1. Mixed-initiative Control for Teaching and Learning in Disciple George Mason University School of Information Technology and Engineering Computer Science Department Learning Agents Laboratory Acapulco, Mexico - August 9 th, 2003 Mihai Boicu, Gheorghe Tecuci, Dorin Marcu, Cristina Boicu, Bogdan Stanescu IJCAI – 2003 Workshop on Mixed-initiative Intelligent Systems

2. © 2003 Learning Agents Laboratory – Mihai Boicu Slide: 2 Disciple Approach Learning Agent Interface Learning Problem Solving Ontology + Rules The expert teaches the agent in a way that resembles how the expert would teach a person. The agent learns from the expert, building, verifying and improving its knowledge base Disciple is an evolving theory, methodology and family of agent shells for rapid development and use of knowledge bases and agents, by subject matter experts, with limited assistance from knowledge engineers The agent help the expert to teach The expert help the agent to learn Mixed-initiative

3. © 2003 Learning Agents Laboratory – Mihai Boicu Slide: 3 Teaching Methodology

4. © 2003 Learning Agents Laboratory – Mihai Boicu Slide: 4 I need to Therefore Test whether the US_Sectret_Service_1943 that protects President_Roosevelt has any significant vulnerability Test whether President_Roosevelt has mean_to_be_protected What is a means to protect President_Roosevelt from all threats? President_Roosevelt is protected by the US_Secret_Service_1943. IF Test whether a controlling element has a critical requirement The controlling element is ?O1 The critical requirement is ?O2 THEN: Test whether a protection agency that protects a controlling element has any significant vulnerability The controlling element is ?O1 The protection agency is ?O3 Explanation ?O1 is_protected_by ?O3 ?O3 provides ?O2 ?O2 is requirement_to_be_protected ?O3 is protection_agency Plausible Upper Bound Condition ?O1 is agent is_protected_by ?O3 ?O2 is requirement_to_be_ protected ?O3 is protection_agency provides ?O2 IF Test whether ?O1 has ?O2 Question: What is a means to protect ?O1 from all threats? Answer: ?O1 is protected by the ?O3. Plausible Lower Bound Condition ?O1 is head_of_government is_protected_by ?O3 ?O2 is requirement_to_be_ protected ?O3 is personal_protection_ agency provides ?O2 THEN Test whether the ?O3 that protects ?O1 has any significant vulnerability means_to_ be_protected Rule President_ Roosevelt is_protected_by critical_requirement_ for_a_capability strategic_COG_ relevant_factor agent object person controlling_leader head_of_government political_leader Example US_Secret_ Service_1943 protection_ agency personal_ protection_agency provides requirement_to_ be_protected critical_requirement_for_ a_capability_of_a_leader critical_requirement Knowledge Representation

5. © 2003 Learning Agents Laboratory – Mihai Boicu Slide: 5 Mixed-initiative Problem Solving and Learning Input Task Generated Reduction Mixed-Initiative Problem Solving Ontology + Rules Reject Reduction Accept Reduction New Reduction Rule Refinement Task Refinement Rule Refinement Modeling Formalization Learning Solution

6. © 2003 Learning Agents Laboratory – Mihai Boicu Slide: 6 Multi-agent architecture of Teaching Assistant Modeling Assistant Distributed GUI Word Completion Agent Example Editor Agent Example Completion Agent Mixed-initiative Control Agent Teaching Assistant Rule Learning Assistant Problem Solving Assistant Teaching Assistant GUI Modeling Assistant GUI Example Analyzer Agent GUI Example Editor Agent GUI Example Completion Agent GUI Example Analyzer Agent Task Formalization Assistant Rule Analyzer Agent Implicit Explanations Agent Explanation Generation Agent Problem Solving Agent Word Completion Agent GUI Rule Refinement Assistant Rule Regeneration Agent Example-based Ontology Learning Exception-based Ontology Learning

7. © 2003 Learning Agents Laboratory – Mihai Boicu Slide: 7 Mixed-initiative Interaction during Modeling Distributed GUI Example Editor Agent User typed letter “i” in the question of the example Update the screen appearance Notify registered processes Word Completion Agent Update example question (example-id, new-question, version) If the same version update GUI appearance What is a means to protect Pr| Example Editor President_Roosevelt President Word completion Automatically identify known terms Modify the internal structure of the example Notify registered processes If the same version, validate component and show completion suggestions Example Completion Agent User modified example question (example-id, new-question, version) Compute the most plausible word completion, if any Invalidate component selection Update the preemptive computation of the next element (the answer) Update completions (word-completions, version) 1 2 3 2 2 2 3 3 4 4 2

8. © 2003 Learning Agents Laboratory – Mihai Boicu Slide: 8 Evaluation of the Modeling of Expert’s Reasoning Experimental results for the modeling adviser 2001  The expert succeeded to model more complex reasoning steps in the 2002 experiment than in the 2001 one.  The experts assessed that the suggestions provided by the modeling adviser were understandable and useful  The experts assessed that the modeling adviser is relatively easy to use and well organized. 2002

9. © 2003 Learning Agents Laboratory – Mihai Boicu Slide: 9 Evaluation of the Rule Analyzer Rule Analyzer After a rule is learned (or refined) the agent analyze it to identify potential problems and to suggest corrections:  Variable flow problems (some input variables are not linked to output variables)  Under-constrained rule (the rule generates too many solutions during problem solving)... Experimental results  The experts have always responded to the warnings of the Rule Analyzer  The experts have assessed that the suggestions of the Rule Analyzer were generally understandable and useful

10. © 2003 Learning Agents Laboratory – Mihai Boicu Slide: 10 Acknowledgements This research was performed in the Learning Agents Laboratory (LALAB) of George Mason University under the direction of Dr. Gheorghe Tecuci. Research of the LALAB is sponsored by the Defense Advanced Research Projects Agency, the Air Force Office of Scientific Research, Air Force Research Laboratory, Air Force Material Command, US Air Force, and the US Army, under agreement number F30602-00-2-0546 and grant number F49620-00-1-0072. The members of the LALAB contributed to the success of the developed systems.