1. From context sensitivity to intelligent user interfaces Requirements for learning agents Jarmo Korhonen 8.10.2002

2. Overview Machine learning Software agents Role of agents Implementation requirements –Sensors –Actions Use of learning results

3. The Incredible Learning Machine Tasks: –Classification,clustering –Prediction –Modeling Algorithms –Neural networks –Genetic algorithms –Bayesian learning –etc. Definition: The ability of a device to improve its performance based on its past performance

4. Software Agents For user, Software Agent is: An artificial agent which operates in a software environment. One that is authorized to act for another. Agents possess the characteristics of delegacy, competency, and amenability. In AI tech., Software Agent is: "An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors." Russell & Norvig Basically, agent has sensor, actors and goals.

5. Problems with ML in HCI with SA ML needs to process all instances at once ML requires large amounts of data ML requires suitable amount of features ML assumes static feature space User input difficult to apply to ML ML requires clear goals Mistakes need to be corrected by expert

6. Machine learning in UI Must learn quickly – two to five samples Continuous environment – must decide what is a sample from huge feature space Incremental and sequential – order is important Sustainable – incremental learning Reversible, ability to forget

7. Role of Agents Taking initiative Visibility – what is the agent doing Synchronizing with user Trust –required for delegating

8. Sensors Context, intent, emotion etc.: all are indirect sensors Direct sensors: user actions, software/device internal state There must be a mapping between direct sensors and needed indirect sensors Learning can be done with either –but feature space for direct sensors is huge

9. Actions Agent has a set of possible actions Agent has a goal Select action that go towards the goal In user interface agents, actions may be –Suggestions to user –Anticipate the actions of user –Operations on the behalf of the user

10. Results of learning The learning should be used for something Change the user interface –context-sensitivity, adapting to different users –Agent role is assistant Automating tasks –Repetitive tasks, tasks with long duration –Agent role is autonomous

11. Conclusions Learning technology needs to be improved –Take hints from user –Constrain automatically the feature domain –Learn incrementally and sequentally Agents still need to be tailored to the task