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From Personal Computers to Learning Assistants

From Personal Computers to Learning Assistants. Gheorghe Tecuci Learning Agents Center and Computer Science Department School of Information Technology and Engineering George Mason University. 21 September 2005. Overview. Learning Agents Center: Research Vision.

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From Personal Computers to Learning Assistants

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  1. From Personal Computers to Learning Assistants Gheorghe Tecuci Learning Agents Center and Computer Science Department School of Information Technology and Engineering George Mason University 21 September 2005

  2. Overview Learning Agents Center: Research Vision Research Issues for Learning Agents Personal Cognitive Assistant for Intelligence Analysis Agents for Centers of Gravity and Critical Vulnerabilities Virtual Experts for Multi-domain Collaborative Planning Agent for Course of Action Critiquing Final Remarks

  3. http://lac.gmu.edu Mission • Conducts fundamental and experimental research on the development of knowledge-based learning and problem solving agents. • Supports teaching in the areas of intelligent agents, machine learning, knowledge acquisition, artificial intelligence and its applications. • Develops the Disciple theory, methodology and agent shells for building agents that can be taught how to solve problems by subject matter experts. Basic Research Tools Applications Transitions

  4. Teaching as Alternative to Programming Building an intelligent machine by programming is too difficult. “Instead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulates the child's? If this were then subjected to an appropriate course of education one would obtain the adult brain.” Turing, A.M. (1950). Computing machinery and intelligence. Mind, 59, 433-460.

  5. How are Expert Systems Built and Why it is Hard Edward Feigenbaum, 1993:Rarely does a technology arise that offers such a wide range of important benefits.

  6. Research Problem and Approach

  7. Disciple’s Vision on the Future of Software Development Learning Agents Personal Computers Mainframe Computers

  8. Vision on the Use of Disciple in Education teaches teaches teaches DiscipleAgent DiscipleAgent DiscipleAgent DiscipleAgent … KB KB KB KB teaches  2005, Learning Agents Center

  9. Overview Learning Agents Center: Research Vision Research Issues for Learning Agents Personal Cognitive Assistant for Intelligence Analysis Agents for Centers of Gravity and Critical Vulnerabilities Virtual Experts for Multi-domain Collaborative Planning Agent for Course of Action Critiquing Final Remarks

  10. The Overall Architecture of a Disciple Agent  2005, Learning Agents Center

  11. Knowledge Base = Ontology + Rules PROBLEM SOLVING TASK ONTOLOGY FRAGMENT Determine whether John Smith can be a PhD advisor for Tom Even in Artificial Intelligence. IF: Determine whether ?O1 can be a PhD advisor for ?O2 in ?O3. Main condition ?O1 is PhD_advisor has_as_employer ?O4 has_as_position ?O5 ?O2 is PhD_student ?O3 is research_area ?O4 is university ?O5 is tenured_position Except when condition ?O1 is person is_likely_to_move_to ?O6 ?O6 is employer THEN: Determine whether ?O1 would be a good PhD advisor for ?O2 in ?O3. REASONING RULE

  12. Main Idea of the Disciple Approach With Disciple Instruct SME to explain reasoning Import and develop initial ontology Define and explain examples Critique examples SME SME KE SME Agent Agent SME KE Develop reasoning trees Specify instances and features Learn ontological elements Learnreasoning rules Explain critiques Refine rules SME Agent SME Agent Agent SME Agent Agent KE Model the reasoning of SME Create object ontology Define reasoning rules Verify and update rules SME Traditionally Determine whether John Smith can be a PhD advisor for Tom Even in Artificial Intelligence.

  13. Research Issues for Learning Agents Problem Solving Paradigm for Expert-Agent Collaboration Learning with an Evolving Representation Language Plausible Reasoning with Partially Learned Knowledge Integrated Teaching and Learning Multistrategy Learning Agent Architecture for Generality-Power Tradeoff Knowledge Base Structuring for Knowledge Reuse

  14. Problem Solving Paradigm for Expert-Agent Collaboration Task reduction and solution composition guided by questions and answers T1 S1 Q1 … S11 A1n A11 S1n T1n T11a S11a T11b S11b … Q11b S11b … S11bm S11b1 A11bm A11b1 … T11b1 T11bm

  15. Learning with an Evolving Representation Language

  16. Plausible Reasoning with Partially Learned Knowledge IF <task> Plausible Upper Bound Condition<PUB condition> Plausible Lower Bound Condition<PLB condition> THEN <subtask 1> … <subtask m>

