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A Study on the Emergent Strategy Acquisition in the Massively Parallel Graph-Reduction

大规模并行图约简中的应急策略获取研究

基本信息

批准号：
07680377
负责人：
TAKAI Yoshiaki
金额：
$ 1.47万
依托单位：
Hokkaido University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1995
资助国家：
日本
起止时间：
1995 至 1996
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-07680377/
关键词：
emergent computation massively parallel computation load balancing enforced learning agent リダクション

项目摘要

We have developed an interactive visual simulator for observing complex behavior of the emergent computation model. As a preliminary experiment using this simulator, we have tried to generate cooperative strategies in a multiagent game defined on a reward matrix. It is not so easy to identify emergent behavior in the multiagent game because of the accidental property that is the essence of the emergent computation. Moreover the parameters specifying the agents are very critical.We have tried to build a massively parallel graph-reduction system for lambda expressions on the visual simulator described above. To design the local transition rules for each agent is a harder work than we expected. Consequently, within the practical simulation steps, we could not find out interesting emergent behavior in the graph-reduction process by using the cooperative agents. Hence, we have partially changed our original research plan, and developed an emergent load balancing system for the massively parallel graph-reduction. In a massively parallel computation field, an autonomous agent (computation node) searches lightly-loaded nodes among its neighbors to transfer extra tasks. To apply a stochastic learning automaton to the fitness evaluation in a genetic algorithm, we have achieved highly adaptive load balancing with a dynamically changing load environment.We have implemented a prototype of the emergent load balancing system on the massively parallel computer SR2201. By using independent virtual tasks with no file access, we have found well load balancing emerged in the parallel computation field. Clusters of nodes for the task transfer is built up in a self-organized way. This is a distinguished feature of the emergent computation model. The load balancing in the massively parallel computation field is one of the promising applications of the emergent computation.

我们已经开发了一个交互式的视觉模拟器观察复杂的行为的紧急计算模型。作为一个初步的实验，使用这个模拟器，我们试图在多智能体游戏中定义的奖励矩阵产生合作策略。在多智能体博弈中，由于突现计算的本质是偶然性，所以识别突现行为并不容易。此外，指定代理的参数是非常关键的。我们已经尝试建立一个大规模并行的图形约简系统的lambda表达式上的可视化模拟器上述。为每个Agent设计局部迁移规则比我们预期的要困难。因此，在实际的模拟步骤中，我们不能发现有趣的紧急行为，在图约简过程中使用的合作代理。因此，我们部分改变了我们原来的研究计划，并开发了一个紧急的负载平衡系统的大规模并行图约简。在大规模并行计算领域，自治代理（计算节点）在其邻居中搜索负载较轻的节点以传输额外的任务。为了将随机学习自动机应用于遗传算法的适应度评估，我们在动态变化的负载环境下实现了高度自适应的负载平衡，并在大规模并行计算机SR2201上实现了紧急负载平衡系统的原型。通过使用独立的虚拟任务，没有文件访问，我们发现很好的负载平衡出现在并行计算领域。以自组织的方式建立用于任务转移的节点簇。这是涌现计算模型的一个显著特征。大规模并行计算领域的负载均衡是突现计算的一个很有前景的应用。