1. Gheorghe Tecuci 1,2, Mihai Boicu 1, Dorin Marcu 1 1 Learning Agents Laboratory, George Mason University 2 Center for Strategic Leadership, US Army War College IJCAI-03 Workshop on Mixed-Initiative Intelligent Systems Acapulco, Mexico, 9 August 2003

2. The expert teaches the agent to perform various tasks in a way that resembles how the expert would teach a person. Develop a learning agent shell that can be taught directly by a subject matter expert to become a knowledge-based assistant 1. Mixed-initiative problem solving 2. Teaching and learning 3. Multistrategy learning Use several levels of synergism between the expert that has the knowledge to be formalized and the agent that knows how to formalize it, and between complementary learning methods: Disciple: an approach to KB and agent development The agent learns from the expert, building, verifying and improving its knowledge base

3. Main idea of the Disciple mixed-initiative approach The complex knowledge engineering activities, traditionally performed by a knowledge engineer (KE) and a subject matter expert (SME), are replaced with equivalent activities performed by the SME and a Disciple Agent, through mixed- initiative reasoning, and with very limited assistance from the KE. Define domain model Create ontology Define rules Verify and update rules KE SME Traditionally KE Agent SMEAgent SME Specify instances Learn ontological elements Import and create initial ontology Agent Learn rules SME Agent Define and explain examples SME AgentSMEAgent Critique examples Refine rules Explain critiques SME Agent Extend domain model SME KE Define initial model With Disciple

4. Extended KB stay informed be irreplaceable communicate be influential Integrated KB Initial KB have support be protected be driving force 432 concepts and features, 29 tasks, 18 rules For COG identification for leaders 37 acquired concepts and features for COG testing COG identification and testing (leaders) Domain analysis and ontology development (KE+SME) Parallel KB development (SME assisted by KE) KB merging (KE) Knowledge Engineer (KE) All subject matter experts (SME) DISCIPLE-COG Training scenarios: Iraq 2003 Arab-Israeli 1973 War on Terror 2003 Team 1 Team 2Team 3Team 4Team 5 5 features 10 tasks 10 rules Learned features, tasks, rules 14 tasks 14 rules 2 features 19 tasks 19 rules 35 tasks 33 rules 3 features 24 tasks 23 rules Unified two features Deleted 4 highly incomplete rules Refined 11 rules Did not affect the other 84 rules +9 features  478 concepts and features +105 tasks  134 tasks +95 rules  113 rules DISCIPLE-COG Testing scenario: North Korea 2003 Correctness = 98.15% 2.5 examples/rule 5.47 hours average training time Current status: Parallel KB development experiment – Sp03

5. 2 Agent doctrinal training After action review and agent personalization Agent use and non-disruptive learning DISCIPLE-MI 3 Intelligent tutoring 4 DISCIPLE-MI 5 6 Knowledge base optimization and re-use Agent Lifecycle DISCIPLE-MI Building an agent shell 1 Domain experts Knowledge engineer Knowledge engineer Envisioned life-cycle of future Disciple-MI

6. Research directions Modeling expert’s reasoning Learnable knowledge representation Multistrategy teaching and learning Resource-bounded learning Learning a model of the expert Mixed-initiative problem solving Acquisition of expert’s language User-agent interaction KB optimization and integration Intelligent tutoring

7. This research was sponsored by the Defense Advanced Research Projects Agency, Air Force Research Laboratory, Air Force Material Command, USAF under agreement number F30602-00-2-0546, by the Air Force Office of Scientific Research under grant number F49620-00-1-0072 and by the US Army War College. Acknowledgements