Automated planning of tutorial dialogues

Managing a dialogue between a student and an intelligent tutoring system is a challenging problem for many applications. It has often been argued and demonstrated that adaptive dialogues between a user and a computer can be generated automatically, using automated planning techniques to plan speech acts. To date such plan-based dialogue generation approaches have relied on deterministic planning algorithms. Consequently they can only handle sequential dialogue structures. In this paper we describe a new approach for automatically planning more general tree-like dialogue structures, by using a nondeterministic planner with incomplete knowledge and sensing. Our approach takes into account incomplete information about the user's knowledge by including queries that the computer can ask to the user to gather missing information that is necessary for an effective feedback. We illustrate our system with an application to an intelligent tutoring system for medical diagnosis.

[1]  Froduald Kabanza,et al.  Persuasive Argumentation in a Medical Diagnosis Tutoring System , 2010 .

[2]  Reva Freedman.  Plan-Based Dialogue Management in a Physics Tutor , 2000, ANLP.

[3]  Brady Clark,et al.  Advantages of Spoken Language Interaction in Dialogue-Based Intelligent Tutoring Systems , 2004, Intelligent Tutoring Systems.

[4]  Froduald Kabanza,et al.  Clinical Reasoning Learning with Simulated Patients , 2005, AIME.

[5]  W. Lewis Johnson,et al.  Tutoring Diagnostic Problem Solving , 2000, Intelligent Tutoring Systems.

[6]  Vincent Aleven,et al.  Learning by diagramming Supreme Court oral arguments , 2007, ICAIL.

[7]  Floriana Grasso,et al.  Dialectical argumentation to solve conflicts in advice giving: a case study in the promotion of healthy nutrition , 2000, Int. J. Hum. Comput. Stud..

[8]  Kurt VanLehn,et al.  The Andes Physics Tutoring System: Lessons Learned , 2005, Int. J. Artif. Intell. Educ..

[9]  Clinical-Reasoning Skill Acquisition through Intelligent Group Tutoring , 2005, IJCAI.

[10]  Claus Zinn,et al.  Supporting Constructive Learning with a Feedback Planner , 2000 .

[11]  Karsten Stegmann,et al.  Facilitating argumentative knowledge construction with computer-supported collaboration scripts , 2007, Int. J. Comput. Support. Collab. Learn..

[12]  Richard E. Neapolitan,et al.  Learning Bayesian networks , 2007, KDD '07.

[13]  Martha E. Pollack,et al.  A Model of Plan Inference That Distinguishes Between the Beliefs of Actors and Observers , 1986, ACL.

[14]  Hector J. Levesque,et al.  Intention is Choice with Commitment , 1990, Artif. Intell..

[15]  Tangming Yuan,et al.  A Human-Computer Dialogue System for Educational Debate: A Computational Dialectics Approach , 2008, Int. J. Artif. Intell. Educ..

[16]  Alison Cawsey.  Planning interactive explanations , 1993 .

[17]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[18]  Fahiem Bacchus,et al.  Extending the Knowledge-Based Approach to Planning with Incomplete Information and Sensing , 2004, ICAPS.

[19]  Froduald Kabanza,et al.  Implementing tutoring strategies into a patient simulator for clinical reasoning learning , 2006, Artif. Intell. Medicine.

[20]  Johanna D. Moore,et al.  Towards a Principled Representation of Discourse Plans , 1994, Proceedings of the Sixteenth Annual Conference of the Cognitive Science Society.