The work of this paper is inspired by the flocking phenomenon observed in Reynolds (1987). We introduce a class of local control laws for a group of mobile agents that result in: (i) global alignment of their velocity vectors, (ii) convergence of their speeds to a common one, (iii) collision avoidance, and (iv) minimization of the agents artificial potential energy. These are made possible through local control action by exploiting the algebraic graph theoretic properties of the underlying interconnection graph. Algebraic connectivity a#ects the performance and robustness properties of the overall closed loop system. We show how the stability of the flocking motion of the group is directly associated with the connectivity properties of the interconnection network and is robust to arbitrary switching of the network topology.

How and why the presence of a person directly affects the perception and action of another person is a phenomenon that has been approached in a limited and piecemeal fashion within psychology. This kind of diffuse strategy has failed to capture the jointness of perception and action within and between people. In contradistinction, the authors offer a perspective that retains both integrally social features (e.g., involves interaction) and yet adequately exploits the current state of knowledge regarding the ecological properties of perception–action, while at the same time drawing on aspects of dynamic systems theory. In this article the authors review the best attempts to examine how one individual affects another’s perceptions and actions in the emergence of a social unit of action. Two important approaches, the individual-level and cognitive dynamics approaches, have yielded insights that derive in significant degree from principles of ecological psychology and/or dynamical systems theory. Prototypic of the individual-level approach is a focus on what can be perceived by coactors with the aim of uncovering how the dispositional qualities (affordances) of another person are informationally specified during social interaction. In contrast, the cognitive dynamics approach simulates dynamical characteristics of cognition and psychological influence with the aim of uncovering how cooperative interaction emerges out of its component parts. The authors argue that these approaches involve, respectively, insufficient mutuality and insufficient embodiment. Consequently, a social synergy perspective is discussed that approaches the problem of socially cooperative interaction at the relational, nonreductive level, using novel methods to examine how social perception and action emerge through self-organizing processes.

In adaptive systems that involve large numbers of agents, emergent, global behaviours that arise from local agent interactions are a critical concept. In nature, such behaviours are central complex group behaviours that must arise from individuals that evolve selfishly. In artificial systems that mimic these adaptive, multi-agent models, understanding and shaping emergence may be essential to such systems' success. To aid in this understanding, this paper introduces a measure gleaned from statistical physics and non-linear systems theory. Unlike other measures of this sort, the one presented here is easy to calculate and can be used for any system that can be described by a set of local state variables centred on each of its constituent agents. It is shown that the measure can be successfully employed as feedback to an optimising genetic algorithm (GA), and to a GA that has similarities to a learning classifier system. The paper concludes that this measure (and others like it) may be a useful tool for understanding and shaping emergent behaviours, and discusses future directions for its use.

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This thesis presents a methodology for designing self-organized autonomous robotic systems and demonstrates how this process can be applied to the problem of finding the source of an airborne odor plume. The design methodology is applicable to other task domains and the resulting odor localization system extends the state of the art. The design procedure centers on the ability to define a specific task performance metric, systematically evaluate performance in a realistic environment, and define abstract relationships between system parameters and system performance. Once such relationships have been experimentally validated in a test environment, they can be used to guide the design of a deployable system. Because this process relies heavily on evaluative feedback, this work emphasizes the development of tools that allow the collection of accurate performance data. It presents a reliable multiple robot test-bed and some task-enabling sensory hardware, as well as validation of the sensory and kinematic models used in simulation. Also, a reinforcement learning methodology is described that provides consistent optimization performance while minimizing the amount of required evaluation.

First and for most, I would like to thank my supervisor Prof. Alan FT Winfield for his guide and advise to complete this work. I really appreciate the freedom that Alan gave me in choosing the research direction and method. Along the way I have benefited a lot from the discussion with him, both from formal supervision meeting and informal research chatting every Friday lunch time. I would like especially to thank Alan for the help to correct all the grammars in English through the whole thesis with great patient. Without the help from Alan, this thesis couldn’t reach its final form. I am also grateful to my second supervisor Dr. Jin Sa for the insightful discussion about the thesis and the project. I want to thank Jin for personally supporting me in settling down in Bristol at the beginning of my study, which makes the life much easier. I would like to thank the director of the Bristol Robotics Laboratory, Prof. Chris Melhuish for providing an extremely friendly and stimulating research environment. I would like also to thank all the colleagues in the lab for all the suggestions and kindless help during last three years. A special thank goes to Jan Dyre Bjerknes for the useful and helpful discussion in swarm robotics, and for his organisation of all kinds of parties and activities.

In a traditional choreography a choreographer determines the motions of a dancer or a group of dancers. Information theory shows that there is a limit to the complexity that can be created in any given amount of time. This is true even when building on previous work, since movements and their interactions have to be communicated to the dancers. When creating a group work, choreographers circumvent this problem by focusing either on the movements of individual dancers (giving rise to intricate movements but within a simple spatiotemporal organization) or on the overall structure (intricate patterns but simple movements) or by creating room for the dancers to fill in part of the movements. Complexity theory offers a different paradigm towards the generation of enticing patterns. Flocks of birds or schools of fish for instance are considered ‘beautiful ’ but lack a central governing agent. Computer simulations show that a few simple rules can give rise to the emergence of the kind of patterns seen in flocks or swarms. In these models individual agents are represented by dots or equivalent shapes. To be of use to choreography and to be implemented on or rather with dancers, some additional rules will therefore have to be introduced. A number of possible rules are presented, which were extracted from ‘real life ’ experiments with dancers. The current framework for modeling flocking behavior, based on local interactions between single agents, will be extended to include more general forms of interaction. Dancers may for instance perceive the global structure they form, e.g. a line or a cluster, and then put that knowledge to creative use according to some pre-established rules, e.g. if there is a line, form a circle or if there is a cluster spread out in all directions. Some of these rules may be applied back to other complex systems. The present paper is also an invitation to complexity theorists working in different fields to contribute additional rules and ideas. 1

ABSTRACT Pattern formation in 3D random media has been a topic of interest in soft matter and biological systems. However, the onset of long-range microscopic ordering has not been explored in randomly moving self-propelled particles due to a lack of model systems as well as local probe techniques. In this article, we report on a novel experiment, using motile Escherichia coli bacteria as a model system, to study the onset of dynamic correlation and collective movement in threedimension. We use fluctuation of an optically trapped micron-size bead as a detector of correlated bacterial motion, and further study this behavior by analyzing the motility of fluorescent bacteria in a confocal volume. We find evidence of dynamic correlation at very low volume fractions (0.01). We show that the magnitude of this correlation strongly depends on the interbacterial distances and their coupling modes. This opens up possibilities to probe long-range pattern formation in actively propelled cells or organisms coupled through hydrodynamics and/or chemical signaling.

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