Online Learning and Adaptation for Intelligent Embedded Agents Operating in Domestic Environments

  • Hani Hagras
  • Victor Callaghan
  • Martin Colley
  • Graham Clarke
  • Hakan Duman
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 116)


In this chapter we show how intelligent embedded agents situated in an intelligent domestic environment can perform learning and adaptation. A typical domestic environment provides an environment where there is wide scope for utilising computer-based products to enhance living conditions. Intelligent embedded agents can be part of the building infrastructure and static in nature (e.g. lighting, HVAC etc.), some will be carried on the person as wearables, others will be highly mobile, as with robots. Both non-intrusive and interactive learning modes (including a mix of both) are used, depending on situation of the agent. For instance mobile robotic agents use an interactive learning whilst most building based agents use non-intrusive background learning modes. In this chapter we will introduce the learning and adaptation mechanisms needed by the Building and Robotic embedded agents to fulfil their missions in intelligent domestic environments. We also present a high-level multi embedded-agent model, explaining how it facilitates inter-agent communication and cooperation between heterogeneous sets of embedded agents within a domestic environment.


Membership Function Rule Base Transmission Control Protocol Obstacle Avoidance Fuzzy Logic Controller 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Hani Hagras
    • 1
  • Victor Callaghan
    • 1
  • Martin Colley
    • 1
  • Graham Clarke
    • 1
  • Hakan Duman
    • 1
  1. 1.Department of Computer ScienceUniversity of EssexColchesterEngland

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