| Project/Area Number |
16500125
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Sensitivity informatics/Soft computing
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| Research Institution | Nagoya University |
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Principal Investigator |
ARITA Takaya Nagoya University, Graduate School of Information Science, Professor, 情報科学研究科, 教授 (40202759)
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| Project Period (FY) |
2004 – 2005
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| Project Status |
Completed (Fiscal Year 2005)
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| Budget Amount *help |
¥3,500,000 (Direct Cost: ¥3,500,000)
Fiscal Year 2005: ¥2,300,000 (Direct Cost: ¥2,300,000)
Fiscal Year 2004: ¥1,200,000 (Direct Cost: ¥1,200,000)
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| Keywords | Embodied Evolution / Theory of Mind / Multilevel Selection / Evolutionary Robotics / Genetic Algorithm / Swarm Robotics / Prisoner's Dilemma / Fitness Landscape / 進化心理学 / 誤認 / 人工生命 |
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| Research Abstract |
Evolutionary robotics is the new and active field of applying computational models of evolution to the design of autonomous robots. However, it suffers from the problems that are derived from the gap between evolutionary simulation and real robot environments. This study established the concepts of embodied evolution and evolutionary computation methods for robots, and constructed a framework for experimental environments for them. Using the framework, we developed our understanding of human minds and society from an evolutionary perspective as follows. 1) We focused on the techniques of evolutionary computation for generating players performing tasks cooperatively and conducted comprehensive evaluation for 18 candidate methods using the ultimately simplified soccer game. 2) We constructed an agent-based model for multilevel selection, in which group-level selection and individual-level selection in population are supposed to work simultaneously, with dynamic group-restructuring, and conducted evolutionary experiments using it. 3) We defined a minimal model of emotion in robots based on a behavioristic theory on human emotions, and conducted evolutionary simulations based on the definition of emotions to verify the evolutionary adaptivity with the scenario of the origin and the evolution of human emotions in mind. 4) We conducted evolutionary simulations using a model describing physically-situated agents moving around avoiding collisions in order to investigate the dynamics inherent in the mechanism of recursion in the theory of mind. 5) We constructed an evolutionary model of quantitative traits by using an extended version of Kauffman's NK fitness landscape, in which whether each phenotype is plastic or not is genetically defined and plastic phenotypes are adjusted by learning, in order to give a new insight into the benefit and cost of learning by focusing on the quantitative evolution of phenotypic plasticity under the assumption of epistatic interactions.
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