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腥倶荀 ≪(No.2)

茯ャ紫咲ｃ †

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腱糸 †

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荀恰謙膤糸冴ャ＞散絎御帥若括 †

篋咲鐚医茵綽鐚蕭罨≦荀ュ鐚怨墾絎у熊帥鐚茯睡榊憗с鐚鐚篋羝筝順紊絎医с鴻倶膓牙腟薑筝純с鐚倶с＞散荀馹с鐚腥吟с鐚絲乗院絎筝э医紊絲障鐚鐚鐚綣莨若帥у上鐚鐚CPG＜帥紊眼у上緇ゆ糸膺肢吟篋羝≪罅鐚絖掩九勝鐚3潟ャ若帥潟荵∫九勝綽鐚
  • Toshiyuki Kondo, Takanori Somei, Koji Ito: "A predictive constraints selection model for periodic motion pattern generation", Proceedings of 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'04), TP1-K2, pp.975-980, Sendai, Japan, (2004)

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若遵堺罕罧級綣桁絖膺 †

篋咲違帥若潟絖膺鐚с峨ヨ障鐚医荅茵э倶膓牙｢膣≪鐚絖膺с鐚鐚ｻ腟薑絖膺号篏咲遵鐚篋咲莖鐚 ョ医筝ц膓荀羆緇腱糸茵絖膺鴻腓阪筝鐚綽絖J.Piaget逸篋咲茯ャ脂絖膺罘罕鐚鐚鐚鐚域宴∝ャヨ罸莠鐚鐚鐚鐚筝眼翫鐚ヨ罕篏紊眼鐚膵違菴咲鐚腥吟с鐚ョ医緇腱糸茵絖膺箴蕁鐚鐚鐚鐚九勝鐚neural network鐚NN鐚綣桁絖膺鐚TD羈鐚у膺鐚鐚鐚鐚絖膺緇NN茵荀(Schema)遵冴鐚ャョ医絖膺絖膺＞散筝э絖膺蕭с鐚鐚
  • 菴ゆ鋈, 篌ょ綵, 篌ゅ: "若遵堺罕緇腱糸罧級茵絖膺", 荐羝九勝絖篌茫, Vol.40, No.3, (2004)

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  • 絎薑
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篋咲-若吾с潟娯綽膤祉膓膓ゃ潟帥激с潟絎 †

篋咲篋阪轡筝睡綽ц蟹綽с鐚菴綛器綽腮叵＜冴鐚若吾с潟眼緇篋咲絲障荀с茯茘ゃゃ鐚鐚篋咲若吾с潟娯綽膓膓娯篏筝罨с鐚憟吟с篋咲眼吾莢紊с鐚鐚腥吟с篋咲若吾с潟≫膓膓若吾с潟鴻罘醇＞散茹ｆ腟鐚￥篋咲蕋純＜冴茹ｆ鐚蕋純娯篏荀靊ゃ絲鐚
  • 菴ゆ鋈, ユ乗箙, 篌ゅ: "篋咲-若吾с潟娯綽膤祉膓膓娯篏絎憗罘醇＞散", 若吾с潟若激с鐚激潟吾2003茗羲茫, pp.432-437, 羞∴君紊∵伾初篌茘医, 球昆, (2003)
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荐膊≪ †

