A Soft-Computing Distributed Artificial Intelligence Architecture for Intelligent Buildings

This paper presents an innovative soft computing architecture based on a combination of DAI (distributed artificial intelligence), fuzzy-genetic driven embedded-agents and IP Internet technology applied to the domain of intelligent-buildings. It describes the nature of intelligent buildings (IB) and embedded-agents, explaining the unique control and learning problems they present. We show how fuzzy-logic techniques can be used to create a behaviour-based multi-agent architecture in intelligent-buildings. We discuss how this approach deals with the highly unpredictable and imprecise nature of the physical world in which the system is situated, and how embedded-agents can be constructed that utilise sensory information to learn to perform tasks related to user comfort, energy conservation, and safety. We explain in detail our machine learning methodology that is based on a novel genetic algorithm mechanism referred to as an associative experience engine (AEE) and present the results of practical experiments. We compare results obtained from the AEE approach to that of the widely known Mendel-Wang method. Finally we explain potential applications for such systems ranging from commercial buildings to living-area control systems for space vehicles and planetary habitation modules.