Knowledge Acquisition and Problem Solving

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CS 7850 Fall 2004
Knowledge Acquisition and Problem Solving
Introduction
Gheorghe Tecuci tecuci_at_gmu.eduhttp//lac.gmu.edu
/
Learning Agents Center and Computer Science
Department George Mason University
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Overview
Class introduction and courses objectives
Artificial Intelligence and intelligent agents
Domain for hands-on experience
Knowledge acquisition for agents development
Overview of the course
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Cartoon
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Course Objectives
Provide an overview of Knowledge Acquisition and
Problem Solving.
Present principles and major methods of knowledge
acquisition for the development of
knowledge-based agents that incorporate the
problem solving knowledge of a subject matter
expert. Major topics include overview of
knowledge engineering analysis and modeling of
the reasoning process of a subject matter expert
ontology design and development rule learning
problem solving and knowledge-base refinement.
The course will emphasize the most recent
advances in this area, such as agent teaching
and learning mixed-initiative knowledge base
refinement knowledge reuse frontier research
problems.
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Course Objectives (cont)
Link Knowledge Acquisition and Problem Solving
concepts to hands-on applications by building a
knowledge-based agent.
Learn about all the phases of building a
knowledge-based agent and experience them
first-hand by using the Disciple agent
development environment to build an intelligent
assistant that helps the students to choose a
Ph.D. Dissertation Advisor. Disciple has been
developed in the Learning Agents Center of George
Mason University and has been successfully used
to build knowledge-based agents for a variety of
problem areas, including planning the repair of
damaged bridges and roads critiquing military
courses of action determining strategic centers
of gravity in military conflicts generating test
questions for higher-order thinking skills in
history and statistics.
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Course organization and grading policy
Course organization

• The classes will consist of
• a theoretical recitation part where the
  instructor will present and discuss the various
  methods and phases of building a knowledge-based
  agent
• a practical laboratory part where the students
  will apply this knowledge to specify, design and
  develop the Ph.D. selection advisor.

Regular assignments will consist of incremental
developments of the Ph.D. selection advisor which
will be presented to the class.
Grading Policy - Exam, covering the theoretical
aspects presented 50 - Assignments,
consisting of lab participation and the
contribution to the development of the Ph.D.
selection advisor 50
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Readings
Lecture notes provided by the instructor
(required). Tecuci G., Building Intelligent
Agents An Apprenticeship Multistrategy Learning
Theory, Methodology, Tool and Case Studies,
Academic Press, 1998 (recommended). Additional
papers recommended by the instructor.
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Overview
Class introduction and courses objectives
Artificial Intelligence and intelligent agents
Domain for hands-on experience
Knowledge acquisition for agents development
Overview of the course
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Artificial Intelligence and intelligent agents
What is Artificial Intelligence
What is an intelligent agent
Characteristic features of intelligent agents
Sample tasks for intelligent agents
Why are intelligent agents important
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What is Artificial Intelligence
Artificial Intelligence is the Science and
Engineering that is concerned with the theory and
practice of developing systems that exhibit the
characteristics we associate with intelligence in
human behavior perception, natural language
processing, reasoning, planning and problem
solving, learning and adaptation, etc.
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Central goals of Artificial Intelligence
Understand the principles that make intelligence
possible(in humans, animals, and artificial
agents)
Developing intelligent machines or agents(no
matter whether they operate as humans or not)
Formalizing knowledge and mechanizing
reasoningin all areas of human endeavor
Making the working with computers as easy as
working with people
Developing human-machine systems that exploit the
complementariness of human and automated
reasoning
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Artificial Intelligence and intelligent agents
What is Artificial Intelligence
What is an intelligent agent
Characteristic features of intelligent agents
Sample tasks for intelligent agents
Why are intelligent agents important
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What is an intelligent agent

• An intelligent agent is a system that
• perceives its environment (which may be the
  physical world, a user via a graphical user
  interface, a collection of other agents, the
  Internet, or other complex environment)
• reasons to interpret perceptions, draw
  inferences, solve problems, and determine
  actions and
• acts upon that environment to realize a set of
  goals or tasks for which it was designed.

