• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
reqsforlearningagents.ppt
 

reqsforlearningagents.ppt

on

  • 198 views

 

Statistics

Views

Total Views
198
Views on SlideShare
198
Embed Views
0

Actions

Likes
0
Downloads
0
Comments
0

0 Embeds 0

No embeds

Accessibility

Categories

Upload Details

Uploaded via as Microsoft PowerPoint

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

    reqsforlearningagents.ppt reqsforlearningagents.ppt Presentation Transcript

    • From context sensitivity to intelligent user interfaces Requirements for learning agents Jarmo Korhonen 8.10.2002
    • Overview
      • Machine learning
      • Software agents
      • Role of agents
      • Implementation requirements
        • Sensors
        • Actions
      • Use of learning results
    • 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
    • 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.
    • 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
    • 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
    • Role of Agents
      • Taking initiative
      • Visibility – what is the agent doing
      • Synchronizing with user
      • Trust
        • required for delegating
    • 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
    • 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
    • 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
    • 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