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Learning and Evolution of Intelligent Systems Composed of Multi-individuals Interacting with Each Other

多个体交互组成的智能系统的学习与进化

基本信息

批准号：
14350212
负责人：
HIRASAWA Kotaro
金额：
$ 9.09万
依托单位：
Waseda University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2002
资助国家：
日本
起止时间：
2002 至 2004
项目状态：
已结题

项目摘要

Intelligent system consisting of multi-individuals interacting between each other have been developed and the learning and evolution of the system have been studied. It is shown that the proposed system has better performance than conventional methods.1.Intelligent agents : Genetic Network Programming (GNP) has been developed as intelligent agents, which represents solutions using directed graph structures, and showed better performance than the conventional method in creating agent behavior.2.Interaction between agents : In order to create intelligent interaction between agents composed of GNP, Multi-Agent Systems with Symbiotic Learning and Evolution (Masbiole) has been developed, which applied symbiotic relations in the ecosystem such as Mutualism, Predation, Competition and Altruism. From the results of the simulations using Tileworld problem, it is clarified that the proposed method shows complex interaction between agents by introducing the symbiotic strategies.3.Learning and Evolution in Multi-agent systems : Generally, living things has been developing through evolution and learning. Evolution has been done for a long period of time, and learning is done based on trial-and-error during lifetime of each individual. Based on this idea, a GNP algorithm has been developed by -using evolution and reinforcement learning. This method can automatically create programs which show better performance than the GNP based on evolution only and the conventional evolutionary computation (Genetic Programming) because the proposed method can improve the programs during task execution (online learning) in addition to the evolution executed after task execution.

由多个个体相互作用组成的智能系统已经被开发出来，并研究了系统的学习和进化。结果表明，所提出的系统具有更好的性能比传统的方法。1.智能代理：遗传网络编程（GNP）已被开发为智能代理，它表示使用有向图结构的解决方案，并表现出更好的性能比传统的方法在创建代理的行为。为了在由GNP组成的Agent之间建立智能交互，提出了具有共生学习和进化的多Agent系统（Masbiole），它应用了生态系统中的共生关系，如互惠、捕食、竞争和利他。从模拟结果使用瓷砖世界的问题，它是澄清，该方法显示了复杂的互动代理之间通过引入共生策略。3.多智能体系统中的学习和进化：一般来说，生物已经通过进化和学习的发展。进化已经进行了很长一段时间，学习是基于每个人一生中的试错。基于这一思想，GNP算法已经开发出-使用进化和强化学习。这种方法可以自动创建程序，表现出更好的性能比GNP的基础上进化和传统的进化计算（遗传编程），因为所提出的方法可以改善程序在任务执行（在线学习）除了执行任务后执行的进化。