Exploring Issues of User Model Transparency and Proactive Behaviour in an Office Environment Control System

It is important that systems that exhibit proactive behaviour do so in a way that does not surprise or frustrate the user. Consequently, it is desirable for such systems to be both personalised and designed in such a way as to enable the user to scrutinise her user model (part of which should hold the rules describing the behaviour of the system). This article describes on-going work to investigate the design of a prototype system that can learn a given user’s behaviour in an office environment in order to use the inferred rules to populate a user model and support appropriate proactive behaviour (e.g. turning on the user’s fan under appropriate conditions). We explore the tension between user control and proactive services and consider issues related to the design of appropriate transparency with a view to supporting user comprehensibility of system behaviour. To this end, our system enables the user to scrutinise and possibly over-ride the ‘IF-THEN’ rules held in her user model. The system infers these rules from the context history (effectively a data set generated using a variety of sensors) associated with the user by using a fuzzy-decision-tree-based algorithm that can provide a confidence level for each rule in the user model. The evolution of the system has been guided by feedback from a number of real-life users in a university department. A questionnaire study has yielded supplementary results concerning the extent to which the approach taken meets users’ expectations and requirements.

[1]  Stephen S. Intille,et al.  Designing a Home of the Future , 2002, IEEE Pervasive Comput..

[2]  Tom M. Mitchell,et al.  Experience with a learning personal assistant , 1994, CACM.

[3]  Kara A. Latorella,et al.  The Scope and Importance of Human Interruption in Human-Computer Interaction Design , 2002, Hum. Comput. Interact..

[4]  E. Langer,et al.  Long-term effects of a control-relevant intervention with the institutionalized aged. , 1977, Journal of personality and social psychology.

[5]  D. Meyer,et al.  Executive control of cognitive processes in task switching. , 2001, Journal of experimental psychology. Human perception and performance.

[6]  Ernesto Arroyo,et al.  Interruptions as multimodal outputs: which are the less disruptive? , 2002, Proceedings. Fourth IEEE International Conference on Multimodal Interfaces.

[7]  Anind K. Dey,et al.  Is Context-Aware Computing Taking Control away from the User? Three Levels of Interactivity Examined , 2003, UbiComp.

[8]  Joseph S. Valacich,et al.  AIS Electronic , 2022 .

[9]  S. Cohen,et al.  Aftereffects of stress on human performance and social behavior: a review of research and theory. , 1980, Psychological bulletin.

[10]  Michael Schlosser,et al.  Continuous-Valued Attributes in Fuzzy Decision Trees , 1996 .

[11]  Keith Cheverst,et al.  Using Context as a Crystal Ball: Rewards and Pitfalls , 2001, Personal and Ubiquitous Computing.

[12]  S. Intille,et al.  Designing and Evaluating Supportive Technology for Homes , 2003 .

[13]  Antti Oulasvirta,et al.  Six modes of proactive resource management: a user-centric typology for proactive behaviors , 2004, NordiCHI '04.

[14]  Gregory D. Abowd,et al.  The context toolkit: aiding the development of context-enabled applications , 1999, CHI '99.

[15]  Michael H. Coen,et al.  Design Principles for Intelligent Environments , 1998, AAAI/IAAI.

[16]  Gregory D. Abowd,et al.  Charting past, present, and future research in ubiquitous computing , 2000, TCHI.

[17]  Bernt Schiele,et al.  Smart-Its Friends: A Technique for Users to Easily Establish Connections between Smart Artefacts , 2001, UbiComp.

[18]  Eric Horvitz,et al.  Principles of mixed-initiative user interfaces , 1999, CHI '99.

[19]  Steffen Hölldobler,et al.  Incremental Fuzzy Decision Trees , 2002, KI.

[20]  Cezary Z. Janikow,et al.  Exemplar learning in fuzzy decision trees , 1996, Proceedings of IEEE 5th International Fuzzy Systems.

[21]  Anthony Jameson,et al.  Resolving the Tension Between Invisibility and Transparency , .

[22]  Judy Kay,et al.  Managing private user models and shared personas , 2003 .

[23]  Michael C. Mozer,et al.  Parsing the Stream of Time: The Value of Event-Based Segmentation in a Complex Real-World Control Problem , 1997, Summer School on Neural Networks.

[24]  Tapio Seppänen,et al.  Adapting Applications in Mobile Terminals Using Fuzzy Context Information , 2002, Mobile HCI.

[25]  James H. Aylor,et al.  Computer for the 21st Century , 1999, Computer.

[26]  CheverstKeith,et al.  Exploring Issues of User Model Transparency and Proactive Behaviour in an Office Environment Control System , 2005 .

[27]  Keith Cheverst,et al.  UTILIZING CONTEXT HISTORY TO PROVIDE DYNAMIC ADAPTATIONS , 2004, Appl. Artif. Intell..

[28]  Barry Brumitt,et al.  EasyLiving: Technologies for Intelligent Environments , 2000, HUC.

[29]  Wolfgang Pohl.  Learning About the User – User Modeling and Machine Learning , 2007 .

[30]  Steven L. Rohall,et al.  Pagers, pilots and prairie dog: Awareness via handheld devices , 1999, Personal Technologies.

[31]  Keith Cheverst,et al.  Supporting Proactive ‘ Intelligent ’ Behaviour : the Problem of Uncertainty , 2003 .

[32]  Daniel Fitton,et al.  Out to lunch: exploring the sharing of personal context through office door displays , 2003 .

[33]  Xizhao Wang,et al.  Maintaining Case-Based Reasoning Systems Using Fuzzy Decision Trees , 2000, EWCBR.