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Incomplete Information Processing and Self-organization of Deep Knowledge for Intelligent Systems

智能系统的不完全信息处理和深层知识的自组织

基本信息

批准号：
01460162
负责人：
IWAI Sosuke
金额：
$ 2.18万
依托单位：
Kyoto University
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (B)
财政年份：
1989
资助国家：
日本
起止时间：
1989 至 1991
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-01460162/
关键词：
Knowledge information processing Incomplete and Inconsistent information processing Automomous decentralized processing Learning from examples EBL Integration of partial information Notion of constraint interval fuzzy sets Decentralized cooper

项目摘要

In this study, a new modeling method is proposed, in which the human abilities to extract information selectively corresponding to his or her point of view and to organize it adaptively for the purpose of problem solving is treated. And the computer algorithms executing the problem solving processes based on the model are developed. The main results obtained are as follows :A method is presented which extracts the primitive events corresponding to knowledge representation units. Concrete cause-effect sequences. that is, ' sinarios ' are formed in their sequences. Also, the inference method to organize the deep structures of problems involved in the concrete cause-effect sequences is presented based on the representation units.The process to create the complete and consistent knowledge from the incomplete and sometimes inconsistent information from the actual world is formulated, which is based on ' order relations ' goveming the world for instance, in an apartment, Keeping dogs are prohibited is a tacit understanding, if the case of, keeping cats are prohibited is true. ').A method to transform raw information such as signals or signs from the environment into the knowledge represented as symbols is proposed, in which the notion of ' constraint interval fuzzy sets ' is introduced based on ' interval constraints ' which represent the constraints goveming the concemed problem domain. Also, the same problem is treated with the method combining the deductive Teaming EBL (Explanation-based teaming and the data teaming methods such as the neural network and the genetic algorithm techniques.A method to create the global and consistent descriptions of the system states from the partial information is presented based on the decentralized cooperative problem solving scheme by distributed sensing and processing units. Also. the same problem is now continuing to formulate as the problem of ' synagetics which ia a theory of creating order in a living thing.

在这项研究中，提出了一种新的建模方法，其中人类的能力，有选择地提取信息对应于他或她的观点，并自适应地组织它的目的，解决问题的治疗。在此基础上，开发了求解问题的计算机算法。取得的主要成果如下：提出了一种提取知识表示单元对应的原始事件的方法。具体的因果顺序。也就是说，“sinarios”是在它们的序列中形成的。提出了基于表征单元的推理方法来组织具体因果序列中问题的深层结构，阐述了从现实世界中不完整的、有时不一致的信息中生成完整的、一致的知识的过程，该过程基于“序关系”来支配世界，例如在公寓中，禁止养狗是一种默契，如果是这样的话，禁止养猫是真的。提出了一种将来自环境的信号或符号等原始信息转化为符号表示的知识的方法，该方法基于“区间约束”引入了“约束区间模糊集”的概念，“区间约束”表示了控制问题域的约束。同时，将演绎Teaming-EBL（基于推理的团队）与神经网络、遗传算法等数据团队方法相结合，提出了一种基于分布式传感器和处理单元的分布式协同问题求解方案，从部分信息中建立系统状态的全局一致描述的方法. Also.同样的问题现在继续被表述为“synagetics”的问题，synagetics是一种在生物中创造秩序的理论。