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CS 785 Fall 2004. Knowledge Acquisition and Problem Solving. Knowledge engineering: Advanced approaches. Gheorghe Tecuci tecuci@gmu.edu http://lac.gmu.edu/. Learning Agents Center and Computer Science Department George Mason University. Overview.

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CS 785 Fall 2004

Knowledge Acquisition and Problem Solving

Knowledge engineering: Advanced approaches

Gheorghe Tecuci tecuci@gmu.eduhttp://lac.gmu.edu/

Learning Agents Center and Computer Science Department

George Mason University


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


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.


Limiting factors in developing intelligent agents

Limited ability to reuse previously developed knowledge

The knowledge acquisition bottleneck

The knowledge maintenance bottleneck

The scalability of the agent building process

Finding the right balance between using general tools and developing domain specific modules

Portability of the tools and of the developed agents


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


Advanced approaches to KB and agent development

Problem:

Limited ability to reuse previously developed knowledge

Solution:

Ontology reuse (import, merge, export, OKBC protocol, CYC)

Example:

Ontologies of military units and equipment developed for a particular military planning agent could be reused by a course of action critiquing agent or other military agent.


Advanced approaches to KB and agent development

Problem:

The knowledge acquisition bottleneck

Solution:

Automation of knowledge acquisition through machine learning

Example:

A subject matter expert teaching an agent

through examples and explanations,

similarly to how the expert would teach an apprentice.


Advanced approaches to KB and agent development

Problem:

The knowledge maintenance bottleneck

Solution:

Use of machine learning methods by the agent, to continuouslyupdate its knowledge base in response to changes in the application domain or in the requirements of the system.

Example:

A subject matter expert providing feedback to the agentand guiding it to update its knowledge base.

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.


Advanced approaches to KB and agent development

Problem:

Finding the right balance between using general tools and developing domain specific modules

Solution:

Customizable learning agent shell.

It is applicable to a wide variety of application domains.

Requires limited customization.

Example:

Disciple learning agent shell


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


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.


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


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


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

Define

initial

model

Import and

create initial

ontology

Define and

explain

examples

Critique

examples

SME

SME

KE

SME

Agent

Agent

SME

KE

Extend

domain

model

Specify

instances

Learn

ontological

elements

Learn

rules

Explain

critiques

Refine

rules

SME

Agent

SME

Agent

Agent

SME

Agent

Agent


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 basecapturing 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 EFX’98 as part of an integrated application led by Alphatech.

Development of Disciple’s KB during evaluation.


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.

100%

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 Disciple’s KB during evaluation.


Knowledge acquisition experiment at BCBL, Ft. Leavenworth

Questions

Answers

Do you think that Disciple is a useful tool for Knowledge Acquisition?

  • Rating 5. Absolutely! The potential use of this tool by domain experts is only limited by their imagination - not their AI programming skills.

  • 5

  • 4

  • Yes, it allowed me to be consistent with logical thought.

Do you think that Disciple is a useful tool for Problem Solving?

  • Rating 5. Yes.

  • 5 (absolutely)

  • 4

  • Yes. As it develops and becomes tailored to the user, it will simplify the tedious tasks.

Do you think that Disciple has potential to be used in other tasks you do?

  • Rating 5. Again the use of the tool is only limited to one’s imagination but potential applications include knowledge bases built to support distance/individual learning, a multitude of decision support tools (not just COA Analysis), and autonomous and semi-autonomous decision makers - all these designed by the domain expert vs an AI programmer.

  • Absolutely. It can be used to critique any of the BOS's for any mission.

  • 5 Yes

  • 4

Were the procedures/ processes used in Disciple compatible with Army doctrine and/or decision making processes?

  • Rating 5. As a minimum yes, as a maximum—better!

  • This again was done very well.

  • 4

  • 4

Could Disciple be used to support operational requirements in your organization?

  • Rating 5. Yes, Absolutely! I’ll take 10 of them!

  • 5

  • 5

  • Not at this point of development.

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.


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


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

Agent Use

KBs Integration

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).

Disciple agents regularly used in two courses at US Army War College (2001-2004).

Three agent training and knowledge bases development experiments (2001, 2002, 2003).


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.


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 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. Giles and Galvin, USAWC 1996.


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.


Student – Disciple collaboration

Is guided by Disciple to describe the relevant aspects of a strategic environment.

Develops a formal representation of the scenario.

Identifies and tests strategic COG candidates.

Studies the logic behind COG identification and testing.

Generates a COG analysis report.

Critiques Disciple’s analysis and finalizes the analysis report.

Student

Disciple



Disciple identifies and tests COG candidates aspects of a strategic environment.

