The Evolution of Reaction-Diffusion Controllers for Minimally Cognitive Agents

Dale, Kyran; Husbands, Phil

doi:10.1162/artl.2009.16.1.16100

Cited by 35 publications

(31 citation statements)

References 44 publications

(52 reference statements)

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“…Such a mood triggers or modulates several pre-programmed and hand-coded behavioral controllers. Most similarities to our approach might be discovered in [13]. However, the number of cells or compartments, for example, differs by the order of magnitudes (we use less than 10 compared to 128) which will arguably result in qualitatively different processes.…”

Section: Discussionmentioning

confidence: 99%

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A hormone-based controller for evolutionary multi-modular robotics: From single modules to gait learning

Hamann

Stradner

Schmickl

2010

IEEE Congress on Evolutionary Computation

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Section: Discussionmentioning

confidence: 99%

“…Alternative and promising approaches, that are similar to the approach proposed in this article, are artificial homeostatic systems [11], [12] and the diffusion-reaction controller approach [13].…”

Section: Introductionmentioning

confidence: 99%

A hormone-based controller for evolutionary multi-modular robotics: From single modules to gait learning

Hamann

Stradner

Schmickl

2010

IEEE Congress on Evolutionary Computation

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“…RD controllers for robots were evolved in [5], based on the Gray-Scott system in one dimension (ring). Chemicals in the RD ring were used to activate the robot's motors in response to sensor input.…”

Section: Evolving Reaction-diffusion Solutionsmentioning

confidence: 99%

“…The potential of RD in micro-and nanotechnology is reviewed in [11], such as the fabrication of structures and devices at very small scales. RD can also be used in models of distributed computation inspired by chemistry, with applications to sensor networks (role assignment [22], routing [15]), and robotics (robot controllers [5], swarm robotics [25]). …”

Section: Introductionmentioning

confidence: 99%

Evolving Reaction-Diffusion Systems on GPU

Yamamoto

Banzhaf

Collet

2011

Progress in Artificial Intelligence

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Abstract. Reaction-diffusion systems contribute to various morphogenetic processes, and can also be used as computation models in real and artificial chemistries. Evolving reaction-diffusion solutions automatically is interesting because it is otherwise difficult to engineer them to achieve a target pattern or to perform a desired task. However most of the existing work focuses on the optimization of parameters of a fixed reaction network. In this paper we extend this state of the art by also exploring the space of alternative reaction networks, with the help of GPU hardware. We compare parameter optimization and reaction network optimization on the evolution of reaction-diffusion solutions leading to simple spot patterns. Our results indicate that these two optimization modes tend to exhibit qualitatively different evolutionary dynamics: in the former, the fitness tends to improve continuously in gentle slopes, while the latter tends to exhibit large periods of stagnation followed by sudden jumps, a sign of punctuated equilibria.

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“…Therefore, using concepts from Evolutionary Robotics (Harvey et al, 2005;Floreano & Mattiussi, 2008;Floreano & Keller, 2010), we explore the role of synchronisation in the performance of a simulated robotic agent during the execution of two different minimally cognitive tasks: the first, a categorical perception task (Beer, 2003;Izquierdo, 2008;Dale & Husbands, 2010), in which the robot has to discriminate between moving circles and squares; the second, an orientation task, where the robotic agent has to approach moving circles with both normal and inverted vision, adapting to both conditions. These tasks were chosen for being currently regarded as benchmarks in the evolutionary robotics and adaptive behaviour communities, with categorical perception underpinning cognitive systems (Harnad, 1987).…”

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confidence: 99%

Synchronisation effects on the behavioural performance and information dynamics of a simulated minimally cognitive robotic agent

2012

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Article (Accepted Version) http://sro.sussex.ac.uk Moioli, Renan C, Vargas, Patricia A and Husbands, Phil (2012) Synchronisation effects on the behavioural performance and information dynamics of a simulated minimally cognitive robotic agent. Biological Cybernetics, 106 (6-7). pp. 407-427. ISSN 0340-1200 This version is available from Sussex Research Online: http://sro.sussex.ac.uk/40960/ This document is made available in accordance with publisher policies and may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the URL above for details on accessing the published version. Copyright and reuse:Sussex Research Online is a digital repository of the research output of the University.Copyright and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable, the material made available in SRO has been checked for eligibility before being made available.Copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. Noname manuscript No. (will be inserted by the editor)Synchronisation effects on the behavioural performance and information dynamics of a simulated minimally cognitive robotic agent Renan C. Moioli, Patricia A. Vargas, Phil Husbands Abstract Oscillatory activity is ubiquitous in nervous systems, with solid evidence that synchronisation mechanisms underpin cognitive processes. Nevertheless, its informational content and relationship with behaviour are still to be fully understood. Additionally, cognitive systems cannot be properly appreciated without taking into account brain -body -environment interactions. In this paper, we developed a model based on the Kuramoto Model of coupled phase oscillators to explore the role of neural synchronisation in the performance of a simulated robotic agent in two different minimally cognitive tasks. We show that there is a statistically significant difference in performance and evolvability depending on the synchronisation regime of the network. In both tasks, a combination of information flow and dynamical analyses show that networks with a definite, but not too strong, propensity for synchronisation are more able to reconfigure, to organise themselves functionally and to adapt to different behavioural conditions. The results highlight the asymmetry of information flow and its behavioural correspondence. Importantly it also shows that neural synchronisation dynamics, when suitably flexible and reconfigurable, can generate minimally cognitive embodied behaviour.

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The Evolution of Reaction-Diffusion Controllers for Minimally Cognitive Agents

Cited by 35 publications

References 44 publications

A hormone-based controller for evolutionary multi-modular robotics: From single modules to gait learning

A hormone-based controller for evolutionary multi-modular robotics: From single modules to gait learning

Evolving Reaction-Diffusion Systems on GPU

Synchronisation effects on the behavioural performance and information dynamics of a simulated minimally cognitive robotic agent

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