input/
sensors
IntelligentAgent
output/
user/ environment
effectors
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What is an intelligent agent (cont.)
Humans, with multiple, conflicting drives,
multiple senses, multiple possible actions, and
complex sophisticated control structures, are at
the highest end of being an agent. At the low
end of being an agent is a thermostat.It
continuously senses the room temperature,
starting or stopping the heating system each time
the current temperature is out of a pre-defined
range. The intelligent agents we are concerned
with are in between. They are clearly not as
capable as humans, but they are significantly
more capable than a thermostat.
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What is an intelligent agent (cont.)
An intelligent agent interacts with a human or
some other agents via some kind of
agent-communication language and may not blindly
obey commands, but may have the ability to modify
requests, ask clarification questions, or even
refuse to satisfy certain requests. It can
accept high-level requests indicating what the
user wants and can decide how to satisfy each
request with some degree of independence or
autonomy, exhibiting goal-directed behavior and
dynamically choosing which actions to take, and
in what sequence.
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What an intelligent agent can do

• An intelligent agent can
• collaborate with its user to improve the
  accomplishment of his or her tasks
• carry out tasks on users behalf, and in so doing
  employs some knowledge of the user's goals or
  desires
• monitor events or procedures for the user
• advise the user on how to perform a task
• train or teach the user
• help different users collaborate.

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Artificial Intelligence and intelligent agents
What is Artificial Intelligence
What is an intelligent agent
Characteristic features of intelligent agents
Sample tasks for intelligent agents
Why are intelligent agents important
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Knowledge representation and reasoning
An intelligent agent contains an internal
representation of its external application
domain, where relevant elements of the
application domain (objects, relations, classes,
laws, actions) are represented as symbolic
expressions.
Model of the Domain
Application Domain
ONTOLOGY
represents
If an object is on top of another object that is
itself on top of a third object then the first
object is on top of the third object.
RULE ? x,y,z ? OBJECT, (ON x y) (ON y z) ?
(ON x z)
(cup1 on book1) (book1 on table1) ? (cup1 on
table1)
(cup1 on table1)
This mapping allows the agent to reason about the
application domain by performing reasoning
processes in the domain model, and transferring
the conclusions back into the application domain.
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Basic agent architecture
Implements a general method of interpreting the
input problem based on the knowledge from the
knowledge base
Intelligent Agent
Input/
Problem Solving Engine
Sensors
User/ Environment
Ontology Rules/Cases/
Knowledge Base
Output/
Effectors
Data structures that represent the objects from
the application domain, general laws governing
them, action that can be performed with them,
etc.
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There are two basic components of an agent the
knowledge base and the problem solving
engine. The knowledge base contains data
structures that represent the application domain.
It includes representations of objects and their
relations (the object ontology), but also
representations of laws, actions, rules, cases or
elementary problem solving methods. The problem
solving engine implements a problem solving
method that manipulates the data structures in
the knowledge base to reason about the input
problem, to solve it, and to determine the
actions to perform next. That is, there is a
clear separation between knowledge (which is
contained into the knowledge base) and control
(represented by the problem solving engine). This
separation allows the development of general
tools, or shells, that do not contain any domain
specific knowledge in the knowledge base. By
defining this knowledge, one can develop a
specific agent. The idea of these tools is to
re-use the problem solving engine for a new
application by defining the appropriate content
of the knowledge base.
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Transparency and explanations

• The knowledge possessed by the agent and its
  reasoning processes should be understandable to
  humans.
• The agent should have the ability to give
  explanations of its behavior, what decisions it
  is making and why.
• Without transparency it would be very difficult
  to accept, for instance, a medical diagnosis
  performed by an intelligent agent.
• The need for transparency shows that the main
  goal of artificial intelligence is to enhance
  human capabilities and not to replace human
  activity.

