1. 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 moza@ics.uci.edu ICS 205 – Spring 2002

2. Overview Agents… Agents…  Agents and their duties  Problems with interface agents  Previous approaches  Proposed approach  Some examples Beyond Intelligent Interfaces Beyond Intelligent Interfaces  Cooperative problem solving  Results of an empirical study  Analysis of the results Conclusion Conclusion

3. Agents that Reduce Work and Information Overload Pattie Maes

4. Why Agents? Computers assisting in everyday tasks Computers assisting in everyday tasks Untrained users interacting with computers Untrained users interacting with computers Computers require continuous user interaction Computers require continuous user interaction “Indirect Management” required instead of “direct manipulation” “Indirect Management” required instead of “direct manipulation”  Collaboration with the user as a “personal assistant”

5. 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.

6. Agents Duties’ Perform tasks on the users behalf Perform tasks on the users behalf  e.g. Selection of books Train or teach the user Train or teach the user  e.g. Image editing Help different users collaborate Help different users collaborate  e.g. Meeting scheduling Monitor events and procedures on the user’s behalf Monitor events and procedures on the user’s behalf  e.g. Information filtering

7. Building Agents – Problems Competence Competence  How does an agent acquire the knowledge? Trust Trust  How does the user feel confident delegating the task?

8. Previous Approaches End user program the interface agent End user program the interface agent  User programmed rules  Disadvantages  Does not deal with the competence criterion  Requires too much insight from the end user Knowledge-based approach Knowledge-based approach  Domain knowledge programmed into the agent  Disadvantages  Work for programming the knowledge  Adaptation to particular users preferences  Trust a big issue

9. Approach – Machine Learning Under certain conditions the agent program itself Under certain conditions the agent program itself  Limited background knowledge  Learns from user and other agents Conditions for the agent to learn Conditions for the agent to learn  Repetition an important aspect  Behavior different for all users The metaphor – “personal assistant” 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

10. Advantages of the Approach Less work Less work Adaptation Adaptation Transferring Information Transferring Information

11. Learning Technique Observe and imitate Observe and imitate Adapt based on user feedback Adapt based on user feedback  Direct feedback  Indirect feedback Trained based on examples Trained based on examples Advice from other agents Advice from other agents

12. 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.

13. Example agents Electronic mail handling agent Electronic mail handling agent Meeting scheduling agent Meeting scheduling agent Electronic news filtering agent Electronic news filtering agent Recommending agent Recommending agent

14. Electronic Mail Agent – Maxim Learns to prioritize, delete, forward, sort and archive mail Learns to prioritize, delete, forward, sort and archive mail Uses Memory-based reasoning Uses Memory-based reasoning Measures confidence level in the prediction Measures confidence level in the prediction Actions determined by thresholds Actions determined by thresholds Dealing with initial low competence Dealing with initial low competence

15. 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).

16. Other Agents Meeting Scheduling Agent Meeting Scheduling Agent  Generic learning agent adapted to the scheduling software. News Filtering Agent – NewT News Filtering Agent – NewT  Filter Usenet news  Agents can be trained for specific purposes Entertainment Selection Agent – Ringo 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”

17. Beyond Intelligent Interfaces: Exploring, Analyzing, and Creating Success Models of Cooperative Problem Solving Gerhard Fischer Brent Reeves

18. Cooperative Problem Solving Augmenting a person’s ability to create, reflect, design, decide and reason Augmenting a person’s ability to create, reflect, design, decide and reason Conceptual framework behind a system determines its behavior Conceptual framework behind a system determines its behavior

19. Empirical Study Study of a success model Study of a success model Highlights the inherent difficulties in high functionality systems Highlights the inherent difficulties in high functionality systems Necessary to get a better understanding of the system Necessary to get a better understanding of the system

20. Results from the study (1/2) Users do not know the existence of tools Users do not know the existence of tools Users do not know how to access tools Users do not know how to access tools Users do not know when to use the tools Users do not know when to use the tools Users cannot combine or adapt tools for special uses Users cannot combine or adapt tools for special uses

21. Results from the study (2/2) Incremental problem specification Incremental problem specification  Identifying the problem Achieving shared understanding Achieving shared understanding  Identifying the solution Integration between problem setting and problem solving Integration between problem setting and problem solving  Context important in determining the problem

22. Analysis based on the results Natural Language is less important than Natural Communication Natural Language is less important than Natural Communication Multiple specification technique Multiple specification technique Mixed initiative dialogues Mixed initiative dialogues Management of trouble Management of trouble Simultaneous exploration of problem and solution spaces Simultaneous exploration of problem and solution spaces Humans operate in the physical world Humans operate in the physical world Humans make use of distributed intelligence Humans make use of distributed intelligence

23. Requirements for a Cooperative Problem Solving System Beyond user interfaces Beyond user interfaces Problems in the context Problems in the context Reliability of “Back talk” in design situations must be increased Reliability of “Back talk” in design situations must be increased Need for specialization and putting knowledge in the world Need for specialization and putting knowledge in the world Supporting human problem-domain communication with domain-oriented architectures Supporting human problem-domain communication with domain-oriented architectures

24. Conclusions Interfaces of the future Interfaces of the future  Intelligent  Context aware  Trustworthy  Competent  Invisible Issues Issues  Privacy  Ethical