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Proceedings of AISB'06: Adaptation in Artificial and Biological Systems Proceedings of AISB’06: Adaptation in Artificial and Biological Systems Tim Kovacs and James A. R. Marshall (eds.) ISBN 1 902956 97 5 – Volume 1 ISBN 1 902956 98 3 – Volume 2 ISBN 1 902956 96 7 – Volume 3 Published by the Society for the Study of Artificial Intelligence and the Simulation of Behaviour Printed by the University of Bristol, Bristol, UK Contents Foreword I-iii Tim Kovacs and James A. R. Marshall Plenary Talks Evolutionary Design of Complex Chemical Systems – 3rd April I-v Mark A. Bedau Life and Mind: A Union without Divorce? – 4th April I-vi Maggie Boden From Individual to Collective Intelligence – 5th April I-vii Nigel R. Franks Artificial Consciousness and the Simulation of Behaviour – 6th April I-viii Owen Holland Symposia Artificial Immune Systems and Immune System Modelling I-1 Associative Learning and Reinforcement Learning I-22 Integrative Approaches to Machine Consciousness I-107 Motor Development I-174 Biologically Inspired Robotics (Biro-net) II-68 Grand Challenge 5: Architecture of Brain and Mind II-1 Nature-Inspired Systems for Parallel, Asynchronous and Decentralised Environments II-145 Exploration vs. Exploitation in Naturally Inspired Search II-178 Narrative AI and Games III-1 Network Analysis in Natural Sciences and Engineering III-86 Social Insect Behaviour: Theory and Applications III-199 Author Index I-203, II-210, III-210 GC5: Architecture of Brain and Mind Integrating High Level Cognitive Processes with Brain Mechanisms and Functions in a Working Robot 3rd - 4th April 2006 Organisers Aaron Sloman, University of Birmingham Invited Speakers Jackie Chappell, Univ. of Birmingham Peter Redgrave, Sheffield University Mike Denham, Plymouth University Murray Shanahan, Imperial College Steve Furber, Manchester University London Jeffrey Krichmar, The Neurosciences Aaron Sloman, University of Birmingham Institute Mark Steedman, University of Edinburgh Mark Lee, Univ. of Wales Aberystwyth Tom Ziemke, University of Skövde Contents Abstracts for Invited Speakers 2 Summaries of Posters 6 Introduction to Symposium GC5: Architecture of Brain and Mind – Integrating High Level Cognitive Processes with Brain Mechanisms and Functions in a Working Robot 8 Aaron Sloman How do Animals Gather Useful Information about their Environment and Act on it? 14 Jackie Chappell A Novel Computing Architecture for Cognitive Systems based on the Laminar Microcircuitry of the Neocortex – The COLAMN Project 23 Michael Denham High-Performance Computing for Systems of Spiking Neurons 29 Steve Furber, Steve Temple and Andrew Brown Principles Underlying the Construction of Brain-Based Devices 37 Jeffrey L. Krichmar and Gerald M. Edelman Emotion as an Integrative Process between Non-symbolic and Symbolic Systems in Intelli- gent Agents 43 Ruth Aylett Embodiment vs. Memetics: Is Building a Human getting Easier? 48 Joanna Bryson Requirements & Designs: Asking Scientific Questions About Architectures 52 Nick Hawes, Aaron Sloman and Jeremy Wyatt Integration and Decomposition in Cognitive Architecture 56 John Knapman On the Structure of the Mind 60 Maria Petrou and Roberta Piroddi Social Learning and the Brain 64 Mark A. Wood 1 1 Invited speakers Mike Denham Plymouth University Jackie Chappell http://www.plymouth.ac.uk/pages/dynamic.asp The University of Birmingham, UK ?page=staffdetails.id=mdenham.size=l http://users.ox.ac.uk/.kgroup/jackie.html The role of the neocortical laminar microcircuitry How do animals gather useful information about in perception, cognition, and consciousness their environment and act on it? Paper online (PDF) Slides online (PDF) Abstract Abstract The talk will focus on the objectives of the EPSRC- Animals are much more successful than current and EU-funded projects I am involved in, including robots in their ability to gather information from the the role of the neocortical laminar microcircuitry in environment, detect affordances, attribute causes to perception, cognition, and (dare I say it) conscious- affects, and sometimes generate individually novel ness. Of particular interest is the question of how the behaviour. What kinds of mechanisms might make brain uses context to modify perceptual awareness, as this possible? I will discuss different mechanisms for illustrated for example by visual illusions. acquiring information in animals, and their strengths and weaknesses given different life histories and Steve Furber niches. I will discuss experiments which have at- tempted to uncover the extent of animals’ abilities Manchester University to use information from their environment, and the http://www.cs.manchester.ac.uk/apt/people/sfurber/ mechanisms that might be used to accomplish this. High-Performance Computing for Systems of The development of these kinds of