腥倶荀

<宴2006綛岩札с鐚 憜腥倶荀ゃ鐚腥九HP荀т鐚

茯ャc

ョ医茯ャ腑腟篆蕋上莊膓蚊≪

篏篌吾違絎号ゃс鐚鐚絎憗腮鐚荀荀膤祉篏ヨ鐚膓画糸篏篏臀≫荐膊鐚腟薑冴ャ篏莖篋羝鐚罔荵鐚罔荵絎憗膈綣球九勝膈鐚茲怨綽荀鐚障鐚怨罘罕鐚緇鐚医娯篏緇峨鐚腥吟с鐚腑腟莊≪鐚g潟ャ若若鐚鐚ャ荵∝у器綺罸箴紊画劫90綺荵≪劫医鐚筝т√育絎憗膈九勝絖膺鐚腑腟莊≪膩綵≪c若篁絎э医紊ャ九勝罕峨腆肴鐚

  • Toshiyuki Kondo, Koji Ito: A Neuromoduratory Neural Networks Model for Environmental Cognition and Motor Adaptation, Proceedings of IEEE World Congress on Computational Intelligence (WCCI2006), Vancouver, Canada, pp.9865-9870 (2006)

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鐚九勝絲乗院医紊絲障鐚鐚鐚鐚膩綵∽絖ゅ莨若推綽帥若潟鐚筝刻綣莨若帥у上育c紊с医紊絲障鐚鐚鐚鐚医篋羝≪冴ャ絖<帥緇帥у上鐚篋綽c若若帥若括≪罅鐚絖掩九勝鐚3潟ャ若帥潟荵∫九勝綽鐚

  • 菴ゆ鋈, 篌ゅ, >散絎御, 荐羝九勝, vol.44, no.9, pp.596-601, (2005)
  • 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)

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ゃ障倶膓牙「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)

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)

荐膊≪

峨recruitャ綣桁絖膺羈

茲逸蕭罨≦鐚祉潟球ュ腱糸鐚怨篏鐚逸若違帥鴻緇峨荅帥鐚鐚祉潟泣若≪若翠≫鐚<潟潟若鐚NGnetц篌若鐚NGNetс倶腥咲莇罐篏у蚊違鐚鐚倶蚊膣違逸阪∽逸RBF鐚違紊с荐膊莢激紜鐚筝刻膕蚊鐚綽荀倶蚊с絖膺蚊障鐚

腥吟с鐚NGnetActor-Critic綣桁絖膺冴ャ茵絖膺茵鐚筝倶駕RBF紊с鐚荅箴<峨recruitmentワevolutionary recruitment strategy鐚罅鐚

  • 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)

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

膩罘純腑腟莊≪鐚ャ若≪吾ャ若帥ャ若若鐚

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.

  • Toshiyuki Kondo, Evolutionary design and behavior analysis of neuromodulatory neural networks for mobile robots control, Applied Soft Computing, Elsevier, in press
  • 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)

篏膤祉冴ャ窮阪綽≪

Conventional artificial intelligent (AI) 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 proposed 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 proposed 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)

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