This survey characterizes an emerging research area, sometimes called coordination theory, that focuses on the interdisciplinary study of coordination. Research in this area uses and extends ideas about coordination from disciplines such as computer science, organization theory, operations research, economics, linguistics, and psychology. A key insight of the framework presented here is that coordination can be seen as the process of managing dependencies among activities. Further progress, therefore, should be possible by characterizing different kinds of dependencies and identifying the coordination processes that can be used to manage them. A variety of processes are analyzed from this perspective, and commonalities across disciplines are identified. Processes analyzed include those for managing shared resources, producer/consumer relationships, simultaneity constraints, and tank/subtask dependencies. Section 3 summarizes ways of applying a coordination perspective in three different domains: (1) understanding the effects of information technology on human organizations and markets, (2) designing cooperative work tools, and (3) designing distributed and parallel computer systems. In the final section, elements of a research

This thesis addresses situated, embodied agents interacting in complex domains. It focuses on two problems: 1) synthesis and analysis of intelligent group behavior, and 2) learning in complex group environments. Basic behaviors, control laws that cluster constraints to achieve particular goals and have the appropriate compositional properties, are proposed as effective primitives for control and learning. The thesis describes the process of selecting such basic behaviors, formally specifying them, algorithmically implementing them, and empirically evaluating them. All of the proposed ideas are validated with a group of up to 20 mobile robots using a basic behavior set consisting of: safe--wandering, following, aggregation, dispersion, and homing. The set of basic behaviors acts as a substrate for achieving more complex high--level goals and tasks. Two behavior combination operators are introduced, and verified by combining subsets of the above basic behavior set to implement collective flocking, foraging, and docking. A methodology is introduced for automatically constructing higher--level behaviors

This paper attempts to bridge the fields of machine learning, robotics, and distributed AI. It discusses the use of communication in reducing the undesirable effects of locality in fully distributed multi-agent systems with multiple agents/robots learning in parallel while interacting with each other. Two key problems, hidden state and credit assignment, are addressed by applying local undirected broadcast communication in a dual role: as sensing and as reinforcement. The methodology is demonstrated on two multi-robot learning experiments. The first describes learning a tightly-coupled coordination task with two robots, the second a loosely-coupled task with four robots learning social rules. Communication is used to 1) share sensory data to overcome hidden state and 2) share reinforcement to overcome the credit assignment problem between the agents and bridge the gap between local/individual and global/group payoff. 1 Introduction This paper attempts to bridge the fields of machine l...

Abstract. Swarm Intelligence (SI) is the property of a system whereby the collective behaviors of (unsophisticated) entities interacting locally with their environment cause coherent functional global patterns to emerge. SI provides a basis with which it is possible to explore collective (or distributed) problem solving without centralized control or the provision of a global model. To tackle the formation of a coherent social collective intelligence from individual behaviors, we discuss several concepts related to self-organization, stigmergy and social foraging in animals. Then, in a more abstract level we suggest and stress the role played not only by the environmental media as a driving force for societal learning, as well as by positive and negative feedbacks produced by the many interactions among agents. Finally, presenting a simple model based on the above features, we will address the collective adaptation of a social community to a cultural (environmental, contextual) or media informational dynamical landscape, represented here – for the purpose of different experiments – by several three-dimensional mathematical functions that suddenly change over time. Results indicate that the collective intelligence is able to cope and quickly adapt to unforeseen situations even when over the same cooperative foraging period, the community is requested to deal with two different and contradictory purposes. 1

Using the theory and methods of non-linear dynamics, I explore the consequences of cooperation on the size and dynamics of social groups. I present a model that incorporates into a discrete growth equation a positive density-dependent factor to represent the synergistic eVects of cooperation. Analysis of this model shows that, by increasing the net reproductive output of group-living organisms, cooperation could either stabilize or destabilize the dynamics of a social group. At one end of the spectrum, group-living and cooperation could make persistence possible under harsh demographic or ecological conditions. At the other end of the spectrum, in populations already organized in social groups, cooperation could lead to more highly integrated social groups that are subject to a boom-and-bust pattern of growth. When groups last for multiple generations, such a pattern could take the form of periodic or chaotic dynamics. It is suggested that dynamical instability could result in rates of group turnover large enough for selection among the highly integrated social groups to take over as the primary evolutionary force. Consideration of the dynamical eVects of cooperation, therefore, may shed light both on the ecological and demographic conditions leading to the origin and maintenance of group-living as well as on the forces responsible for shaping the diversity of animal societies.

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The work presented in this thesis deals with techniques to improve problem solving control skills of cooperative agents through machine learning. In a multi-agent system, the local problem solving control of an agent can interact in complex and intricate ways with the problem solving control of other agents. In such systems, an agent cannot make effective control decisions based purely on its local problem solving state. Effective cooperation requires that the global problem-solving state influence the local control decisions made by an agent. We call such an influence cooperative control. An agent with a purely local view of the problem solving situation cannot learn ...

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Social responses without early experience: Australian brush-turkey chicks use

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