文章基本信息

标题：CoopRA Algorithm for Universal Characterization of the Experimental Evaluation Results of Cooperative Multiagent Systems
其他标题：CoopRA Algorithm for Universal Characterization of the Experimental Evaluation Results of Cooperative Multiagent Systems
本地全文：下载
作者：Laszlo Barna Iantovics ; Muaz A. Niazi ; Adrian Gligor 等
期刊名称：Brain. Broad Research in Artificial Intelligence and Neuroscience
印刷版ISSN：2067-3957
出版年度：2018
卷号：9
期号：3
页码：37-49
语种：English
出版社：EduSoft publishing
摘要：Experimental evaluation of the cooperative multiagent systems (CMASs) provides an assessment way that should be analysed. In this paper, we propose an algorithm with acronym CoopRA that can make a deep performance characterization, based on different indicators, of the experimental evaluation results of a CMAS. This could lead to the formulation of helpful information in some decisions related to the performance of the studied CMASs. In order to validate the proposed algorithm, we performed a case study on a CMAS composed of simple reactive agents that operate by mimicking the problem/task solving of natural ants. We chose this type of cooperative multiagent system architecture, based on the fact that even in case of the cooperativemultiagent systems composed of simple efficiently and flexibly cooperating agents could emerges an increased problem solving intelligence at the systems level. The evaluation was performed for the Travelling Salesman Problem (TSP) solving that is a well-known NP-hard problem, having many real-life applications.
关键词：intelligent system;cooperative multiagent system;NP-hard problem;travelling salesman problem;nature inspired computing;ant colony optimization;digital image processing;medical imaging