reqsforlearningagents.ppt

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reqsforlearningagents.ppt

  1. 1. From context sensitivity to intelligent user interfaces Requirements for learning agents Jarmo Korhonen 8.10.2002
  2. 2. Overview <ul><li>Machine learning </li></ul><ul><li>Software agents </li></ul><ul><li>Role of agents </li></ul><ul><li>Implementation requirements </li></ul><ul><ul><li>Sensors </li></ul></ul><ul><ul><li>Actions </li></ul></ul><ul><li>Use of learning results </li></ul>
  3. 3. The Incredible Learning Machine <ul><li>Tasks: </li></ul><ul><ul><li>Classification,clustering </li></ul></ul><ul><ul><li>Prediction </li></ul></ul><ul><ul><li>Modeling </li></ul></ul><ul><li>Algorithms </li></ul><ul><ul><li>Neural networks </li></ul></ul><ul><ul><li>Genetic algorithms </li></ul></ul><ul><ul><li>Bayesian learning </li></ul></ul><ul><ul><li>etc. </li></ul></ul>Definition: The ability of a device to improve its performance based on its past performance
  4. 4. Software Agents <ul><li>For user, Software Agent is: </li></ul><ul><li>An artificial agent which operates in a software environment. </li></ul><ul><li>One that is authorized to act for another. Agents possess the characteristics of delegacy , competency , and amenability . </li></ul><ul><li>In AI tech., Software Agent is: </li></ul><ul><li>&quot;An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors.&quot; Russell & Norvig </li></ul><ul><li>Basically, agent has sensor, actors and goals. </li></ul>
  5. 5. Problems with ML in HCI with SA <ul><li>ML needs to process all instances at once </li></ul><ul><li>ML requires large amounts of data </li></ul><ul><li>ML requires suitable amount of features </li></ul><ul><li>ML assumes static feature space </li></ul><ul><li>User input difficult to apply to ML </li></ul><ul><li>ML requires clear goals </li></ul><ul><li>Mistakes need to be corrected by expert </li></ul>
  6. 6. Machine learning in UI <ul><li>Must learn quickly – two to five samples </li></ul><ul><li>Continuous environment – must decide what is a sample from huge feature space </li></ul><ul><li>Incremental and sequential – order is important </li></ul><ul><li>Sustainable – incremental learning </li></ul><ul><li>Reversible, ability to forget </li></ul>
  7. 7. Role of Agents <ul><li>Taking initiative </li></ul><ul><li>Visibility – what is the agent doing </li></ul><ul><li>Synchronizing with user </li></ul><ul><li>Trust </li></ul><ul><ul><li>required for delegating </li></ul></ul>
  8. 8. Sensors <ul><li>Context, intent, emotion etc.: all are indirect sensors </li></ul><ul><li>Direct sensors: user actions, software/device internal state </li></ul><ul><li>There must be a mapping between direct sensors and needed indirect sensors </li></ul><ul><li>Learning can be done with either </li></ul><ul><ul><li>but feature space for direct sensors is huge </li></ul></ul>
  9. 9. Actions <ul><li>Agent has a set of possible actions </li></ul><ul><li>Agent has a goal </li></ul><ul><li>Select action that go towards the goal </li></ul><ul><li>In user interface agents, actions may be </li></ul><ul><ul><li>Suggestions to user </li></ul></ul><ul><ul><li>Anticipate the actions of user </li></ul></ul><ul><ul><li>Operations on the behalf of the user </li></ul></ul>
  10. 10. Results of learning <ul><li>The learning should be used for something </li></ul><ul><li>Change the user interface </li></ul><ul><ul><li>context-sensitivity, adapting to different users </li></ul></ul><ul><ul><li>Agent role is assistant </li></ul></ul><ul><li>Automating tasks </li></ul><ul><ul><li>Repetitive tasks, tasks with long duration </li></ul></ul><ul><ul><li>Agent role is autonomous </li></ul></ul>
  11. 11. Conclusions <ul><li>Learning technology needs to be improved </li></ul><ul><ul><li>Take hints from user </li></ul></ul><ul><ul><li>Constrain automatically the feature domain </li></ul></ul><ul><ul><li>Learn incrementally and sequentally </li></ul></ul><ul><li>Agents still need to be tailored to the task </li></ul>

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