  17. Mixed-Initiative Problem Solving Problem Creative solutions Inventive solutions Innovative solutions Routine solutions Solution

  18. Integrated Teaching and Learning examples, facts, rules Input knowledge learning hints Explicit learning guidance classification of examples, problem solutions Problem solving behavior Explicit teaching guidance questions

  19. Rule Learning Method Analogy and Hint Guided Explanation Analogy-based Generalization Plausible version space rule plausible explanations PUB guidance, hints Example of a task reduction step PLB Incomplete explanation analogy Knowledge Base

  20. Multistrategy Learning

  21. Overview Learning Agents Center: Research Vision Research Issues for Learning Agents Personal Cognitive Assistant for Intelligence Analysis Agents for Centers of Gravity and Critical Vulnerabilities Virtual Experts for Multi-domain Collaborative Planning Agent for Course of Action Critiquing Final Remarks

  22. Challenges for the Intelligence Analyst P A H1 Hn Knowledge Difficult to share intelligence Difficult to collaborate with other analysts and experts Overwhelmed by information Intelligence analysis is very difficult Difficult to consider multiple hypotheses Difficult to train new analysts Difficult to rigurously explain the analysis Difficult to find time for critical analysis and AARs Difficult to analyze in reference to the culture of the data source Difficult to acquire and retain expertise Difficult to avoid the analytic mindset

  23. Investigated Solution An integrated approach to intelligence analysis research, education, and operations. • Develop a new type of intelligent agent that • can rapidly acquire expertise in intelligence analysis, • can train new intelligence analysts, and • can assist the analysts to solve complex problems.

  24. Vision: Integration of Research, Education, and Operations Building an agent shell DISCIPLE-LTA 1 Rapid agent development Agent optimization Knowledge baseoptimization and re-use Agent training by expert analyst DISCIPLE-LTA 2 6 DISCIPLE-LTA Expert analyst and knowledge engineer Knowledge engineer and expert analyst Teaching new analysts After action review andagent personalization 3 5 Intelligent tutoring DISCIPLE-LTA DISCIPLE-LTA Analyst Analyst 4 Analyst’s assistant (mixed-initiative learning) Analyst’s assistant (mixed-initiative analysis) Agent use andnon-disruptive learning DISCIPLE-LTA Analyst Knowledge engineer Agent Lifecycle

  25. Vision: Use of Disciple-LTA Agents in an Operational Environment Disciple Client Disciple-LTA GLOBAL KNOWLEDGE BASE SEARCH ENGINES Libraries Knowledge Repositories Massive Databases Disciple-LTA Intelligent agent Disciple-LTA

  26. Synergistic Integration of Research and Education Develop a systematic approach to military intelligence analysis Experimentation with Disciple-LTA in the 589 MAAI elective Military Education& Practice Military Research Working closely with the expert analysts in a multi-disciplinary research Working closely with the end user to receive crucial and timely feedback DiscipleLTA IntelligentAgents Research Agent development by expert analysts using learning agent technology  2005, Learning Agents Center

  27. US Army War College Course 589 Military Applications of Artificial Intelligence: Intelligence Analysis Live Experiment

  28. Assess whether Location-A is a training base for terrorist operations

  29. Assess whether Location-A is a training base for terrorist operations What type of factors should be considered to assess the presence of a terrorist training base?

  30. What type of factors should be considered to assess the presence of a terrorist training base? Political environment, physical structures, flow of suspected terrorists, weapons and weapons technology, other suspected bases in the region, and terrorist sympathetic population

  31. Political environment, physical structures, flow of suspected terrorists, weapons and weapons technology, other suspected bases in the region, and terrorist sympathetic population Assess whether there is a flow of suspected terrorists in the region of Location-A Assess whether there are other suspected bases for terrorist operations in the region of Location-A Assess whether the political environment would support a training base for terrorist operations at Location-A Assess whether there is terrorist sympathetic population in the region of Location-A Assess whether the physical structures at Location-A support the existence of a training base for terrorist operations Assess whether there are weapons and weapons technology at Location-A that suggest the presence of a training base for terrorist operations

  32. Assess whether there is a flow of suspected terrorists in the region of Location-A Assess whether there are other suspected bases for terrorist operations in the region of Location-A Assess whether the political environment would support a training base for terrorist operations at Location-A Assess whether there is terrorist sympathetic population in the region of Location-A Assess whether the physical structures at Location-A support the existence of a training base for terrorist operations Assess whether there are weapons and weapons technology at Location-A that suggest the presence of a training base for terrorist operations

