Knowledge Acquisition and Problem Solving

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CS 785 Fall 2004
Knowledge Acquisition and Problem Solving
Knowledge engineering Advanced approaches
Gheorghe Tecuci tecuci_at_gmu.eduhttp//lac.gmu.edu
/
Learning Agents Center and Computer Science
Department George Mason University
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Overview
Limits of the classical knowledge engineering
approaches
Advanced approaches to agent development
Learning agent shells
A Disciple agent for center of gravity analysis
Demo Use of a Disciple agent as a
decision-making assistant
Design principles for instructable agents
Demo Training a Disciple agent
Research problems and research visions
Recommended readings
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How are agents built
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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Limiting factors in developing intelligent agents
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Overview
Limits of the classical knowledge engineering
approaches
Advanced approaches to agent development
Learning agent shells
A Disciple agent for center of gravity analysis
Demo Use of a Disciple agent as a
decision-making assistant
Design principles for instructable agents
Demo Training a Disciple agent
Research problems and research visions
Recommended readings
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Advanced approaches to KB and agent development
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Advanced approaches to KB and agent development
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Advanced approaches to KB and agent development
Remark Software maintenance is estimated to be
about four times more expensive that software
development. With learning agents that are
directly taught by humans, there is no longer a
distinction between building the agent and
maintaining it.
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Advanced approaches to KB and agent development
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Overview
Limits of the classical knowledge engineering
approaches
Advanced approaches to agent development
Learning agent shells
A Disciple agent for center of gravity analysis
Demo Use of a Disciple agent as a
decision-making assistant
Design principles for instructable agents
Demo Training a Disciple agent
Research problems and research visions
Recommended readings
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Expert system shell
An expert system is a system that can help solve
complex, real-world problems, in specific
scientific, engineering, medical specialties,
etc., by using large bodies of domain knowledge
(facts and procedures) obtained from human
experts, that have proven useful for solving
typical problems in their domain.
Expert System Shell
An expert system shell is a system that consists
of an inference engine for a certain class of
tasks (like planning, design, diagnosis,
monitoring, prediction, interpretation, etc.) and
supports representation formalisms in which a
knowledge base can be encoded.
Problem Solving Engine
EmptyKnowledge Base
If the inference engine is adequate for a
certain expert task (e.g. planning), then the
process of building the expert system is reduced
to the building of the knowledge base.
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Learning agent shell definition
A learning agent shell is a tool for building
agents. It contains a general problem solving
engine, a learning engine and an empty knowledge
base structured into an object ontology and a set
of rules. Building an agent for a specific
application consists in customizing the shell for
that application and in developing the knowledge
base. The learning engine facilitates the
building of the knowledge base by subject matter
experts and knowledge engineers.
Problem Solving
Ontology Rules
Interface
Learning
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Disciple learning agent shell

• The Disciple learning agent shell
• can use imported ontological knowledge
• solves problems through task reduction
• can be taught directly by subject matter experts
  to become a knowledge-based assistant.

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
Mixed-initiative reasoning between the expert
that has the knowledge to be formalized and the
agent that knows how to formalize it.
Problem Solving
Ontology Rules
Interface
Learning
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Main idea of the Disciple mixed-initiative
approach
The complex knowledge engineering activities,
traditionally performed by a knowledge engineer
with assistance from a subject matter expert, are
replaced with equivalent ones performed by the
subject matter expert and a learning agent,
through mixed-initiative reasoning, and with
limited assistance from the knowledge engineer.
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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A Disciple agent for action planning
Disciple-WA (1997-1998) Estimates the best
plan of working around damage to a transportation
infrastructure, such as a damaged bridge or road.
Disciple-WA 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.
72 increase of KB size in 17 days
Evolution of KB coverage and performance from the
pre-repair phase to the post-repair phase.
Disciple-WA features

• High knowledge acquisition rate
• High problem solving performance (including
  unanticipated solutions).
• Demonstrated at EFX98 as part of an integrated
  application led by Alphatech.

Development of Disciples KB during evaluation.
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A Disciple agent for course of action critiquing
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.
Disciple-COA demonstrated the generality of its
learning methods that used an object ontology
created by another group (TFS/Cycorp).
It also demonstrated that a knowledge engineer
and a subject matter expert can jointly teach
Disciple.
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46 increase of KB size in 8 days
Evolution of KB coverage and performance from the
pre-repair phase to the post-repair phase for
the final 3 evaluation items.
Disciple-COA features

• High knowledge acquisition rate
• Better performance than the other evaluated
  systems
• Better performance than the evaluating experts
  (many unanticipated solutions).

