Keeping the Resident in the Loop: Adapting the Smart Home to the User

Advancements in supporting fields have increased the likelihood that smart-home technologies will become part of our everyday environments. However, many of these technologies are brittle and do not adapt to the user's explicit or implicit wishes. Here, we introduce CASAS, an adaptive smart-home system that utilizes machine learning techniques to discover patterns in resident's daily activities and to generate automation polices that mimic these patterns. Our approach does not make any assumptions about the activity structure or other underlying model parameters but leaves it completely to our algorithms to discover the smart-home resident's patterns. Another important aspect of CASAS is that it can adapt to changes in the discovered patterns based on the resident implicit and explicit feedback and can automatically update its model to reflect the changes. In this paper, we provide a description of the CASAS technologies and the results of experiments performed on both synthetic and real-world data.

[1]  Ming-Syan Chen,et al.  Progressive Partition Miner: An Efficient Algorithm for Mining General Temporal Association Rules , 2003, IEEE Trans. Knowl. Data Eng..

[2]  Richard S. Sutton,et al.  Introduction to Reinforcement Learning , 1998 .

[3]  Diane J. Cook,et al.  Attributing events to individuals in multi-inhabitant environments , 2008 .

[4]  Hani Hagras,et al.  A fuzzy embedded agent-based approach for realizing ambient intelligence in intelligent inhabited environments , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[5]  A. Fox,et al.  Integrating information appliances into an interactive workspace , 2000, IEEE Computer Graphics and Applications.

[6]  Henry A. Kautz,et al.  Location-Based Activity Recognition using Relational Markov Networks , 2005, IJCAI.

[7]  Gregory D. Abowd,et al.  The Human Experience , 2002, IEEE Pervasive Comput..

[8]  William C. Mann,et al.  The Gator Tech Smart House: a programmable pervasive space , 2005, Computer.

[9]  S. V. Vaseghi.  State duration modelling in hidden Markov models , 1995, Signal Process..

[10]  Diane J. Cook,et al.  Keeping the intelligent environment resident in the loop , 2008 .

[11]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[12]  J. Vanhala,et al.  Continuous-time fuzzy control and learning methods , 2007, 2007 International Symposium on Communications and Information Technologies.

[13]  Heikki Mannila,et al.  Discovering Generalized Episodes Using Minimal Occurrences , 1996, KDD.

[14]  Sajal K. Das,et al.  Smart Environments: Technology, Protocols and Applications (Wiley Series on Parallel and Distributed Computing) , 2004 .

[15]  Manuel Roveri,et al.  What planner for ambient intelligence applications? , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[16]  Diane J. Cook,et al.  Adapting to Resident Preferences in Smart Environments , 2008 .

[17]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[18]  Tom Fawcett,et al.  Activity monitoring: noticing interesting changes in behavior , 1999, KDD '99.

[19]  Kent Larson,et al.  Activity Recognition in the Home Using Simple and Ubiquitous Sensors , 2004, Pervasive.

[20]  P. S. Sastry,et al.  A survey of temporal data mining , 2006 .

[21]  X.S. Wang,et al.  Discovering Frequent Event Patterns with Multiple Granularities in Time Sequences , 1998, IEEE Trans. Knowl. Data Eng..

[22]  John F. Roddick,et al.  A Survey of Temporal Knowledge Discovery Paradigms and Methods , 2002, IEEE Trans. Knowl. Data Eng..

[23]  Diane J. Cook,et al.  Data Mining for Hierarchical Model Creation , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[24]  Michael C. Mozer,et al.  The Neural Network House: An Environment that Adapts to its Inhabitants , 1998 .