Learning Agents Center George Mason University

1. Development and Use of Intelligent Decision-making Assistants: The Disciple Approach Gheorghe Tecuci, Mihai Boicu, Dorin Marcu, Bogdan Stanescu, Cristina Boicu, Marcel Barbulescu Learning Agents Center George Mason University Computer Science Department Partners Day Symposium May 4, 2004

2. Overview Research Problem, Approach, and Application Knowledge Representation, Reasoning, and Learning Experiments of Agent Development and Use Long Term Research Vision Acknowledgements

3. How are agents built and why it is hard Intelligent Agent Domain Knowledge Inference Engine Expert Engineer Dialog Programming Knowledge Base Results The knowledge engineer attempts to understand how the subject matter expert reasons and solves problems and then encodes the acquired expertise into the agent's knowledge base. This modeling and representation of expert’s knowledge is long, painful and inefficient (known as the “knowledge acquisition bottleneck”).

4. Research Problem and Approach Research Problem:Elaborate a theory, methodology and family of systems for the development of knowledge-base agents by subject matter experts, with limited assistance from knowledge engineers. Approach: Develop a learning agent that can be taught directly by a subject matter expert while solving problems in cooperation. The expert teaches the agent how to perform various tasks 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 1. Mixed-initiative problem solving 2. Teaching and learning 3. Multistrategy learning Problem Solving Ontology + Rules Interface Learning

5. Synergistic collaboration and transition to the USAWC George Mason University - US Army War College Students developed scenarios 319jw Case Studies inCenter of Gravity Analysis Students developed agents 589jw Military Applications of Artificial Intelligence Use of Disciple in a sequence of two joint warfighting courses Military Education& Practice Military Strategy Research Disciple Formalization ofthe Center of Gravity(COG) analysis process ArtificialIntelligence Research Knowledge bases and agent development by subject matter experts, using learning agent technology. Experiments in the USAWC courses.

6. Sample Domain: Center of Gravity Analysis Centers of Gravity:Primary sources of moral or physical strength, power or resistance of the opposing forces in a conflict. Application to current war scenarios (e.g. War on terror, Iraq)with state and non-state actors (e.g. Al Qaeda). Identify COG candidates Test COG candidates Identify potential primary sources of moral or physical strength, power and resistance from: Test each identified COG candidate to determine whether it has all the necessary critical capabilities: Which are the critical capabilities? Are the critical requirements of these capabilities satisfied? If not, eliminate the candidate. If yes, do these capabilities have any vulnerability? Government Military People Economy Alliances Etc.

7. Problem Solving Approach: Task Reduction Knowledge Base Object Ontology Reduction Rules Composition Rules • A complex problem solving task is performed by: • successively reducing it to simpler tasks; • finding the solutions of the simplest tasks; • successively composing these solutions until the solution to the initial task is obtained.

8. Problem Solving and Learning We need to Identify and test a strategic COG candidatecorresponding to a member of the Allied_Forces_1943 Which is a member of Allied_Forces_1943? US_1943 EXAMPLE OF REASONING STEP Therefore we need to ONTOLOGY FRAGMENT Identify and test a strategic COG candidate for US_1943 LEARNED RULE IF Identify and test a strategic COG candidate corresponding to a member of a force The force is ?O1 IF Identify and test a strategic COG candidate corresponding to a member of the ?O1 FORMAL STRUCTURE Plausible Upper Bound Condition ?O1 is multi_member_force has_as_member ?O2 ?O2 is force QuestionWhich is a member of ?O1 ? Answer?O2 Plausible Lower Bound Condition ?O1 is equal_partners_multi_state_alliance has_as_member ?O2 ?O2 is single_state_force INFORMAL STRUCTURE THEN Identify and test a strategic COG candidate for ?O2 THEN Identify and test a strategic COG candidate for a force The force is ?O2

9. Use of Disciple at the US Army War College 319jw Case Studies in Center of Gravity Analysis Disciple helps the students to perform a center of gravity analysis of an assigned war scenario. Disciple was taught based on the expertise of Prof. Comello in center of gravity analysis. Problemsolving Teaching DiscipleAgent KB Learning Global evaluations of Disciple by officers from the Spring 03 course Disciple helped me to learn to perform a strategic COG analysis of a scenario The use of Disciple is an assignment that is well suited to the course's learning objectives Disciple should be used in future versions of this course

10. Use of Disciple at the US Army War College 589jw Military Applications of Artificial Intelligence course Students teach Disciple their COG analysis expertise, using sample scenarios (Iraq 2003, War on terror 2003, Arab-Israeli 1973) Students test the trained Disciple agent based on a new scenario (North Korea 2003) Global evaluations of Disciple by officers during three experiments I think that a subject matter expert can use Disciple to build an agent, with limited assistance from a knowledge engineer Spring 2001 COG identification Spring 2002 COG identification and testing Spring 2003 COG testing based on critical capabilities

11. Parallel development and merging of knowledge bases 432 concepts and features, 29 tasks, 18 rules For COG identification for leaders Initial KB Domain analysis and ontology development (KE+SME) Knowledge Engineer (KE) All subject matter experts (SME) Training scenarios: Iraq 2003 Arab-Israeli 1973 War on Terror 2003 Parallel KB development (SME assisted by KE) 37 acquired concepts and features for COG testing Extended KB DISCIPLE-COG DISCIPLE-COG DISCIPLE-COG DISCIPLE-COG DISCIPLE-COG stay informed be irreplaceable communicate be influential have support be protected be driving force Team 1 Team 2 Team 3 Team 4 Team 5 5 features 10 tasks 10 rules 14 tasks 14 rules 2 features 19 tasks 19 rules 35 tasks 33 rules 3 features 24 tasks 23 rules KB merging (KE) Learned features, tasks, rules Integrated KB Unified 2 features Deleted 4 rules Refined 12 rules Final KB: +9 features  478 concepts and features +105 tasks 134 tasks +95 rules 113 rules 5h 28min average training time / team 3.53 average rule learning rate / team COG identification and testing (leaders) DISCIPLE-COG Testing scenario: North Korea 2003 Correctness = 98.15%

12. Other Disciple agents Disciple-WA (1997-1998): Estimates the best plan of working around damage to a transportation infrastructure, such as a damaged bridge or road. Demonstrated that a knowledge engineer can use Disciple to rapidly build and update a knowledge basecapturing knowledge from military engineering manuals and a set of sample solutions provided by a subject matter expert. Disciple-COA (1998-1999): Identifies strengths and weaknesses in a Course of Action, based on the principles of war and the tenets of army operations. Demonstrated the generality of its learning methods that used an object ontology created by another group (TFS/Cycorp). Demonstrated that a knowledge engineer and a subject matter expert can jointly teach Disciple.

13. Disciple’s vision on the future of software development Personal Computers Learning Agents Software systems developed and used by persons that are not computer experts Software systems developed by computer experts and used by persons that are not computer experts Mainframe Computers Software systems developed and used by computer experts

14. Vision on the use of Disciple in Education teaches teaches teaches teaches … The expert/teacher teaches Disciple through examples and explanations, in a way that is similar to how the expert would teach a student. DiscipleAgent DiscipleAgent DiscipleAgent DiscipleAgent KB KB KB KB Disciple tutors the student in a way that is similar to how the expert/teacher has taught it.

15. Acknowledgements 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.