Knowledge Acquisition and Problem Solving

2004, G.Tecuci, Learning Agents Center

CS 785 Fall 2004

Learning Agents Center and Computer Science Department

George Mason University

Gheorghe Tecuci [email protected]

http://lac.gmu.edu/


OverviewOverview

A Disciple agent for center of gravity analysis

Limits of the classical knowledge engineering approaches

Advanced approaches to agent development

Recommended readings

Research problems and research visions

Demo: Training a Disciple agent

Design principles for instructable agents

Demo: Use of a Disciple agent as a decision-making assistant

Learning agent shells


How are agents builtHow 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.

KnowledgeEngineer

DomainExpert

Knowledge Base

Inference Engine

Intelligent Agent

ProgrammingDialog

Results


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

Limiting factors in developing intelligent agentsLimiting factors in developing intelligent agents


OverviewOverview











Advanced approaches to KB and agent developmentAdvanced approaches to KB and agent development

Limited ability to reuse previously developed knowledge

Problem:

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

Solution:

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.


The knowledge acquisition bottleneck

Problem:

Automation of knowledge acquisition through machine learning

Solution:

Example:

A subject matter expert teaching an agent through examples and explanations,

similarly to how the expert would teach an apprentice.



The knowledge maintenance bottleneck

Problem:

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.

Solution:

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.



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

Problem:

Customizable learning agent shell.It is applicable to a wide variety of application domains.

Requires limited customization.

Solution:

Example:

Disciple learning agent shell



OverviewOverview











Expert system shellExpert system shell

Problem SolvingEngine

Expert System Shell

EmptyKnowledge Base

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.

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.

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: definitionLearning 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.

Inte

rfac

e ProblemSolving

Learning

Ontology+ Rules


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.

Mixed-initiative reasoning

between the expert that has the knowledge

to be formalized and the agent

that knows how to formalize it.

Disciple learning agent shellDisciple learning agent shell

The expert teachesthe agent to perform various tasks in a way that resembles

how the expert would teach a person.

The agent learnsfrom the expert,

building, verifyingand improving itsknowledge base

Inte

rfac

e

ProblemSolving

Learning

Ontology+ Rules


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.

Definedomainmodel

Createontology

Definerules

Verify and update rules

KE

SME

Traditionally

KE

Agent

SME Agent

SME

Specifyinstances

Learnontologicalelements

Import andcreate initial

ontology

Agent

Learnrules

SME Agent

Define andexplain

examples

SME

AgentSME Agent

Critiqueexamples

Refinerules

Explaincritiques

SME Agent

Extenddomainmodel

SMEKE

Defineinitialmodel

With Disciple

Main idea of the Disciple mixed-initiative approachMain idea of the Disciple mixed-initiative approach


Disciple-WA (1997-1998): Estimates the best planof working around damage to a transportation

infrastructure, such as a damaged bridge or road.River bedSite 106

Left bankSite 105Right bank

Site 107

Site 103:cross-sectionDamage 200: destroyed bridge

Bridge/Rivergap = 25 meters

Near approach(Right approach)Site 108

Far approach(Left approach)Site 104

River bedSite 106


Site 107





River bedSite 106


Site 107





A Disciple agent for action planning

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.

Evolution of KB coverage and performance from the pre-repair phase to the post-repair phase.

Coverage

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Ontology Tasks Rules Knowledge Base

Rule Axioms

Concept Axioms

Task Axioms

Total Axioms

72% increase of KB size

Development of Disciple’s KB during evaluation.

72% increase of KB sizein 17 days

• High knowledge acquisition rate;

• High problem solving performance (including unanticipated solutions).

• Demonstrated at EFX’98 as part of an integrated application led by Alphatech.

Disciple-WA features:


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.

A Disciple agent for course of action critiquingMission: BLUE-BRIGADE2 attacks to penetrate RED-MECH-REGIMENT2 at 130600 Aug in order to enable the completion of seize

OBJ-SLAM by BLUE-ARMOR-BRIGADE1.

