Gheorghe%20Tecuci1,2,%20Mihai%20Boicu1,%20Dorin%20Marcu1 - PowerPoint PPT Presentation

Title: Gheorghe%20Tecuci1,2,%20Mihai%20Boicu1,%20Dorin%20Marcu1


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Toward a Disciple-based Mixed-Initiative
Cognitive Assistant
Gheorghe Tecuci1,2, Mihai Boicu1, Dorin Marcu1 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
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Disciple an approach to KB and agent development
Develop a learning agent shell that can be taught
directly by a subject matter expert to become a
knowledge-based assistant
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
The expert teaches the agent 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
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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.
KE
Define domain model
Create ontology
Define rules
Verify and update rules
SME
Traditionally
With Disciple
Import and create initial ontology
Define and explain examples
Critique examples
Define initial model
KE
SME
Agent
SME
Agent
SME
SME
KE
Extend domain model
Specify instances
Learn ontological elements
Learn rules
Refine rules
Explain critiques
Agent
SME
Agent
Agent
SME
Agent
SME
Agent
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Current status Parallel KB development
experiment Sp03
432 concepts and features, 29 tasks, 18 rules For
COG identification for leaders
Initial KB
Domain analysis and ontology development (KESME)
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 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
2.5 examples/rule 5.47 hours average training time
COG identification and testing (leaders)
DISCIPLE-COG
Testing scenario North Korea 2003
Correctness 98.15
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Envisioned life-cycle of future Disciple-MI
Building an agent shell
DISCIPLE-MI
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Knowledge engineer
Agent doctrinal training
Knowledge baseoptimization and re-use
DISCIPLE-MI
2
6
DISCIPLE-MI
Domain experts
Knowledge engineer
Agent Lifecycle
After action review andagent personalization
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5
Intelligent tutoring
DISCIPLE-MI
DISCIPLE-MI
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Agent use andnon-disruptive learning
DISCIPLE-MI
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Research directions
Modeling experts reasoning
Learnable knowledge representation
Multistrategy teaching and learning
Acquisition of experts language
Mixed-initiative problem solving
Resource-bounded learning
Learning a model of the expert
User-agent interaction
KB optimization and integration
Intelligent tutoring
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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.