Development of Disciples KB during evaluation.
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Knowledge acquisition experiment at BCBL, Ft.
Leavenworth
Questionnaire All comments consider the fact that
Disciple is a research prototype. Degree of
agreement with a statement 1 (not at all) to 5
(very).
Showed that a subject matter expert, who does not
have prior knowledge engineering experience, can
be rapidly trained to teach Disciple to critique
COAs, based on a given model of the COA
critiquing process.
LTC John N. Duquette LTC Jay E. Farwell MAJ
Michael P. Bowman MAJ Dwayne E. Ptaschek
Disciple COA
IKB
KB extended with 26 rules and 28 tasks
in 3 hours
KB development during experimentation.
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Overview
Limits of the classical knowledge engineering
approaches
Advanced approaches to agent development
Learning agent shells
A Disciple agent for center of gravity analysis
Demo Use of a Disciple agent as a
decision-making assistant
Design principles for instructable agents
Demo Training a Disciple agent
Research problems and research visions
Recommended readings
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Disciple-RKF An agent for center of gravity
analysis
Goal Develop the technology that enables teams
of subject matter experts to build integrated
knowledge bases and agents incorporating their
problem solving expertise.
Parallel Agent Training and KB Development
KBs Integration
Agent Use
The mediator team integrates the knowledge bases
developed by each subject matter expert and
personal Disciple-RKF agent.
Each SME teaches a personal Disciple-RKF learning
agent how to solve problems, in a way that
resembles how the expert would teach a human
apprentice.
Disciple-RKF with the integrated KB is used in
practical applications.
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
Knowledge bases integration experiment at the US
Army War College (2003).
Three agent training and knowledge bases
development experiments (2001, 2002, 2003).
Disciple agents regularly used in two courses at
US Army War College (2001-2004).
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Synergistic collaboration and transition at 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.
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Sample Domain Center of Gravity Analysis
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.
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, 1996.
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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First computational approach to COG analysis

• Approach to center of gravity analysis based on
  the concepts ofcritical capabilities, critical
  requirements and critical vulnerabilities, which
  have been recently adopted into the joint
  military doctrine.

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.
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Student Disciple collaboration
Disciple
Student
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The student is guided by Disciple to describe the
relevant aspects of a strategic environment.
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Disciple identifies and tests COG candidates
The students study the logic behind COG
identification and testing
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Disciple generates a COG analysis report
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Spring 2003 scenarios and COGs selected
War on Terror 2003
Iraq 2003
Al Qaeda 2003 Terrorist Cells of Al
Qaeda Muslim non-state actors neutral to Al
Qaeda US Coalition 2003 will of the people of
US Muslim non-state actors neutral to Al Qaeda
Iraq Saddam Hussein US led coalition will of
the people of United States will of the people
of Great Britain
LTC Thomas T. Smith LTC Joseph P. Schweitzer
LTC Michael S. Yarmie CDR John J. Welsh
North Korea 2003
Israel-PLO 2003
Israel financial capacity of
Israel Palestine external support from Arab
Countries to Palestine Liberation Organization
North Korea military of North Korea US Led
Coalition will of the people of United States
COL Douglas J. Lee COL Robert F. Barry
COL Christian E. de Graff LTC Robert D. Grymes
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Demonstration
Strategic leaders assistant
Disciple
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Overview
Limits of the classical knowledge engineering
approaches
Advanced approaches to agent development
Learning agent shells
A Disciple agent for center of gravity analysis
Demo Use of a Disciple agent as a
decision-making assistant
Design principles for instructable agents
Demo Training a Disciple agent
Research problems and research visions
Recommended readings
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Generality-Power Tradeoff
Structure the architecture into a reusable
domain-independent learning agent shell and
domain specific modules
Disciple Agent
Learning Agent Shell
Graphical User Interface
Customized User Interface
Knowledge Repository
Knowledge Acquisition and Learning
Customized Problem Solver
Problem Solver
Knowledge Base Manager
Domain Independent Modules
Domain Dependent Plug-in Modules
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Cognitive Functions
Make separate modules for each cognitive
function, such as communication, problem solving,
learning, and knowledge management
Implement each cognitive module as a
collaborative agent, in a mixed-initiative
framework
Disciple each module is implemented as a set of
collaborative agents
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Problem Solving Paradigm
Use a general problem solving paradigm, that can
be applied to a wide range of application domains
and develop a methodology to help the subject
matter experts express their reasoning and teach
the agent using it
Disciple the task reduction paradigm

• A complex problem solving task is performed by
• successively reducing it to simpler tasks
• finding the solutionsof the simplest tasks
• successively composing these solutions until the
  solution to the initial task is obtained.