Close: BLUE-TASK-FORCE1, a balanced task force (MAIN EFFORT) attacks to penetrate RED-MECH-COMPANY4, then clears RED-TANK-COMPANY2 in order to enable the completion of seize OBJ-SLAM by BLUE-ARMOR-BRIGADE1. BLUE-TASK-FORCE2, a balanced task force (SUPPORTING EFFORT 1) attacks to fix RED-MECH-COMPANY1 and RED-

MECH-COMPANY2 and RED-MECH-COMPANY3 in order to prevent RED-MECH-COMPANY1 and RED-MECH-COMPANY2 and RED-MECH-COMPANY3 from interfering with conducts of the MAIN-EFFORT1, then clears RED-MECH-COMPANY1 and RED-MECH-COMPANY2 and RED-MECH-COMPANY3 and RED-TANK-COMPANY1. …

Reserve: The reserve, BLUE-MECH-COMPANY8, a mechanized infantry company, follows Main Effort, and is prepared to reinforce ) MAIN-EFFORT1.

Security: SUPPORTING-EFFORT1 destroys RED-CSOP1 prior to begin moving across PL-AMBER by MAIN-EFFORT1 in order to prevent RED-MECH-REGIMENT2 from observing MAIN-EFFORT1. …

Deep: Deep operations will destroy RED-TANK-COMPANY1 and RED-TANK-COMPANY2 and RED-TANK-COMPANY3.

Rear: BLUE-MECH-PLT1, a mechanized infantry platoon secures the brigade support area.

Fires: Fires will suppress RED-MECH-COMPANY1 and RED-MECH-COMPANY2 and RED-MECH-COMPANY3 and RED-MECH-COMPANY4 and RED-MECH-COMPANY5 and RED-MECH-COMPANY6.

End State: At the conclusion of this operation, BLUE-BRIGADE2 will enable accomplishing conducts forward passage of lines through BLUE-BRIGADE2 by BLUE-ARMOR-BRIGADE1. MAIN-EFFORT1 will complete to clear RED-MECH-COMPANY4 and RED-TANK-COMPANY2. SUPPORTING-EFFORT1 will complete to clear RED-MECH-COMPANY1 and RED-MECH-COMPANY2 and RED-MECH-COMPANY3 and RED-TANK-COMPANY1. SUPPORGING-EFFORT2 will complete to clear RED-MECH-COMPANY5 and RED-MECH-COMPANY6 and RED-TANK-COMPANY3.

Coverage

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(Evaluation Items 3, 4, and 5)

GMU

ISITFS/CyCorp

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Ontology Tasks Rules Knowledge Base

Rule Axioms

Concept Axioms

Task Axioms

Total Axioms 46% increase of KB size

Development of Disciple’s KB during evaluation.

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 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.

• High knowledge acquisition rate;

• Better performance than the other evaluated systems;

• Better performance than the evaluating experts (many unanticipated solutions).

Disciple-COA features:

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Tasks Rules Tasks+Rules

Task+Rule axioms

Rule axioms

Task axioms

KB extended with 26 rules and 28 tasks in 3 hours

KB development during experimentation.

KB extended with 26 rules and 28 tasks

in 3 hours

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.

IKB

Disciple COA

QuestionnaireAll comments consider the fact that Disciple is a research prototype.Degree of agreement with a statement: 1 (not at all) to 5 (very).

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

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

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

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

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

Questions Answers• 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.

• Rating 5. Yes, Absolutely! I’ll take 10 of them!• 5• 5• Not at this point of development.

• Rating 5. As a minimum yes, as a maximum—better!• This again was done very well.• 4• 4

• 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

• Rating 5. Yes.• 5 (absolutely)• 4• Yes. As it develops and becomes tailored to the user, it will simplify the tedious tasks.

LTC John N. DuquetteLTC Jay E. FarwellMAJ Michael P. BowmanMAJ Dwayne E. Ptaschek

Knowledge acquisition experiment at BCBL, Ft. Leavenworth


OverviewOverview











KBs Integration

Integrated KB

KB1

Disciple-RKF Assistant


Problem solver for a non-expert

Tutorto a student

Assistant of an expert

KBn


...Expert

Expert

Parallel Agent Training and KB Development

Agent Use

Three agent training and knowledge bases development

experiments (2001, 2002, 2003).

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).

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.

The mediator team integrates the knowledge bases developed by each subject matter expert and personal Disciple-RKF agent.

Disciple-RKF with the integrated KB is used in practical applications.

