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Hitoshi Iba, “Frontiers in Evolutionary Robotics ”

Introduction

Some problems can be efficiently solved only by teams consisting of cooperative

autonomous players (robots). Many researchers have developed methods that do not

require human designers to define specific behaviors of players for each problem. The work

reported in this chapter focuses on the techniques of evolutionary computation, which has

been regarded as one of the most promising approaches to solving such complex problems.

However, in using evolutionary computation for generating players performing tasks

cooperatively, one faces fundamental and difficult decisions, including the one regarding

the so-called credit assignment problem (Haynes et al., 1995). For example, if we can only

evaluate the global performance of each team, how do we divide up the team’s performance

among the participating players? We believe that there are some correlations among design

decisions, and therefore a comprehensive evaluation of them is essential, although several

researchers have proposed evolutionary methods for evolving teams performing specific

tasks.

This chapter is organized as follows. In Section 2, we list three fundamental decisions and

possible options in each decision in designing a method for evolving a cooperative team. We

find that there are 18 typical combinations available. Then, in Section 3, we describe the

ultimately simplified soccer game played on a one-dimensional field as a testbed for

comparative evaluation of these 18 candidate methods. Section 4 reports on the results of the

comparative evaluation of these methods, and Section 5 summarizes the work.

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