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

多智能体系统作为包括人工智能系统在内的许多软件系统的结构模型正受到人们的关注。在多智能体系统中，多个自治智能体相互协作。每个智能体被生成为一个进程或一个任务，可以有自己的情况，并在这种情况下运行。情况由状态和过程组成，它们通常都可以动态改变。因此，代理需要动态地保存状态(数据)和过程(以下称为知识库)，并且当多个代理协作时，它们需要共享知识库并在代理之间进行知识通信。提出了一种协作智能体的计算模型。在这个模型中，我们引入了一个字段作为智能体组的抽象。每个代理可以通过一个字段与同一组中的其他代理通信。该字段还使我们能够指定组的操作，即…此外，它还用于知识库，以保持组中代理共享的规则和数据。在此模型的基础上，设计了基于PROLOG的程序设计语言，并在网络连接的分布式多工作站系统上建立了原型处理系统。我们还开发了一个基于最大似然法的微型生物系统发育分析的并行处理系统。考虑到DNA序列数据的巨大增长，从分子序列数据推断关系的方法特别有价值。最大似然法使用进化过程的具体模型，在统计上是有良好动机的，但计算成本阻碍了这种方法用于推断含有大约20个生物的树。我们利用系统发育树的并行计算和似然值的并行计算两种类型的并行性，实现了极大似然方法的并行化。通过将这两个并行性结合起来，我们获得了显著的加速比。较少

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