1. Proactivity using Bayesian Methods and Learning 3rd UK-UbiNet Workshop, Bath Lukas Sklenar Computing Laboratory, University of Kent

2. Organisation of the presentation Collating Context Using Context –Is it done? Limitations? Bayesian Belief Networks Prediction & Proactivity – Next steps

3. Collating Context Large portions of current research dedicated to collating context –Particularly to achieve a high confidence in the gathered data –Reasoning under uncertainty, e.g. inference has to be done on low-quality sensor data

4. Collating Context - mechanisms Many mechanism exist to help with the interpretation of gathered context –Bayesian Networks, Neural Nets, Biologically inspired solutions, etc. Toolkits exist that provide higher level context information –Create abstractions over sensors –Give (almost) human readable results

5. Examples of Toolkits Location Stack –http://portolano.cs.washington.edu/projects/lo cation/http://portolano.cs.washington.edu/projects/lo cation/ PlaceLab –http://placelab.org/http://placelab.org/ The Context Toolkit –http://www.cs.berkeley.edu/~dey/context.htmlhttp://www.cs.berkeley.edu/~dey/context.html An Architecture for Context Prediction [Rene Mayrhofer, Pervasive 2004]

6. Limitations Context is collected, displayed –Little is actually done with it –Although can be useful when displayed to others Some implementations allow for better use, usually via if-then-else rules –Such rules work, but can be cumbersome –Usually have to be added/removed manually –Such rules not resilient to change

7. Improvements Need for intelligent proactivity Should comply with Weiser’s vision of disappearing hardware (and software!) For such functionality we need devices that behave intelligently We propose to use Bayesian Belief Networks to provide this intelligence

8. Bayesian Belief Networks A Bayesian network is a compact, graphical model of a probability distribution [Pearl 1988]. –A directed acyclic graph which represents direct influences among variables –A set of conditional probability tables that quantify the strengths of these influences –Mathematically correct and repeatable

9. Technology : BBNs – overview1 Forecast?Rain? Take Umbrella? P(R)P(F) P(U) RainNo Rain 3070 Rain?SunnyCloudyRainy Rain152560 No Rain702010 Forecast?YESNO Sunny0100 Cloudy2080 Rain7030 Multiple parents possible Multiple parents possible

10. Technology : BBN’s – overview2 Example in Netica. www.norsys.com www.norsys.com

11. Technology : BBN’s – Summary BBN's are trees which you can use to predict P(state|other states) Structure and influences can be learned from past data and/or constructed by domain experts Used to interpret sensor data Could be used to proactively activate features/alerts/etc. FOR ME INFO... http://www.norsys.com/belief.html http://www.murrayc.com/learning/AI/bbn.shtml

12. BBN Uses Already used when interpreting sensors Sensor Data Interpretation layer

13. BBN Proactivity Sensor Data Interpretation layer Sensor Data Interpretation layer BBN-based Proactivity Mechanism More features (power?) for a user Same engine?

14. Adding Proactivity with BBNs Eg. Add a threshold of say 50. If >50, recommend to take umbrella Add a threshold to trigger events for every combination Add a satisfaction measure Adapt network or threshold or both according to satisfaction

15. Potential Having an intelligent proactivity mechanism/enabler –Could be learned from observing user usage history –Or created by a domain expert –Complex relationships could be used as input for an intelligent trigger –These relationships would be resilient to changes in your typical environment –Whether to proactively activate something or not could be calibrated with use

16. The End – thank you Presented by Lukas Sklenar http://www.cs.kent.ac.uk/people/rpg/ ls85/index.html ls85@kent.ac.uk QUESTIONS?