A study of distributed intelligent systems for creation of future social welfare infrastructure

创建未来社会福利基础设施的分布式智能系统研究

• 批准号: 18560401
• 负责人: HAMAGAMI Tomoki
• 金额: $ 1.91万
• 依托单位: Yokohama National University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2006
• 资助国家: 日本
• 起止时间: 2006 至 2007
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-18560401/
• 关键词: systems engineering; information system; artificial intelligence; intelligent robotics; intelligent machine; 知能ロボティックス

For creation of future social welfare infrastructure, fundamental studies of distributed intelligence, evaluations with simulation experiments, and feasibility studies of applications have been developed. The state of the art of this study is that the intelligent algorithm can develop dependable and low cost devices appropriate for the component of welfare and social systems. In order to realize these intelligent systems, this study proposes a new reinforcement learning algorithm, approaches with multi agent design, and implements of new distributed intelligent applications. The proposed methods and techniques enable agents to acquire autonomously adaptive behavior in several task according to environments, and to construct adaptive state space in spite of sensor limitations for efficient learning. Specifically the following main outcomes have been brought from this study.(1) A new method of constructing effective state space with low dependence on sensor configurations is conducted. T … More he method consists of abstracting surrounding environment by rectangles, and generalization of state space by classifying with self organization map(SOM). Simulation experiments show that the state space that has been constructed can be reused for the agent with another sensor configuration.(2) Q-learning with action values in complex numbers is proposed to overcome agent's learning under partially observable Markov decision processes environments. It is expected that the technique enables an agent to acquire context-dependent behaviors similar to complex-valued neural networks. The results of both experiments showed that there are possibilities for agents to learn behaviors under the POMDPs environments by using contexts.(3) A multi agent based autonomous power distribution network restoration system by using a new genetic algorithm and contract network protocol under the distributed resources is proposed. The proposed system enables us to improve the restoration performance, and to reduce computational resources and costs. The simulation results show the proposed method achieves to improve the performance under the pure distributed environment. Less

为了创造未来的社会福利基础设施，已经开发了分布式智能的基础研究，模拟实验的评估和应用的可行性研究。本研究的最新进展是，智能算法可以开发出适用于福利和社会系统组成部分的可靠且低成本的设备。为了实现这些智能系统，本研究提出了一种新的强化学习算法，采用多智能体设计方法，并实现了新的分布式智能应用。所提出的方法和技术，使代理获得自主适应性的行为，在几个任务根据环境，并构建自适应的状态空间，尽管传感器的限制，有效的学习。具体而言，本研究取得了以下主要成果。(1)提出了一种新的构造对传感器结构依赖性小的有效状态空间的方法。不 ...更多信息 该方法包括用矩形来抽象周围环境，用自组织映射（SOM）进行分类来概括状态空间。仿真实验表明，该方法构造的状态空间可以重用于具有其他传感器配置的智能体。(2)针对部分可观测马尔可夫决策过程环境下智能体的学习问题，提出了一种行为值为复数的Q-学习方法。预计该技术使智能体能够获得类似于复值神经网络的上下文相关行为。两个实验的结果表明，有可能为代理学习行为POMDPs环境下，通过使用上下文。(3)提出了一种基于多Agent的分布式资源下的配电网自治恢复系统，该系统采用一种新的遗传算法和合同网络协议。所提出的系统使我们能够提高恢复性能，并减少计算资源和成本。仿真结果表明，该方法在纯分布式环境下能够有效地提高系统的性能。少

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• 发表时间: 2008
• 期刊: 計測自動制御学会論文集 Vol.44,No2
• 作者: 澁谷長史;小南学;濱上知樹
• 通讯作者: 濱上知樹

契約ネットプロトコルを用いたマルチエージェントによる自律分散型の配電系統事故復旧方式

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• 发表时间: 2007
• 期刊: 電気学会論文誌C 128
• 作者: 澁谷長史;小南学;濱上知樹;T. Shibuya;T. Shibuya;T. Shibuya;小南 学;児玉淳一
• 通讯作者: 児玉淳一

Cooperative behavior acquisition by using local environment parameters for intelligent wheelchairs(in Japanese)

利用局部环境参数获取智能轮椅的协作行为（日语）

• 发表时间: 2007
• 期刊: Transaction on SICE Vol.44, No.2
• 作者: 澁谷長史;小南学;濱上知樹;T. Shibuya;T. Shibuya;T. Shibuya;小南 学;児玉淳一;小南学;J. Kodama;M. Kominami;M. Kominami;T. Hamagami
• 通讯作者: T. Hamagami

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• 发表时间: 2008

A New Genetic Algorithm with Diploid Chromosomes by Using Probability Decodingfor Adaptation to Various Environments(in Japanese)

一种利用概率解码的二倍体染色体适应各种环境的新遗传算法（日语）

• 发表时间: 2007
• 期刊: T.IEE Japan Vol.129-C, No.3
• 作者: 澁谷長史;小南学;濱上知樹;T. Shibuya;T. Shibuya;T. Shibuya;小南 学;児玉淳一;小南学;J. Kodama;M. Kominami;M. Kominami
• 通讯作者: M. Kominami