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峨recruitャ綣桁絖膺羈 †

茲逸蕭罨≦鐚祉潟球ュ腱糸鐚怨篏若違帥鴻緇峨荅帥障鐚潟潟若NGnetф鐚倶腥咲莇罐篏у蚊違障鐚鐚倶蚊膣違逸阪∽逸RBF鐚違紊с荐膊莢激紊с障鐚筝刻膕蚊鐚綽荀倶蚊с絖膺蚊帥障鐚腥吟с鐚NGnetActor-Critic綣桁絖膺冴ャ茵絖膺茵鐚筝鐚倶駕RBF紊с鐚荅箴＜峨recruitmentワevolutionary recruitment strategy鐚罅障鐚
  • Toshiyuki Kondo, Koji Ito: "A Study on Designing Robot Controllers by Using Reinforcement Learning with Evolutionary State Recruitment Strategy", Lecture Notes in Computer Science 3141 -Biologically Inspired Approaches to Advanced Information Technology: First International Workshop BioADIT 2004 Lausanne Switzerland January 29-30 2004 Revised Selected Papers, Springer -Verlag Berlin Heideberg, pp.244-257, (2004)
  • Toshiyuki Kondo, Koji Ito: "A Reinforcement Learning with Evolutionary State Recruitment Strategy for Autonomous Mobile Robots Control", Journal of Robotics and Autonomous Systems, vol.46, no.2, pp.111-124 Elsevier, (2004)
  • 菴ゆ鋈, 篌ゅ: "峨recruitmentャ綣桁絖膺緇腱糸九勝荐荐", 荐羝九勝絖篌茫, Vol.39, No.9, pp.857-864, (2003)

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綛合完榊≪ャ若≪吾ャ若帥≪緇腱糸綵∽紫腟膤祉演峨綵∽ †

LEGO(TM) Robot Project targets evolving a suitable body shape and its nervous system (i.e. artificail neural network-based controller) for a light seeking robot. Using a grammar-based gene encoding method, we can save computational resources (length of genotype), in other words, it enables to evolve much faster than ordinary coding methods. In the current situation, I revised "dynamically-rearranging neural networks" method much more useful. For example, instead of each synapse, each neuron has its own NM interpretation table, and then each synapse originated from same neuron uses the interpretation table. In addition, effective area of each NM is restricted within some small range (this is genetic parameter). It is expected this enables the emerged network to have more complicated structure and flexibility, redundancy. In this study, we are concerned with the interaction between three specific adaptive systems: evolutionary change by species, ontogenic change by an individual as it matures; and learning by the individual as it acquires experience. We present experiments in which a population of individuals, each grown from a single cell according to its particular genome, into an adult form corresponding to a simple robot body and NNet which allows it to function in and learn about its environment.
  • Submitted to 7th Joint Symposium on Neural Computation 7 Apr 00
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膩罘純腑腟莊≪ †

Recently, Evolutionary Robotics approach has been attracting a lot of concerns in the field of robotics and artificial life. In this approach, neural networks are widely used to construct controllers for autonomous mobile agents, since they intrinsically have generalization, noise-tolerant abilities and so on. However, the followings are still open questions; 1) gap between simulated and real environments, 2) evolutionary and learning phase are completely separated, and 3) conflict between stability and evolvability/adaptability. In this article, we try to overcome these problems by incorporating the concept of dynamic rearrangement function of biological neural networks with the use of neuromodulators.
  • 菴ゆ鋈, 渇腴紊, 綏罔, Peter Eggenberger: "峨ｃ鴻九勝ユс絎" - 膩罘純腑腟莊≪罅 -, 荐羝九勝絖篌茫, Vol.35, No.11, pp.1407-1414, (1999)
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篏膤祉冴ャ窮阪綽≪ †

Conventional arti.cial intelligent (AI) system have been criticized for its brittleness under hostile/dynamic changing environments. Therefore, recently much attention has been focused on the reactive planning systems such as behavior-based AI. However, in the behavior-based AI approaches, how to construct a mechanism that realizes adequate arbitration among competence modules is still an open question. In this paper, we propose a new decentralized consensus-making system inspired from the biological immune system. And we apply our proposed method to behavior arbitration of an autonomous mobile robot as a practical example. To verify the feasibility of our method, we carry out some experiments.In addition, we propose an adaptation mechanism, and try to construct a suitable immune network for adequate action selection.
  • 菴ゆ鋈, 渇腴紊, 綏罔: "篏膤祉緇腱糸茵茯水罘罕窮榊≪筝羈", 荐羝九勝絖篌茫, Vol.35, No.2, pp.262-270, (1999)