Agents that Reduce Work and Information Overload
and
Beyond Intelligent Interfaces
Presented by
Maulik Oza
Department of Information and Computer Science
University of California, Irvine
[email protected]
ICS 205 – Spring 2002
Agents that Reduce Work and
Information Overload
Pattie Maes
Why Agents?
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Computers assisting in everyday tasks
Untrained users interacting with computers
Computers require continuous user interaction
“Indirect Management” required instead of
“direct manipulation”
 Collaboration with the user as a “personal
assistant”
Figure: The interface agent does not act as an interface or layer between the user and the
application. Rather, it behaves as a personal assistant which cooperates with the user on
the task. The user is able to bypass the agent.
Agents Duties’
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Perform tasks on the users behalf
 e.g. Selection of books
Train or teach the user
 e.g. Image editing
Help different users collaborate
 e.g. Meeting scheduling
Monitor events and procedures on the user’s
behalf
 e.g. Information filtering
Building Agents – Problems
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Competence
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How does an agent acquire the knowledge?
Trust
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How does the user feel confident delegating
the task?
Previous Approaches
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End user program the interface agent
User programmed rules
 Disadvantages
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Does not deal with the competence criterion
 Requires too much insight from the end user
Knowledge-based approach
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Domain knowledge programmed into the agent
 Disadvantages
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Work for programming the knowledge
Adaptation to particular users preferences
Trust a big issue
Approach – Machine Learning
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Under certain conditions the agent program itself
 Limited background knowledge
 Learns from user and other agents
Conditions for the agent to learn
 Repetition an important aspect
 Behavior different for all users
The metaphor – “personal assistant”
 Learns based on the preferences of the employer
 Requires time for performing efficiently
 Learns based on experience, employer’s instructions as
well as from experienced assistants
Advantages of the Approach
Less work
 Adaptation
 Transferring Information
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Learning Technique
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Observe and imitate
Adapt based on user feedback
 Direct feedback
 Indirect feedback
Trained based on examples
Advice from other agents
Figure: The interface agent learns in four different ways: (1) it observes and imitates
the user's behavior, (2) it adapts based on user feedback, (3) it can be trained by the
user on the basis of examples, and (4) it can ask for advice from other agents
assisting other users.
Example agents
Electronic mail handling agent
 Meeting scheduling agent
 Electronic news filtering agent
 Recommending agent
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Electronic Mail Agent – Maxim
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Learns to prioritize, delete, forward, sort and archive
mail
Uses Memory-based reasoning
Measures confidence level in the prediction
Actions determined by thresholds
Dealing with initial low competence
Figure: Simple caricatures convey the state of the agent to the user. The agent can be "alert" (tracking the
user's actions), "thinking" (computing a suggestion), "offering a suggestion" (confidence insuggestion is
above "tell-me" threshold), "surprised" if the suggestion is not accepted, "gratified" if the suggestion is
accepted, "unsure" about what to do in the current situation (confidence below "tell-me" threshold, and
thus suggestion is not offered), "confused" about what the user ends up doing, "pleased" that the
suggestion it was not sure about turned out to be the right one after all, and "working" or performing an
automated task (confidence in prediction above "do-it" threshold).
Other Agents
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Meeting Scheduling Agent
 Generic learning agent adapted to the scheduling
software.
News Filtering Agent – NewT
 Filter Usenet news
 Agents can be trained for specific purposes
Entertainment Selection Agent – Ringo
 The “killer app”?
 How to make enough data available to the system for it
to make recommendations
 User may rely too much on the system and stop
entering new items
 Solution – “virtual users”
Beyond Intelligent Interfaces: Exploring,
Analyzing, and Creating Success Models
of Cooperative Problem Solving
Gerhard Fischer
Brent Reeves
Cooperative Problem Solving
Augmenting a person’s ability to create,
reflect, design, decide and reason
 Conceptual framework behind a system
determines its behavior
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Empirical Study
Study of a success model
 Highlights the inherent difficulties in high
functionality systems
 Necessary to get a better understanding of
the system
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Results from the study (1/2)
Users do not know the existence of tools
 Users do not know how to access tools
 Users do not know when to use the tools
 Users cannot combine or adapt tools for
special uses
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Results from the study (2/2)
Incremental problem specification
 Identifying the problem
 Achieving shared understanding
 Identifying the solution
 Integration between problem setting and
problem solving
 Context important in determining the
problem
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Analysis based on the results
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Natural Language is less important than Natural
Communication
Multiple specification technique
Mixed initiative dialogues
Management of trouble
Simultaneous exploration of problem and solution
spaces
Humans operate in the physical world
Humans make use of distributed intelligence
Requirements for a Cooperative Problem
Solving System
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Beyond user interfaces
Problems in the context
Reliability of “Back talk” in design situations must be increased
Need for specialization and putting knowledge in the world
Supporting human problem-domain communication with
domain-oriented architectures
Conclusions
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Interfaces of the future
 Intelligent
 Context aware
 Trustworthy
 Competent
 Invisible
Issues
 Privacy
 Ethical