多目标约束模糊嵌入式智能体在线协调的自适应遗传结构

E. Tawil, H. Hagras
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引用次数: 1

摘要

本文提出了一种新的嵌入式智能体架构,该架构旨在使用独特的在线多目标多约束遗传算法来协调现实世界智能环境中相互作用的嵌入式智能体系统。嵌入式代理可以是复杂的,比如操作分层模糊逻辑控制器的移动机器人,也可以是简单的,比如承担阈值功能的台灯。该体系结构将使代理能够了解用户的需求,并根据这些需求实时采取行动,而无需重复配置系统。该系统可以处理不可靠的传感器和执行器,也可以补偿故障的代理,并在线适应突发变化。该体系结构允许代理的组织是动态的,因为它允许代理迁入和迁出系统。对上述体系结构的实现进行了各种各样的实验,其中系统在不同环境的不同场景中进行了测试
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Adaptive Genetic-Based Architecture for the On-line Co-ordination of Fuzzy Embedded Agents with Multiple Objectives and Constraints
This paper presents a novel embedded agent architecture that aims to co-ordinate a system of interacting embedded agents in real-world intelligent environments using a unique on-line multi-objective and multi-constraint genetic algorithm. The embedded agents can be complex ones such as mobile robots that would operate hierarchical fuzzy logic controllers or simple ones such as desk lamps that would bear threshold functions instead. The architecture would enable the agents to learn the users' desires and act based on them in real-time without having to repeatedly configure the system. The system can handle unreliable sensors and actuators as well as compensating for agents that break down and adapting on-line to sudden changes. The architecture allows for the organisation of agents to be dynamic since it accommodates for agents migrating in and out of the system. Multifarious experiments were performed on implementations of the aforementioned architecture where the system was tested in different scenarios of varying circumstances
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