A hierarchical fuzzy-genetic multi-agent architecture for intelligent buildings online learning, adaptation and control
A hierarchical fuzzy-genetic multi-agent architecture for intelligent buildings online learning, adaptation and control
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DOI：
10.1016/s0020-0255(02)00368-7
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发表时间：
2003-03-01
影响因子：
8.1
通讯作者：
Clarke, G
中科院分区：
文献类型：
--
作者：
Hagras, H;Callaghan, V;Clarke, G
在本文中，我们描述了一个新的应用领域的智能自治系统-智能建筑（IB）。在这样做的基础上，我们提出了一种新的方法来实现IB代理的层次模糊遗传多嵌入式代理架构，包括一个低级别的行为为基础的反应层，其输出是协调在一个模糊的方式，根据审议计划。使用我们的专利模糊遗传技术（英国专利99-10539.7）的房间居民舒适度相关的模糊规则学习和适应在线。通过迭代的机器-用户对话更新和调整学习的规则库。这种学习从代理存储器（经验库）中存储的最佳规则集开始，从而减少学习时间并创建具有记忆的智能代理。我们讨论了学习在建筑控制系统中的作用，并解释了从传感器获取信息的重要性，而不是依赖于预先编程的模型来确定用户需求。我们描述了我们的体系结构，分布式嵌入式代理组成，利用感官信息来学习执行与用户舒适，节能和安全相关的任务。我们展示了这些代理，采用基于行为的方法来自机器人研究，能够不断学习和适应建筑物内的个人，同时始终提供快速，安全的响应任何情况。此外，我们表明，我们的系统学习类似的规则，其他离线监督的方法，但我们的系统有额外的能力，快速学习和优化学习的规则库。该系统的应用包括个人支持（例如，提高老年人的独立性和生活质量）、商业建筑的能源效率或航天器和行星居住舱的生活区控制系统。(C)2002年爱思唯尔科技有限公司All rights reserved.
In this paper, we describe a new application domain for intelligent autonomous systems-intelligent buildings (IB). In doing so we present a novel approach to the implementation of IB agents based on a hierarchical fuzzy genetic multi-embedded-agent architecture comprising a low-level behaviour based reactive layer whose outputs are co-ordinated in a fuzzy way according to deliberative plans. The fuzzy rules related to the room resident comfort are learnt and adapted online using our patented fuzzy-genetic techniques (British patent 99-10539.7). The learnt rule base is updated and adapted via an iterative machine-user dialogue. This learning starts from the best stored rule set in the agent memory (Experience Bank) thereby decreasing the learning time and creating an intelligent agent with memory. We discuss the role of learning in building control systems, and we explain the importance of acquiring information from sensors, rather than relying on pre-programmed models, to determine user needs. We describe how our architecture, consisting of distributed embedded agents, utilises sensory information to learn to perform tasks related to user comfort, energy conservation, and safety. We show how these agents, employing a behaviour-based approach derived from robotics research, are able to continuously learn and adapt to individuals within a building, whilst always providing a fast, safe response to any situation. In addition we show that our system learns similar rules to other offline supervised methods but that our system has the additional capability to rapidly learn and optimise the learnt rule base. Applications of this system include personal support (e.g. increasing independence and quality of life for older people), energy efficiency in commercial buildings or living-area control systems for space vehicles and planetary habitation modules. (C) 2002 Elsevier Science Inc. All rights reserved.