A hierarchical fuzzy genetic multi-agent architecture for intelligent buildings sensing and control

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 based on a hierarchical fuzzy genetic mufti 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 resident's comfort are learnt online in a short time interval using our patented Fuzzy-Genetic techniques (British Patent 99-10539.7) from the resident's actual behaviour in a learning phase. Our approach utilises an intelligent agent approach to autonomously governing the building environment. We discuss the role of learning in building control systems, and contrast this approach with existing IB solutions. 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. Such a system could be used to provide support for older people, or people with disabilities, allowing them greater independence and quality of life.