PREDICTING INHABITANT ACTION USING ACTION AND TASK MODELS WITH APPLICATION TO SMART HOMES
- 1 March 2004
- journal article
- Published by World Scientific Pub Co Pte Ltd in International Journal on Artificial Intelligence Tools
- Vol. 13 (1), 81-99
- https://doi.org/10.1142/s0218213004001533
Abstract
An intelligent home is likely in the near future. An important ingredient in an intelligent environment such as a home is prediction – of the next low-level action, the next location, and the next high-level task that an inhabitant is likely to perform. In this paper we model inhabitant actions as states in a simple Markov model. We introduce an enhancement to this basic approach, the Task-based Markov model (TMM) method. TMM discovers high-level inhabitant tasks using the supplied unlabeled data. We investigate clustering of actions to identify tasks, and integrate clusters into a hidden Markov model that predicts the next inhabitant action. We validate our approach and observe that for simulated data we achieve good accuracy using both the simple Markov model and the TMM, whereas on real data we see that simple Markov models outperform the TMM. We also perform an analysis of the performance of the HMM in the framework of the TMM when diverse patterns are introduced into the data.Keywords
This publication has 2 references indexed in Scilit:
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