A hierarchical fuzzy-genetic multi-agent architecture for intelligent buildings online learning, adaptation and control

  • Authors:

  • Hani Hagras;Victor Callaghan;Martin Colley;Graham Clarke

  • Affiliations:

  • Department of Computer Science, University of Essex, Wivenhoe Park, Colchester CO43SQ, UK;Department of Computer Science, University of Essex, Wivenhoe Park, Colchester CO43SQ, UK;Department of Computer Science, University of Essex, Wivenhoe Park, Colchester CO43SQ, UK;Department of Computer Science, University of Essex, Wivenhoe Park, Colchester CO43SQ, UK

  • Venue:
  • Information Sciences—Informatics and Computer Science: An International Journal - Special issue on recent advances in soft computing
  • Year:
  • 2003

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Abstract

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.