S1
T1

S11
S1n
T1n
T11

S111
T111
S11m
T11m
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Question-answering based task reduction
Let T1 be the problem solving task to be
performed. Finding a solution is an iterative
process where, at each step, we consider some
relevant information that leads us to reduce the
current task to a simpler task or to several
simpler tasks. The question Q associated with
the current task identifies the type of
information to be considered. The answer A
identifies that piece of information and leads us
to the reduction of the current task.

T1
S1
Q1

S11
A1n
A11
S1n
T1n
S11a
T11a
S11b
T11b

S11b
Q11b

S11bm
S11b1
A11bm
A11b1

T11bm
T11b1
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Task Reduction Example COG Analysis
Rule_1
Rule_2
Rule_3
Rule_4
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Knowledge Base Structuring
Structure the knowledge base into its more
general and reusable components, and its more
specific components

• Disciple separation between
• the ontology that defines the concepts and
  features from an application domain (which is a
  more general component and may be reused from
  existing knowledge repositories)
• the set of problem solving rules (which is a
  more specific component and is learned from the
  subject matter expert)

Knowledge Base
Ontology
Rules
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Disciple Ontology Fragment
A hierarchical representation of the objects and
types of objects.
A hierarchical representation of the types of
features.
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Disciple Example of a Task Reduction Rule
We need to
Identify and test a strategic COG candidate
corresponding to a member of the
Allied_Forces_1943
Which is a member of Allied_Forces_1943?
EXAMPLE OF REASONING STEP
US_1943
Therefore we need to
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
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
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
INFORMAL STRUCTURE
FORMAL STRUCTURE
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Partially Learned Knowledge
Allow the representation, use, and refinement of
partially learned knowledge
Plausible version space (PVS)

• Disciple use of plausible version spaces (PVS)
  to represent and use partially learned knowledge
• Rules with PVS conditions
• Tasks with PVS conditions
• Features with the domain and range represented
  as PVS conditions

Universe of Instances
Plausible Upper Bound
Concept
Plausible Lower Bound
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Integrated Problem Solving and Learning
Develop a methodology where the subject matter
expert and the agent solve problems in
cooperation and the agent learns from the problem
solving contributions of the expert, and from its
own problem solving attempts
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Disciple Problem-Solving and Learning
Learning
Modeling
Problem Solving
Refining
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Integrated Teaching and Learning
Develop a methodology where the subject matter
expert helps the agent to learn (e.g. by
providing examples, hints and explanations), and
the agent helps the subject matter expert to
teach it (e.g. by asking relevant questions)
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Find an explanation of why the example is correct

We need to
Identify and test a strategic COG candidate
corresponding to a member of the
Allied_Forces_1943
Which is a member of Allied_Forces_1943?
US_1943
Therefore we need to
Identify and test a strategic COG candidate for
US_1943
The explanation is an approximation of the
question and the answer, in the object ontology.
has_as_member
US_1943
Allied_Forces_1943
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We need to
Generate the PVS rule
Identify and test a strategic COG candidate
corresponding to a member of a force The force
is Allied_Forces_1943
has_as_member
US_1943
Allied_Forces_1943
Therefore we need to
Identify and test a strategic COG candidate for a
force The force is US_1943

