Transcript Document

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
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 The agent learns from the
1. Mixed-initiative
problem solving
2. Teaching and
learning
3. Multistrategy
learning
to perform various tasks in a
way that resembles how the
expert would teach a person.
expert, building, verifying
and improving its
knowledge base
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 mixedinitiative reasoning, and with very limited assistance from the KE.
Define
domain
model
Create
ontology
KE
SME
Define
rules
Traditionally
Define
initial
model
KE SME
Extend
domain
model
SME Agent
With Disciple
Import and
create initial
ontology
KE SME Agent
Specify
instances
SME
Verify and
update rules
Learn
ontological
elements
Agent
Define and
explain
examples
SME Agent
Learn
rules
Agent
Critique
examples
SME
Explain
critiques
SME Agent
Refine
rules
Agent
Current status: Parallel KB development experiment – Sp03
Initial KB
Domain analysis and ontology
development (KE+SME)
Parallel KB development
(SME assisted by KE)
DISCIPLE-COG
Team 1
stay informed
be irreplaceable
5 features
10 tasks
10 rules
Knowledge
Engineer (KE)
All subject matter
experts (SME)
37 acquired concepts and
Extended KB features for COG testing
DISCIPLE-COG
Team 2
communicate
14 tasks
14 rules
DISCIPLE-COG
Team 3
be influential
2 features
19 tasks
19 rules
KB merging (KE)
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
Correctness = 98.15%
432 concepts and features, 29 tasks, 18 rules
For COG identification for leaders
Training scenarios:
Iraq 2003
Arab-Israeli 1973
War on Terror 2003
DISCIPLE-COG
Team 4
have support
35 tasks
33 rules
DISCIPLE-COG
Team 5
be protected
be driving force
3 features
24 tasks
23 rules
Learned features, tasks, rules
Integrated KB
DISCIPLE-COG
2.5 examples/rule
5.47 hours average training time
COG identification and testing (leaders)
Testing scenario:
North Korea 2003
Envisioned life-cycle of future Disciple-MI
Building an agent shell
DISCIPLE-MI
Knowledge
engineer
1
Agent doctrinal
training
Knowledge base
optimization
and re-use
DISCIPLE-MI
2
6
DISCIPLE-MI
Domain experts
Knowledge
engineer
Intelligent tutoring
3
Agent
Lifecycle
DISCIPLE-MI
5
After action review and
agent personalization
DISCIPLE-MI
4
Agent use and
non-disruptive learning
DISCIPLE-MI
Research directions
Modeling expert’s reasoning
Learnable knowledge representation
Multistrategy teaching and learning
Acquisition of expert’s language
Mixed-initiative problem solving
Resource-bounded learning
Learning a model of the expert
User-agent interaction
KB optimization and integration
Intelligent tutoring
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.