Xivth International Conference on Parallel Problem Solving from Nature
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Josh C Bongard Curriculum Vitae
Josh C Bongard Curriculum Vitae E428 Innovation Hall University of Vermont, Burlington, VT 05405 Personal website: jbongard.github.io Lab website: www.meclab.org Twitter: @DoctorJosh [email protected] Google Scholar Profile RESEARCH There are two main thrusts in my research group: evolutionary robotics and machine science. STATEMENT In our evolutionary robotics work, our long-term goal is to enable increasingly large numbers of non-experts to direct the evolution of increasingly complex, capable, and autonomous machines to perform a widening array of difficult tasks. This involves work in theoretical biology: what mechanisms from biological evolution must be incorporated into an evolutionary robotics system to increase its evolvability? It involves work in embodied cognition: how does one spread ‘computation’ out across not only the neural controller of the machine, but also its morphology? It involves work in computational neuroscience: what kinds of neural models should be instantiated in our evolving robots? Finally, our work has psychological and philosophical implications: if a robot evolves that exhibits the behavioral manifestations of high-level cognitive competencies such as self-awareness, how can we determine whether a robot actually possesses, rather than just simulates, that competency? In our work on machine science, we attempt to automate as best we can all aspects of the scientific process: hypothesis generation, selection of phenomenon to measure, experimental design, and data collection. (Machine learning, in contrast, only focuses on hypothesis generation.) My overall strategy is to select generally high-risk, high-reward research questions that, if validated, are likely to change our thinking about seemingly obvious approaches, such as focusing only on control, rather than both morphology and control, in robotics. -
In This Issue
Volume 8 SIGEVOLution Issue 3 newsletter of the ACM Special Interest Group on Genetic and Evolutionary Computation in this issue Evolutionary Art The Evolution of Things Book Reviews Upcoming Events and CFPs PhD Opportunities Photograph from the Santa Fe Institute EDITORIAL This issue has somewhat of a creative theme. The front cover showcases an art work generated by the Unplugged Evolutionary Algorithm, a project that combines Artistic Expression and Computer Science by applying evolutionary algorithms in order to study the processes that influence artistic creation. Our feature article , written by Prof. A. Eiben and Prof. J. Smith, is a summary of their recent Nature article, published in May 2015, and discusses how Artificial Evolution can be used to create new objects, bypassing the need for a human designer. Such high-profile coverage of Evolutionary Computing is great news for the field in general. On the same theme, a new book by Kenneth Stanley and Joel Lehman (Why Greatness Cannot be Planned: The Myth of the Objective), previewed on page 7 adds further weight to the argument that biological evolution, science, and human culture are creative, endlessly innovative processes. The issue also provides a précis of another recent book, written by Mike Preuss, on the subject of Multi-Objective Optimisation by Means of Evolutionary Algorithms. Xiaodong Li of RMIT University, Melbourne, kindly provided a review – it seems clear that the book will provide an excellent and comprehensive resource for researchers and practitioners working in this area. More new research is highlighted in two recent Special Issues from ECJ and GPEM, on the topics of Theory of Evolutionary Algorithms and Semantic Methods in GP respectively. -
ADAPT – UNIZH Past-Present
ADAPT – UNIZH Past-Present Morphology, Materials, and Control “Developmental Robotics” Rolf Pfeifer, Gabriel Gomez, Martin Krafft, Geoff Nitschke, NN Artificial Intelligence Laboratory Department of Information Technology University of Zurich Switzerland © Rolf Pfeifer Artificial Intelligence Laboratory Department of Information Technology Director PhD students Rolf Pfeifer David Andel Josh Bongard University of Zurich Post-docs Simon Bovet Daniel Bisig Raja Dravid Switzerland Peter Eggenberger Miriam Fend Hansruedi Früh Andreas Fischer Charlotte Hemelrijk Gabriel Gomez Lorenz Gygax Visitors Verena Hafner Hiroshi Yokoi (visiting prof.) Fumiya Iida Chris Jones Pascal Kaufmann Robert König Martin Krafft Thomas Uehlinger Hanspeter Kunz Jilles Vreeken Lukas Lichtensteiger Noel Verdurmen Massimiliano Lungarella Kojiro Matsushita Staff Geoff Nitschke Erina Kishida Chandana Paul Rafael Schwarzmann Dale Thomas Clausdia Wirth Jan Wantia © Rolf Pfeifer Contents • Introductory comments • Zurich AI Lab research overview • The synthetic methodology • Embodiment – illustrations • A hard problem in cognitive science: perception in the real world • The evolution of intelligence: morphogenesis • The „Zen of robot programming“ © Rolf Pfeifer Contents • Introductory comments • Zurich AI Lab research overview • The synthetic methodology • Embodiment – illustrations • A hard problem in cognitive science: perception in the real world • The evolution of intelligence: morphogenesis • The „Zen of robot programming“ © Rolf Pfeifer The cognitivistic paradigm cognition