Proactivity using Bayesian Methods and Learning

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

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/location/ • PlaceLab • http://placelab.org/ • The Context Toolkit • http://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. Multiple parents possible Multiple parents possible Technology : BBNs – overview1 Forecast? Rain? P(F) P(R) Take Umbrella? P(U)

10. Technology : BBN’s – overview2 • Example in Netica. 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. http://www.norsys.com/belief.html http://www.murrayc.com/learning/AI/bbn.shtml FOR ME INFO...

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

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

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

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?