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Ability to communicate

• An agent should be able to communicate with its
  users or other agents.
• The communication language should be as natural
  to the human users as possible. Ideally, it
  should be free natural language.
• The problem of natural language understanding and
  generation is very difficult due to the ambiguity
  of words and sentences, the paraphrases, ellipses
  and references which are used in human
  communication.

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Use of huge amounts of knowledge

• In order to solve "real-world" problems, an
  intelligent agent needs a huge amount of domain
  knowledge in its memory (knowledge base).
• Example of human-agent dialog
• User The toolbox is locked.
• Agent The key is in the drawer.
• In order to understand such sentences and to
  respond adequately, the agent needs to have a lot
  of knowledge about the user, including the goals
  the user might want to achieve.

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Use of huge amounts of knowledge (example)
User The toolbox is locked. Agent Why is he
telling me this? I already know that the box is
locked. I know he needs to get in. Perhaps he
is telling me because he believes I can help. To
get in requires a key. He knows it and he knows
I know it. The key is in the drawer. If he knew
this, he would not tell me that the toolbox is
locked. So he must not realize it. To make him
know it, I can tell him. I am supposed to help
him. The key is in the drawer.
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Exploration of huge search spaces
An intelligent agent usually needs to search huge
spaces in order to find solutions to
problems. Example A search agent on the
Internet.
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Use of heuristics
Intelligent agents generally attack problems for
which no algorithm is known or feasible, problems
that require heuristic methods.

• A heuristic is a rule of thumb, strategy, trick,
  simplification, or any other kind of device which
  drastically limits the search for solutions in
  large problem spaces.
• Heuristics do not guarantee optimal solutions. In
  fact they do not guarantee any solution at all.
• A useful heuristic is one that offers solutions
  which are good enough most of the time.

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Reasoning with incomplete or conflicting data
The ability to provide some solution even if not
all the data relevant to the problem is available
at the time a solution is required.
Examples The reasoning of a physician in an
intensive care unit. Planning a military course
of action.
Example The reasoning of a military
intelligence analyst that has to cope with the
deception actions of the enemy.
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Ability to learn
The ability to improve its competence and
performance.

• An agent is improving its competence if it learns
  to solve a broader class of problems, and to make
  fewer mistakes in problem solving.
• An agent is improving its performance if it
  learns to solve more efficiently (for instance,
  by using less time or space resources) the
  problems from its area of competence.