competences (in Spiking Neurons evolutionary time and over the course of an individ- Paper online (PDF) ual’s lifetime) is another interesting problem. Explo- Abstract ration and play seem to be very important for some We propose a bottom-up computer engineering ap- kinds of behaviour, particularly flexible responses to proach to the Grand Challenge of understanding the novel problems, but there is also the possibility that Architecture of Brain and Mind as a viable com- animals come equipped with certain kinds of ’core plement to top-down modelling and alternative ap- knowledge’, which might help to direct and structure proaches informed by the skills and philosophies of the acquisition of more complex competences. other disciplines. Our approach starts from the ob- [1] R. C. Barnet, R. P. Cole, and R. R. Miller. Tempo- servation that brains are built from spiking neurons ral integration in second-order conditioning and sen- and then progresses by looking for a systematic way sory pre- conditioning. Animal Learning and Behav- to deploy spiking neurons as components from which ior, 25:221–233, 1997. useful information processing functions can be con- [2] J. Chappell and A. Kacelnik. New Caledonian structed, at all stages being informed (but not con- crows manufacture tools with a suitable diameter for strained) by the neural structures and microarchitec- a novel task. Animal Cognition, 7:121–127, 2004. tures observed by neuroscientists as playing a role in [3] S. E. Cummins-Sebree and D. M. Fragaszy. biological systems. In order to explore the behaviours Choosing and using tools: Capuchins (Cebus apella) of large-scale complex systems of spiking neuron use a different metric than tamarins (Sanguinus components we require high-performance computing oedipus). Journal of Comparative Psychology, equipment, and we propose the construction of a ma- 119(2):210–219, 2005. chine specifically for this task - a massively parallel [4] M. Domjan and N. E. Wilson. Specificity of cue computer designed to be a universal spiking neural to consequence in aversion learning in the rat. Psy- network simulation engine. chonomic Science, 26:143–145, 1972. [5] M. Hayashi and T. Matsuzawa. Cognitive devel- opment in object manipulation in infant chimpanzees. Animal Cognition, 6:225–233, 2003. [6] A. A. S. Weir, J. Chappell, and A. Kacelnik. Shap- ing of hooks in New Caledonian crows. Science, 297(9 August 2002):981, 2002. 2 Jeffrey L. Krichmar From Biological Systems to Neuromimetic Robotics, The Neurosciences Institute, San Diego, USA G.N. Reeke, et al., Editors. 2005, Taylor & Francis: http://www.nsi.edu/users/krichmar/ Boca Raton. p. 613-638. Brain-based devices for the study of nervous sys- tems and the development of intelligent machines. Mark Lee Paper online (PDF) University of Wales, Aberystwyth Abstract http://users.aber.ac.uk/mhl/ Without a doubt the most sophisticated behavior seen Developmental Robotics: an emerging paradigm in either biological or artificial agents is demonstrated for intelligent agents. by organisms whose behavior is guided by a nervous system. Thus, the construction of behaving devices Abstract based on principles of nervous systems may have Much research has explored the issues involved in much to offer. Our group has built series of brain- creating truly autonomous embodied learning agents based devices (BBDs) over the last 14 years to pro- but only recently has the idea of a developmental vide a heuristic for studying brain function by em- approach been investigated as a serious strategy for bedding neurobiological principles on a physical plat- robot learning. This is now emerging as a vibrant new form capable of interacting with the real world. These research area. We examine the goals and methods of BBDs have been used to study perception, operant Developmental Robotics, and assess the current state conditioning, episodic and spatial memory, and motor of play. We give some requirements for a develop- control through the simulation of brain regions such mental system and relate these to the UK Grand Chal- as the visual cortex, the dopaminergic reward system, lenge 5 (The architecture of mind and brain) in terms the hippocampus, and the cerebellum. Following the of design issues for future robotic systems. brain-based model, we argue that an intelligent ma- chine should be constrained by the following design Peter Redgrave principles[1, 2]: (i) it should incorporate a simulated brain with detailed neuroanatomy and neural dynam- Dept of Psychology, Sheffield University ics that controls behavior and shapes memory, (ii) http://www.shef.ac.uk/psychology/staff/academic/ it should organize the unlabeled signals it receives peter-redgrave.html from the environment into categories without a priori Is it just a question of priority? Inspiration
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