In this paper 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 nomadic in nature remaining

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... static for periods but occasionally moving between locations or 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 robot type agents use an interactive learning whilst most building based agents use non-intrusive background learning modes. In this paper 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.