  33. Intelligence Experts Opinion: Quotations REVIEWER #1: a grand challenge to develop an intelligent agent capable of learning, tutoring and decision support … if implemented it would likely be pretty unique. REVIEWER #2: This is an innovative idea that could revolutionize the way we do business, enable us to be more efficient, more effective, more thorough. REVIEWER #3: a very important R&D area for next generation intelligence analysis. The work is well founded, and the execution of real software to implement the ideas is substantial. REVIEWER #4: I have seen a briefing on the work presented here last year and was impressed with the initial ease of use of capturing complex concepts. This could be excellent for use in both training analysts as well as capturing knowledge from more senior analysts.

  34. Overview Learning Agents Center: Research Vision Research Issues for Learning Agents Personal Cognitive Assistant for Intelligence Analysis Agents for Centers of Gravity and Critical Vulnerabilities Virtual Experts for Multi-domain Collaborative Planning Agent for Course of Action Critiquing Final Remarks

  35. DARPA’s Rapid Knowledge Formation Program Develop the Disciple technology to enable teams of subject matter experts to build integrated knowledge bases and agents incorporating their problem solving expertise. Disciple-RKF Assistant Problem solver for a non-expert KB1 ... Disciple-RKF Assistant Expert Assistant of an expert Integrated KB Disciple-RKF Assistant Tutor to a student KBn Expert Successful experiments and transition to the US Army War College

  36. Center of Gravity Analysis The center of gravity of an entity is its primary source of moral or physical strength, power or resistance. Joe Strange, Centers of Gravity & Critical Vulnerabilities, Marine Corps War College, 1996. If a combatant eliminates or influences the enemy’s strategic center of gravity, then the enemy will lose control of its power and resources and will eventually fall to defeat. If the combatant fails to adequately protect his own strategic center of gravity, he invites disaster. P.K. Giles and T.P. Galvin US Army War College, 1996.

  37. Use of Disciple at the US Army War College 589jw Military Applications of Artificial Intelligence Students teach Disciple their COG analysis expertise, using sample scenarios(e.g. 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

  38. 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 05 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

  39. Parallel development and merging of KBs 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%

  40. Current Project Distributed Knowledge Acquisition, Validation, and Maintenance PROBLEM SOLVING AND LEARNING ASSISTANT PROBLEM SOLVING AND LEARNING ASSISTANT Operational Use and Non-Disruptive Learning After Action Review and KB Refinement Knowledge acquired by the agents is validated and integrated into an improved Disciple Knowledge Base Integration Team: Knowledge engineer + Subject matter experts PROBLEM SOLVING AND LEARNING ASSISTANT Operational Use and Non-Disruptive Learning KB INTEGRATION ASSISTANT KB Integration, Validation and Maintenance PROBLEM SOLVING AND LEARNING ASSISTANT After Action Review and KB Refinement Copies of Disciple agents support users’ decision-making and all learn from these experiences. PROBLEM SOLVING AND LEARNING ASSISTANT PROBLEM SOLVING AND LEARNING ASSISTANT Operational Use and Non-Disruptive Learning After Action Review and KB Refinement

  41. Army War College Co-PI, SME Dr. Jerome Comello Experiments in 2005, 2006, 2007 PROBLEM SOLVING AND LEARNING ASSISTANT PROBLEM SOLVING AND LEARNING ASSISTANT Operational Use and Non-Disruptive Learning After Action Review and KB Refinement Integration Team: Knowledge engineer + Subject matter experts PROBLEM SOLVING AND LEARNING ASSISTANT Experimentation Environment 2005, 2006, 2007 Operational Use and Non-Disruptive Learning KB INTEGRATION ASSISTANT KB Integration, Validation and Maintenance PROBLEM SOLVING AND LEARNING ASSISTANT George Mason University After Action Review and KB Refinement Air War College Co-PI, SME Col Jeffrey Hightaian LtCol Todd Kemper Experiments in 2006, 2007 Marine Corps War College Co-PI, SME Dr. Joseph Strange Experiments in 2006, 2007 PROBLEM SOLVING AND LEARNING ASSISTANT PROBLEM SOLVING AND LEARNING ASSISTANT Operational Use and Non-Disruptive Learning After Action Review and KB Refinement

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