In this paper, we examine the problem of controlling multiple behaviour-based autonomous robots. Based on observations made from the study of social insects, we propose ve simple mechanisms used to invoke group behaviour in simple sensor-based mobile robots. The proposed mechanisms allow populations of behaviour-based robots to perform tasks without centralized control or use of explicit communication. We have veri ed our collective control strategies by designing a robot population simulator called SimbotCity. Wehave also constructed a system of ve homogeneous sensor-based mobile robots, capable of achieving simple collective tasks, to demonstrate the feasibility of some of the control mechanisms.

Experiments over a variety of optimization problems indicate that scale-effective convergence is an emergent behavior of certain computer-based agents, provided these agents are organized into an asynchronous team (A-Team). An A-Team is a problem-solving architecture in which the agents are autonomous and cooperate by modifying one another’s trial-solutions. These solutions circulate continually. Convergence is said to occur if and when a persistent solution appears. Convergence is said to be scale-effective if the quality of the persistent solution increases with the number of agents, and the speed of its appearance increases with the number of computers. This paper uses a traveling salesman problem to illustrate scale-effective behavior and develops Markov models that explain its occurrence in A-Teams, particularly, how autonomous agents, without strategic planning or centralized coordination, can converge to solutions of arbitrarily high quality. The models also predict two properties that remain to be experimentally confirmed: • construction and destruction are dual processes. In other words, adept destruction can compensate for inept construction in an A-Team, and vice-versa. (Construction refers to the process of creating or changing solutions, destruction, to the process of erasing solutions.) • solution-quality is independent of agent-phylum. In other words, A-Teams provide an organizational framework in which humans and autonomous mechanical agents can cooperate effectively. 2 1.

A cyber agent is any program, machine or person engaged in computer-enabled work. Thus, cyber agents can vary considerably in complexity and intelligence. Can they, despite their variety, be organized to collaborate effectively ? Both empirical evidence and theory suggest that they can. Moreover, there seem to be simple rules for designing problem-solving organizations in which collaboration among cyber agents is automatic and scale-effective (adding agents tends to improve solution-quality; adding computers tends to improve solution-speed). This paper develops some of these rules. 1. INTRODUCTION Computer networks make it possible to interconnect and therefore, organize, large numbers of distributed cyber agents, varying in type from simple programs to skilled humans. Our goal is to develop a class of organizations in which such agents can collaborate easily and effectively. More specifically, our goal is to develop methods for routinely solving arbitrary instances of the following ...

Is effective collaboration possible among autonomous software agents that are distributed over a network of computers? Both empirical evidence and theory suggest that there are simple rules for designing problem-solving organizations in which collaboration among such agents is automatic and scale-effective (adding agents tends to improve solution-quality; adding computers tends to improve solution-speed). This paper develops some of these rules for off-line problems, and argues that the rules can be extended for the on-line (real-time) control of distributed systems, such as electric power networks. Keywords: autonomous agents, collaboration, multi-agent systems, organizations. 1. INTRODUCTION This paper deals with the skills that unsupervised (autonomous) software agents must have if they are to collaborate effectively. This section explains the terminology that will be used, formulates the collaboration problem and outlines an approach to its resolution. 1.1. Terminology Let a "s...

Individual success in group-structured populations has two components. First, an individual gains by outcompeting its neighbours for local resources. Second, an individual’s share of group success must be weighted by the total productivity of the group. The essence of sociality arises from the tension between selfish gains against neighbours and the associated loss that selfishness imposes by degrading the efficiency of the group. Without some force to modulate selfishness, the natural tendencies of self interest typically degrade group performance to the detriment of all. This is the tragedy of the commons. Kin selection provides the most widely discussed way in which the tragedy is overcome in biology. Kin selection arises from behavioural

this paper, we only consider the case in which S f = S r and thus we refer to S f and S r by a single parameter, Sw . This parameter set has been used throughout with any changes indicated.

Abstract. In social insect colonies, many tasks are performed by higherorder entities, such as groups and teams whose task solving capacities transcend those of the individual participants. In this paper, we investigate the emergence of such higher-order entities using a colony of up to 12 physical robots. We report on an experimental study in which the robots engage in a range of different activities, including exploration, path formation, recruitment, self-assembly and group transport. Once the robots start interacting with each other and with their environment, they selforganise into teams in which distinct roles are performed concurrently. The system displays a dynamical hierarchy of teamwork, the cooperating elements of which comprise higher-order entities. The study shows that teamwork requires neither individual recognition nor inter-individual differences, and as such might contribute to the ongoing debate on the role of such characteristics for the division of labour in social insects.

Abstract. In this study X-machines and hierarchical organized X-machines will be used to model different aspects of the behaviour of social insect communities. The model is organized as a community of complex agents showing similarities to networks of P systems. 1

The relation between culture and biology emerged as one of anthropology’s first intellectual responsibilities. It remains one of our most frustrating enigmas. The dichotomy of “nature and nurture ” has been a persistent obstacle to consilience between the biological and social sciences. Anthropology has traditionally recognized that culture is inextricably linked to the evolution of mind and that the converse is equally important. In this chapter, I review a scenario in which the mind evolved as a “social tool ” in an increasingly cultural environment. I posit that the human psyche was designed primarily to contend with social relationships, whereas the physical environment was relatively less important. Most natural selection in regard to brain evolution was a consequence of interactions with conspecifics, not with food and climate. The primary mental chess game was with other intelligent hominid competitors and cooperators, not with fruits, tools, prey, or snow. An extended juvenile period was favored by natural selection because of the need for more time to develop mental competencies used in forming coalitions and other aspects of social competition. “Culture, ” shorthand for the information acquired and used by minds in social ways, was a 73 03-Burgess.qxd 4/29/04 8:59 PM Page 74

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