IF Identify and test a strategic COG candidate
corresponding to a member of a force The force
is ?O1
Rewrite as
explanation ?O1 has_as_member ?O2
Most general generalization
Plausible Upper Bound Condition
?O1 is multi_member_force has_as_member ?O2
?O2 is force
Condition ?O1 is Allied_Forces_1943
has_as_member ?O2 ?O2 is US_1943
Plausible Lower Bound Condition
?O1 is equal_partners_multi_state_alliance has_as
_member ?O2 ?O2 is single_state_force
Most specific generalization
has_as_member domain multi_member_force
range force
THEN Identify and test a strategic COG candidate
for a force The force is ?O2
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Multistrategy Learning
Integrate several learning strategies, taking
advantage of their complementary strengths to
compensate for each others weaknesses
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Disciple End to End Agent Development Methodology
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Demonstration
Teaching Disciple how to determine whether a
strategic leader has the critical capability to
be protected.
DiscipleDemo
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Overview
Limits of the classical knowledge engineering
approaches
Advanced approaches to agent development
Learning agent shells
A Disciple agent for center of gravity analysis
Demo Use of a Disciple agent as a
decision-making assistant
Design principles for instructable agents
Demo Training a Disciple agent
Research problems and research visions
Recommended readings
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Present research problem
Elaborate a theory, methodology and system for
the development of knowledge bases and agents by
subject matter experts, with limited assistance
from knowledge engineers.
IntelligentAgent
Knowledge Base
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What are the main technical challenges
1. Automating the domain modeling process that
consists of making explicit, at an informal
level, the way the expert solves problems.
2. Building the initial generic object ontology
through import from external repositories and
direct elicitation from a subject matter expert.
3. Populating the generic object ontology with
instances and relationships that describe a
specific situation or scenario.
4. Learning complex problem solving rules
directly from a subject matter expert.
5. Learning object concepts that extend the
generic ontology directly from a subject matter
expert.
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How are these challenges addressed

• Develop a general approach to domain modeling
  that allows a subject matter expert to express
  the way he or she performs a task based on the
  task reduction paradigm.
• Structure the knowledge base into an object
  ontology that can be imported/reused and a set of
  problem solving rules that can be learned from a
  subject matter expert.
• Develop methods to import/reuse ontological
  knowledge from previously developed knowledge
  bases or repositories.
• Develop a learnable knowledge representation that
  can express partially learned knowledge and can
  be used in reasoning.
• Develop multistrategy learning methods that
  synergistically integrate several learning
  strategies.
• Develop methods for integrated teaching and
  learning where the SME helps the agent to learn,
  and the agent helps the SME to teach it.
• Use of plausible reasoning to hypothesize
  solutions based on incomplete and partially
  incorrect knowledge.

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Research goal Life-long continuous agent learning
1. Multistrategy teaching and learning
Implicit reasoning of human expert
Explicit reasoning in natural language
Ontology extensions
Modeling
Ontology Elicitation
Rule Ontology Learning

• Plausible version spaces
• Learning from instruction
• Learning from examples
• Learning from explanations
• Learning by analogy

• Analogy based methods
• Explanation based methods
• Natural Language based methods
• Abstraction based methods

Learned rules, ontology
Learning Agent
2. Mixed-initiative problem solving and learning
Rule Ontology Refining
KB Maintenance
4. KB maintenance and optimization
Refined rules, ontology

• Automatic inductive learning
• Case-based learning
• Abductive learning
• Ontology discovery
• KB optimization
• KB maintenance

• Mixed-initiative learning
• Routine, innovative,
• inventive, and creative reasoning

Rules w/o exceptions
Non-disruptive Learning
User Model Learning
Exception Handling
User model
Cases, rules
3. Autonomous (and interactive) multistrategy
learning
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Long term research vision
Develop a capability that will allow subject
matter experts and typical computer users to
build and maintain knowledge bases and agents, as
easily as they use personal computers for text
processing.
This research aims at changing the way future
knowledge-based agents will be built, from being
programmed by computer scientists and knowledge
engineers, to being taught by subject matter
experts and typical computer users.
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Vision on the future of software development
Mainframe Computers
Software systems developed and used by computer
experts
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Vision on the use of Disciple in Education
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Recommended reading
G. Tecuci, Building Intelligent Agents, Academic
Press, 1998, pp. 13-33. Tecuci G., Boicu M.,
Boicu C., Marcu D., Stanescu B., Barbulescu M.,
The Disciple-RKF Learning and Reasoning Agent,
submitted to publication, September 2004. Boicu
M., Tecuci G., Stanescu B., Marcu D., Barbulescu
M., Boicu C., "Design Principles for Learning
Agents," in Proceedings of AAAI-2004 Workshop on
Intelligent Agent Architectures Combining the
Strengths of Software Engineering and Cognitive
Systems, July 26, San Jose, AAAI Press, Menlo
Park, CA, 2004. http//lac.gmu.edu/publications/d
ata/2004/2004_Disciple-architecture.pdf