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Extended agent architecture
The learning engine implements methods for
extending and refining the knowledge in the
knowledge base.
Intelligent Agent
Problem Solving Engine
Input/
Sensors
Learning Engine
User/ Environment
Output/
Ontology Rules/Cases/Methods
Knowledge Base
Effectors
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Artificial Intelligence and intelligent agents
What is Artificial Intelligence
What is an intelligent agent
Characteristic features of intelligent agents
Sample tasks for intelligent agents
Why are intelligent agents important
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Sample tasks for intelligent agents
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Sample tasks for intelligent agents (cont.)
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Sample tasks for intelligent agents (cont.)
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Sample tasks for intelligent agents (cont.)
Any useful task Information fusion. Information
assurance. Travel planning. Email
management. Help in choosing a Ph.D.
Dissertation Advisor
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Artificial Intelligence and intelligent agents
What is Artificial Intelligence
What is an intelligent agent
Characteristic features of intelligent agents
Sample tasks for intelligent agents
Why are intelligent agents important
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Why are intelligent agents important
Humans have limitations that agents may alleviate
(e.g. memory for the details that isnt effected
by stress, fatigue or time constraints).
Humans and agents could engage in
mixed-initiative problem solving that takes
advantage of their complementary strengths and
reasoning styles.
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Why are intelligent agents important (cont)
The evolution of information technology makes
intelligent agents essential components of our
future systems and organizations.
Our future computers and most of the other
systems and tools will gradually become
intelligent agents.
We have to be able to deal with intelligent
agents either as users, or as developers, or as
both.
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Intelligent agents Conclusion
Intelligent agents are systems which can perform
tasks requiring knowledge and heuristic methods.
Intelligent agents are helpful, enabling us to do
our tasks better.
Intelligent agents are necessary to cope with the
increasing complexity of the information society.
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Overview
Class introduction and courses objectives
Artificial Intelligence and intelligent agents
Domain for hands-on experience
Knowledge acquisition for agents development
Overview of the course
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Problem Choosing a Ph.D. Dissertation Advisor
Choosing a Ph.D. Dissertation Advisor is a
crucial decision for a successful dissertation
and for ones future career. An informed
decision requires a lot of knowledge about the
potential advisors. In this course we will
develop an agent that interacts with a student to
help selecting the best Ph.D. advisor for that
student. See the project notes 1. Problem
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Overview
Class introduction and courses objectives
Artificial Intelligence and intelligent agents
Domain for hands-on experience
Knowledge acquisition for agents development
Overview of the course
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Knowledge Acquisition for agent development
Approaches to knowledge acquisition
Disciple approach to agent development
Demo Agent teaching and learning
Research vision on agents development
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How are agents built Manual knowledge acquisition
Intelligent Agent
Problem Solving Engine
Subject Matter Expert
Knowledge
Engineer
Dialog
Programming
Knowledge Base
Results
A knowledge engineer attempts to understand how a
subject matter expert reasons and solves problems
and then encodes the acquired expertise into the
agent's knowledge base. The expert analyzes the
solutions generated by the agent (and often the
knowledge base itself) to identify errors, and
the knowledge engineer corrects the knowledge
base.
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Why it is hard
The knowledge engineer has to become a kind of
subject matter expert in order to properly
understand experts problem solving knowledge.
This takes time and effort. Experts express
their knowledge informally, using natural
language, visual representations and common
sense, often omitting essential details that are
considered obvious. This form of knowledge is
very different from the one in which knowledge
has to be represented in the knowledge base
(which is formal, precise, and complete). This
transfer and transformation of knowledge, from
the domain expert through the knowledge engineer
to the agent, is long, painful and inefficient
(and is known as "the knowledge acquisition
bottleneck of the AI systems development
process).
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Mixed-initiative knowledge acquisition
Intelligent Learning Agent
Problem Solving Engine
Subject Matter Expert
Dialog
Knowledge
Learning Engine
Knowledge Base
Results
The expert teaches the agent how to perform
various tasks, in a way that resembles how an
expert would teach a human apprentice when
solving problems in cooperation. This process is
based on mixed-initiative reasoning that
integrates the complementary knowledge and
reasoning styles of the subject matter expert and
the agent, and on a division of responsibility
for those elements of knowledge engineering for
which they have the most aptitude, such that
together they form a complete team for knowledge
base development.
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Mixed-initiative knowledge acquisition (cont.)
This is the most promising approach to overcome
the knowledge acquisition bottleneck. DARPAs
Rapid Knowledge Formation Program
(2000-2004) Emphasized the development of
knowledge bases directly by the subject matter
experts.  Central objective Enable distributed
teams of experts to enter and modify knowledge
directly and easily, without the need for prior
knowledge engineering experience. The emphasis
was on content and the means of rapidly acquiring
this content from individuals who possess it with
the goal of gaining a scientific understanding of
how ordinary people can work with formal
representations of knowledge.  Programs primary
requirement Development of functionality
enabling experts to understand the contents of a
knowledge base, enter new theories, augment and
edit existing knowledge, test the adequacy of the
knowledge base under development, receive
explanations of theories contained in the
knowledge base, and detect and repair errors in
content.
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Autonomous knowledge acquisition
Autonomous Learning Agent
Problem Solving Engine
Knowledge
Learning Engine
Data
Knowledge Base
Data Base
Results
The learning engine builds the knowledge base
from a data base of facts or examples. In
general, the learned knowledge consists of
concepts, classification rules, or decision
trees. The problem solving engine is a simple
one-step inference engine that classifies a new
instance as being or not an example of a learned
concept. Defining the Data Base of examples is a
significant challenge. Current practical
applications limited to classification tasks.
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Autonomous knowledge acquisition (cont.)
Autonomous Language Understanding and Learning
Agent
Problem Solving Engine
Text
Text Understanding Engine
Knowledge
Data
Learning Engine
Knowledge Base
Results
The knowledge base is built by the learning
engine from data provided by the text
understanding system able to understand
textbooks. In general, the data consists of facts
acquired from the books. This is not yet a
practical approach, even for simpler agents.
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Knowledge Acquisition for agent development
Approaches to knowledge acquisition
Disciple approach to agent development
Demo Agent teaching and learning
Research vision on agents development
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Disciple approach to agent development
Research Problem Elaborate a theory, methodology
and family of systems for the development of
knowledge-based 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
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Sample 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
base capturing 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.
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A Disciple agent for Center of Gravity
determination
The center of gravity of an entity (state,
alliance, coalition, or group) is the foundation
of capability, the hub of all power and movement,
upon which everything depends, the point against
which all the energies should be directed. Carl
Von Clausewitz, On War, 1832.
If a combatant eliminates or influences the
enemys 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. (Giles and Galvin, USAWC 1996).
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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 gravitydeterminati
on process
ArtificialIntelligence Research
Knowledge bases and agent development by subject
matter experts, using learning agent technology.
Experiments in the USAWC courses.
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Approach to Center of Gravity (COG) determination

