, emphasizing the various theoretical issues that arise in the study of cooperative robotics. We describe the intellectual heritages that have guided early research, as well as possible additions to the set of existing motivations. 1

. Co-evolution can give rise to the "Red Queen effect", where interacting populations alter each other's fitness landscapes. The Red Queen effect significantly complicates any measurement of co-evolutionary progress, introducing fitness ambiguities where improvements in performance of co-evolved individuals can appear as a decline or stasis in the usual measures of evolutionary progress. Unfortunately, no appropriate measures of fitness given the Red Queen effect have been developed in artificial life, theoretical biology, population dynamics, or evolutionary genetics. We propose a set of appropriate performance measures based on both genetic and behavioral data, and illustrate their use in a simulation of co-evolution between genetically specified continuous-time noisy recurrent neural networks which generate pursuit and evasion behaviors in autonomous agents. 1 Introduction Some biologists have suggested that the `Red Queen effect' arising from coevolutionary arms races has been a p...

This paper discusses the feasibility of applying evolutionary methods to automatically generating controllers for physical mobile robots. We overview the state of the art in the field, describe some of the main approaches, discuss the key challenges, unanswered problems, and some promising directions. 1 Introduction This paper is concerned with the distant goal of automated synthesis of robot controllers. Specifically, we focus on the problems of evolving controllers for physically embodied and embedded systems that deal with all of the noise and uncertainly present in the world. We will also address some systems that evolve both the morphology and the controller of a robot. Within the scope of this paper we define morphology as the physical, embodied characteristics of the robot, such as its mechanics and sensor organization. Given that definition, the only examples of evolving both morphology and control exist in simulation. Evolutionary methods for automated hardware design are an ...

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...

In a previous SAB paper [10], we presented the scientific rationale for simulating the coevolution of pursuit and evasion strategies. Here, we present an overview of our simulation methods and some results. Our most notable results are as follows. First, co-evolution works to produce good pursuers and good evaders through a pure bootstrapping process, but both types are rather specially adapted to their opponents' current counter-strategies. Second, eyes and brains can also co-evolve within each simulated species -- for example, pursuers usually evolved eyes on the front of their bodies (like cheetahs), while evaders usually evolved eyes pointing sideways or even backwards (like gazelles). Third, both kinds of coevolution are promoted by allowing spatially distributed populations, gene duplication, and an explicitly spatial morphogenesis program for eyes and brains that allows bilateral symmetry. The paper concludes by discussing some possible applications of simulated pursuit-evasion ...

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One of the persistent themes in Artificial Life research is the use of co-evolutionary arms races in the development of specific and complex behaviors. However, other than Sims’s work on artificial robots, most of the work has attacked very simple games of prisoners dilemma or predator and prey. 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

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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 predator/prey domain is utilized to conduct research in Distributed Artificial Intelligence. Genetic Programming is used to evolve behavioral strategies for the predator agents. To further the utility of the predator strategies, the prey population is allowed to evolve at the same time. The expected competitive learning cycle did not surface. This failing is investigated, and a simple prey algorithm surfaces, which is consistently able to evade capture from the predator algorithms.