The students study the logic behind COG identification and testing


Disciple generates a COG analysis report aspects of a strategic environment.


Spring 2003 scenarios and COGs selected aspects of a strategic environment.

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


Demonstration aspects of a strategic environment.

Strategic leader’s assistant

Disciple


Overview aspects of a strategic environment.

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


Generality power tradeoff
Generality-Power Tradeoff aspects of a strategic environment.

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

Problem

Solver

Customized

Problem Solver

Knowledge

Base Manager

Domain Independent

Modules

Domain Dependent

Plug-in Modules


Cognitive functions
Cognitive Functions aspects of a strategic environment.

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


Problem solving paradigm
Problem Solving Paradigm aspects of a strategic environment.

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.

T1

S1

T11

S11

T1n

S1n

S111

T111

T11m

S11m


Question-answering based task reduction aspects of a strategic environment.

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

T11a

S11a

T11b

S11b

Q11b

S11b

S11bm

S11b1

A11bm

A11b1

T11b1

T11bm


Task reduction example cog analysis
Task Reduction Example: COG Analysis aspects of a strategic environment.

Rule_1

Rule_2

Rule_3

Rule_4


Knowledge base structuring
Knowledge Base Structuring aspects of a strategic environment.

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


Disciple ontology fragment
Disciple: Ontology Fragment aspects of a strategic environment.

A hierarchical representation of the objects and types of objects.

A hierarchical representation of the types of features.


Disciple example of a task reduction rule
Disciple: Example of a Task Reduction Rule aspects of a strategic environment.

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

QuestionWhich 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


Partially learned knowledge
Partially Learned Knowledge aspects of a strategic environment.

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


Integrated problem solving and learning
Integrated Problem Solving and Learning aspects of a strategic environment.

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


Disciple problem solving and learning
Disciple: Problem-Solving and Learning aspects of a strategic environment.

We need to

2

1

Identify and test a strategic COG candidate corresponding to a member of the Allied_Forces_1943

Which is a member of Allied_Forces_1943?

Learns

Provides an example

Rule_4

US_1943

Therefore we need to

Identify and test a strategic COG candidate for US_1943

We need to

3

5

Identify and test a strategic COG candidate corresponding to a member of the European_Axis_1943

Applies

Rule_4

Which is a member of European_Axis_1943?

Refines

?

Rule_4

4

Germany_1943

Therefore we need to

Identify and test a strategic COG candidate for Germany_1943

Accepts the example

Learning

Modeling

Problem Solving

Refining


Integrated teaching and learning
Integrated Teaching and Learning aspects of a strategic environment.

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)


Find an explanation of why the example is correct aspects of a strategic environment.

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

Allied_Forces_1943

US_1943


Generate the PVS rule aspects of a strategic environment.

We need to

Identify and test a strategic COG candidate corresponding to a member of a force

The force is Allied_Forces_1943

has_as_member

Allied_Forces_1943

US_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_1943has_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


Multistrategy learning
Multistrategy Learning aspects of a strategic environment.

Integrate several learning strategies, taking advantage of their complementary strengths to compensate for each other’s weaknesses


Disciple end to end agent development methodology
Disciple End to End Agent Development Methodology aspects of a strategic environment.


Demonstration aspects of a strategic environment.

Teaching Disciple how to determine whether a strategic leader has the critical capability to be protected.

DiscipleDemo


Overview aspects of a strategic environment.

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


Present research problem aspects of a strategic environment.

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


What are the main technical challenges aspects of a strategic environment.

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 froma 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.


How are these challenges addressed aspects of a strategic environment.

  • 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.


Research goal: Life-long continuous agent learning aspects of a strategic environment.

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

KB

Maintenance

Rule & Ontology

Refining

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

Cases, rules

User model

3. Autonomous (and interactive) multistrategy learning


Long term research vision aspects of a strategic environment.

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.


Vision on the future of software development aspects of a strategic environment.

Personal

Computers

Learning

Agents

Software systems developed and used by persons that are not computer experts

Software systems developed

by computer experts

and used by persons that

are not computer experts

Mainframe

Computers

Software systems developed and used by computer experts


Vision on the use of Disciple in Education aspects of a strategic environment.

teaches

teaches

teaches

teaches

The expert/teacher teaches Disciple through examples and explanations, in a way that is similar to how the expert would teach a student.

DiscipleAgent

DiscipleAgent

DiscipleAgent

DiscipleAgent

KB

KB

KB

KB

Disciple tutors the student in a way that is similar to how the expert/teacher has taught it.


Recommended reading aspects of a strategic environment.

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/data/2004/2004_Disciple-architecture.pdf


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