• Based on the concepts of critical capabilities,
  critical requirements and critical
  vulnerabilities, which have been recently adopted
  into the joint military doctrine of USA (Strange
  , 1996).
• Applied to current war scenarios (e.g. War on
  terror 2003, Iraq 2003) with state and non-state
  actors (e.g. Al Qaeda).

Identification of COG candidates
Testing of 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.
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Problem Solving Approach Task Reduction

• 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.

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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
Question Which 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
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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
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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
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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 (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 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
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Knowledge Acquisition for agent development
Approaches to knowledge acquisition
Disciple approach to agent development
Demo Agent teaching and learning
Research vision on agents development
61
Demonstration
Teaching Disciple how to determine whether a
strategic leader has the critical capability to
be protected.
DiscipleDemo
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Knowledge Acquisition for agent development
Approaches to knowledge acquisition
Disciple approach to agent development
Demo Agent teaching and learning
Research vision on agents development
63
Vision on the future of software development
Mainframe Computers
Software systems developed and used by computer
experts
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The military applications presented in this
session show that Disciple has reached a
significant level of maturity, being usable to
rapidly develop complex knowledge based agents.
However, these are only initial results of a
long-term research aimed at changing the way
intelligent agents are built, from being
programmed by a knowledge engineer to being
taught by a user that does not have prior
knowledge engineering experience. Making this
vision a reality would allow a normal computer
user, who is not a trained knowledge engineer, to
build by himself an intelligent assistant as
easily as he now uses a word processor to write a
paper. It is expected that this research will
contribute to a new revolution in the use of
computers, probably even more important than the
creation of personal computers. Indeed, it will
allow every person to no longer be only a user of
pro-grams developed by others, but also an agent
developer himself.
65
Overview
Class introduction and courses objectives
Artificial Intelligence and intelligent agents
Domain for hands-on experience
Knowledge acquisition for agents development
Overview of the course
66
Overview of the course
Overview of knowledge engineering and ofthe
manual knowledge acquisition methods.
Mixed-initiative knowledge acquisition. Overview
of the Disciple approach.
Problem solving through task reduction. Modeling
the reasoning of subject matter experts.
Ontology design and development.
Development of an assistant for choosinga Ph.D.
Dissertation Advisor
Agent teaching and multistrategy learning.
Mixed-initiative problem solving and knowledge
base refinement.
Knowledge bases integration.
Scripts development for scenario elicitation.
Discussion of frontier research problems.
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Additional recommended reading
G. Tecuci, Building Intelligent Agents, Academic
Press, 1998, pp. 1-12.