Multi-agent system description programming language system based on workstation cluster type parllel computer system

基于工作站集群式并行计算机系统的多Agent系统描述编程语言系统

基本信息

批准号：
06680358
负责人：
KANEDA Yukio
金额：
$ 1.41万
依托单位：
Kobe University
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (C)
财政年份：
1994
资助国家：
日本
起止时间：
1994 至 1995
项目状态：
已结题

项目摘要

Multi-agent system is drawing attention as a structural model for many software systems including artificial intelligence systems. In multi-agent system, a number of autonomous agents cooperates mutually. Each agent is generated as a process or a task and can have its own situation and operates under the situation.Situation consists of states and procedures where both of them can be dynamically changed in general. Therefore, it becomes necessary for the agent to dynamically hold the states (data) and procedures (hereafter we refer them as a knowledge base).Moreover, when a number of agents are cooperating, it is necessary for them to share knowledge bases and conduct knowledge communications between agents. We proposed a computation model for cooperative agents. In this model, we introduce a field as an abstraction of the group of agents. Each agent can communicate to the other agents in the same group through a field. The field also enables us to specify the action of the group, i.e., … More it is used for a knowledge base to keep rules and data shared by agents in the group. Based on this model, programming language which is based on Prolog was designed and its prototype processing system was built on the network connected distributed multi-workstation system. We also developed a parallel processing system for analyzing phylogenetic relationships of mircroorganisms based on a maximum likelihood method. Methods for inferring relationships from molecular sequence data are especially valuable, given the enormous increases in DNA sequence data. The maximum likelihood method uses concrete models of the evolutionary process and are well-motivated statistically, but the computational cost has hindered the use of this method for inferring trees with more than about 20 organisms. We parallelized the maximum likelihood method by utilizing two types of parallelism, parallel evaluation of phylogenetic trees and parallel computation of likelihood values. By combining these two parallelisms, we obtained significant speedup. Less

多代理系统将注意力吸引为许多软件系统（包括人工智能系统）的结构模型。在多代理系统中，许多自主代理会相互协调。每个代理都是作为过程或任务生成的，可以具有自己的情况并在这种情况下运行。定点由状态和程序组成，其中两者通常都可以动态更改它们。因此，代理必须动态地持续（数据）和程序（以下称它们为知识库）。此外，当许多代理人合作时，他们有必要共享知识库并进行代理之间的知识通信。我们提出了合作代理商的计算模型。在此模型中，我们将一个领域引入了一组代理的抽象。每个代理都可以通过字段与同一组的其他代理进行通信。该领域还使我们能够指定组的动作，即……更多，它用于知识库来保留组中代理共享的规则和数据。基于此模型，设计了基于Prolog的编程语言，其原型处理系统是在网络连接的分布式多工作系统系统上构建的。我们还开发了一种平行处理系统，用于基于最大似然方法来分析麦克罗生物的系统发育关系。考虑到DNA序列数据的增加，从分子序列数据中推断关系的方法特别有价值。最大似然方法使用了进化过程的具体模型，并且在统计学上取得了良好的动机，但是计算成本阻碍了该方法用于推断具有大约20种生物体的树木。我们通过使用两种并行性，系统发育树的并行评估以及可能性值的平行计算来平行最大似然法。通过结合这两个并行性，我们获得了显着的加速。较少的