Incorporating Tutorial Strategies Into an Intelligent Assistant

1. Incorporating Tutorial Strategies Into an Intelligent Assistant Jim R. Davies, Neal Lesh, Charles Rich, Candace L. Sidner, Abigail S. Gertner, Jeff Rickel

2. Organizations Involved • College of Computing, Georgia Institute of Technology (Davies) • Mitsubishi Electric Research Labs (Lesh, Rich, Sidner) • The MITRE Corporation (Gertner) • USC Information Sciences Institute (Rickel) http://www.cc.gatech.edu/~jimmyd/research/triton/

3. Motivating Example • Long camping trip • Someone tutors you on how to set up a tent • As time passes, that tutor becomes an assistant http://www.cc.gatech.edu/~jimmyd/research/triton/

4. Research Goal • To show that assisting and tutoring are two points on the same spectrum by building an agent that can transition between both behaviors. http://www.cc.gatech.edu/~jimmyd/research/triton/

5. Intellectual History • Collaborative Assisting Agent (COLLAGEN) • assists with software applications • COLLAGEN generated interest in learning to use applications. • We are extending COLLAGEN so it can tutor and built an agent called Triton. http://www.cc.gatech.edu/~jimmyd/research/triton/

6. COLLAGEN (COLLaborative Agent) • Middleware • Discourse theory of collaboration • Shared plan theory • Rich, C. and Sidner, C. L. (1998). COLLAGEN: A collaboration manager for software interface agents. User Modeling and User-Adapted Interaction, Vol. 8, No. 3/4, 315—350. http://www.cc.gatech.edu/~jimmyd/research/triton/

9. Assistant Scenario • User: Let’s plan the second leg of the trip. • Assistant: Okay. • User: [selects Washington] What next? • Assistant: [points to San Francisco] I will select San Francisco. • User: Okay. • Assistant: [selects San Francisco] • User: What next? • Assistant: Let’s remove an airline constraint. http://www.cc.gatech.edu/~jimmyd/research/triton/

11. ITS Background • Domains: physics and algebra • Domain Knowledge • Student Model • Pedagogical knowledge • Wenger, E. (1987). Artificial Intelligence and Tutoring Systems. Morgan Kaufmann Publishers, Inc. Los Altos, CA. http://www.cc.gatech.edu/~jimmyd/research/triton/

12. Tutoring Scenario (part 1) • User: What next? • Triton: Please specify the route of the trip. • User: What next? • Triton: Select Atlanta and then select Boston. • User: What next? • Triton: Please select Atlanta. • User: What next? • Triton: [points to Atlanta with cursor] Please click on the dot near Atlanta on the map. http://www.cc.gatech.edu/~jimmyd/research/triton/

13. Tutoring Scenario (cont.) • User: [clicks on Atlanta] What next? • Triton: Please select Boston. • User: [clicks on Dallas] • Triton: Oops! What you did was select Dallas; what you should have done was select Boston. I will bring you back to the previous state by clicking undo. [undo] Please select Boston. • User: [clicks on Boston] • Triton: Great job! • User: What next? • Triton: Select the dates and times of travel. http://www.cc.gatech.edu/~jimmyd/research/triton/

14. Goal of this research • Add to COLLAGEN to give it tutoring behaviors • Adjustable parameters • Triton http://www.cc.gatech.edu/~jimmyd/research/triton/

15. Worked Examples • Worked examples are kinds of recipes http://www.cc.gatech.edu/~jimmyd/research/triton/

16. The User is Not Always Right • Determining when a task is completed • Responding to Errors http://www.cc.gatech.edu/~jimmyd/research/triton/

17. Responding to Errors • Intervene after n unrecognizable actions • What the intervention looks like: • Say what the student did • Say what the student should have done • Undo to get to previous state http://www.cc.gatech.edu/~jimmyd/research/triton/

18. Tutors are not Maximally Helpful • Because of learning goals • Waiting for Student Initiative • Suggesting actions without doing them • Explaining • Demonstrating • Pointing http://www.cc.gatech.edu/~jimmyd/research/triton/

19. Learning Goals • Usually task goals are in service of learning goals, but not always http://www.cc.gatech.edu/~jimmyd/research/triton/

20. Waiting For Student Initiative • In assisting, always try to help • In tutoring, get student to try herself http://www.cc.gatech.edu/~jimmyd/research/triton/

21. Suggesting Actions Without Doing Them • Should you force the user to do all actions? • Agent suggests doing, but doesn’t do. http://www.cc.gatech.edu/~jimmyd/research/triton/

22. Explaining

23. Explaining (cont.) • Composite Actions • list of task descriptions • Primitive Actions • application-level description of what to do on screen • Stored as explanation recipes http://www.cc.gatech.edu/~jimmyd/research/triton/

24. Demonstrating • Behavior: • Do a sequence of actions • Undo them • Stored as explanation recipes http://www.cc.gatech.edu/~jimmyd/research/triton/

25. Pointing • In assisting, point when proposing • In tutoring, point when explaining a primitive http://www.cc.gatech.edu/~jimmyd/research/triton/

26. Summary of Parameters • When to intervene after error detection • Who defaults to do actions • When to point http://www.cc.gatech.edu/~jimmyd/research/triton/

27. Contributions • Middleware • Use of recipes as a single representational structure for: • abstract actions • utterances • explanations • demonstrations http://www.cc.gatech.edu/~jimmyd/research/triton/

28. Conclusions • This work bridges the gap between tutoring and assisting • Smoothly transitions between them • Based on collaborative discourse theory http://www.cc.gatech.edu/~jimmyd/research/triton/

29. Future Work • Student Model • Automatic Shifting between assisting and tutoring http://www.cc.gatech.edu/~jimmyd/research/triton/

30. URLs • http://www.cc.gatech.edu/~jimmyd/research/triton/ • http://www.merl.com/ http://www.cc.gatech.edu/~jimmyd/research/triton/