Prediction Algorithms for Smart Environments
Diane J. Cook · S.M.A.R.T. environments · 2004
We live in an increasingly connected and automated society. Smart environments embody this trend by linking computers to everyday tasks and settings. Important features of such environments are that they possess a degree of autonomy, adapt themselves to changing conditions, and communicate with humans in a natural way. In order to meet environment goals such as maximizing comfort, minimizing cost, and adapting to inhabitants, a smart environment must rely upon tools from artificial intelligence such as prediction. First, models of various devices can be learned from observation and used to predict their behaviors in the future. Second, predicting an inhabitant's next action may be needed for the environment to automate selected repetitive tasks for the inhabitant, to detect anomalies that could indicate security or health concerns, and to identify ways of improving control of the environment. The results of a prediction algorithm may ultimately be input to a decision making algorithm that selects actions for the house to execute. In this chapter we summarize prediction techniques developed for these purposes, then provide an in-depth look at the role of prediction in the MavHome smart home.