Successful experiments and transition to the US Army War College

Goal: Develop the technology that enables teams of subject matter experts to build integrated knowledge bases and agents incorporating their problem solving expertise.

Disciple-RKF: An agent for center of gravity analysis


Knowledge bases and agent development by subject matter experts, using learning agent technology. Experiments in the USAWC courses.

Formalization ofthe Center of Gravity(COG) analysis process

319jw Case Studies inCenter of Gravity Analysis

Use of Disciple in a sequence of two joint warfighting courses

589jw Military Applications of Artificial Intelligence

Students developedscenarios

Students developed

agents

Extended KB

stay informedbe irreplaceable

communicate be influential

Integrated KB

Initial KB

have supportbe protected

be driving force

432 concepts and features, 29 tasks, 18 rulesFor COG identification for leaders

37 acquired concepts andfeatures for COG testing

COG identification and testing (leaders)

Domain analysis and ontology development (KE+SME)

Parallel KB development (SME assisted by KE)

KB merging (KE)

Knowledge Engineer (KE)

All subject matter experts (SME)

DISCIPLE-COG DISCIPLE-COG DISCIPLE-COG DISCIPLE-COG DISCIPLE-COG

Training scenarios:Iraq 2003

Arab-Israeli 1973War on Terror 2003

Team 1 Team 2 Team 3 Team 4 Team 5

5 features10 tasks10 rules

Learned features, tasks, rules

14 tasks14 rules


35 tasks33 rules


Unified two featuresDeleted 4 incomplete rulesRefined 11 rules+9 features 478 concepts and features+105 tasks 134 tasks+95 rules 113 rules

DISCIPLE-COG

Testing scenario:North Korea 2003Correctness = 98.15%

Completeness = 89.33%

2.5 examples/rule5.47 hours average training time

Identify the strategic COG candidates for the Sicily_1943 scenario

Anglo_allies_1943

Identify the strategic COG candidates for Anglo_allies_1943

Which is an opposing force in the Sicily_1943 scenario?

Is Anglo_allies_1943 a single member force or a multi-member force?

Anglo_allies_1943 is a multi-member force

Identify the strategic COG candidates for the Anglo_allies_1943 which is a multi-member force

What type of strategic COG candidates should I consider for a multi-member force?

Identify the strategic COG candidates corresponding to the multi-member nature of the Anglo_allies_1943

I consider the candidates corresponding to the multi-member nature of the force

What type of strategic COG candidates should I consider for the multi-member nature of the force?

I consider the relationships between the members of the force

I consider the type of operations being conducted by the members of the force

Identify the strategic COG candidates with respect to the type of operations being conducted by the members of the Anglo_allies_1943

Which is the primary force element that will conduct the campaign for Anglo_allies_1943?

Allied_forces_operations_Husky

Is Allied_forces_operations_Husky made up of a true single group or are there subgroups?

Allied_forces_operations_Husky is made up of several subgroups

Identify the strategic COG candidates with respect to the type of operations being conducted by Allied_forces_operations_Husky

Synergistic collaboration and transition at the USAWCGeorge Mason University - US Army War College

ArtificialIntelligenceResearch

Mili

tary

Stra

tegy

Rese

arch

Military

Education

& PracticeDisciple


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 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.

Sample Domain: Center of Gravity Analysis

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.


Government

Military

People

Economy

Alliances

Etc.

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?

• 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 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:

Identify COG candidates Test COG candidates

First computational approach to COG analysis


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

Studies the logic behind COG identification and testing.

Critiques Disciple’s analysis and finalizes the analysis report.

Develops a formal representation of the scenario.

Identifies and tests strategic COG candidates.

Generates a COG analysis report.

DiscipleStudent

Student – Disciple collaborationStudent – Disciple collaboration


The student is guided by Disciple to describe the relevant aspects of a strategic environment.


Disciple identifies and tests COG candidates

The students study the logic behind COG identification and testing


Disciple generates a COG analysis report


War on Terror 2003

LTC Thomas T. SmithLTC Joseph P. Schweitzer

LTC Michael S. YarmieCDR John J. Welsh

Iraq 2003

Israel-PLO 2003

COL Christian E. de Graff LTC Robert D. Grymes

North Korea 2003

COL Douglas J. LeeCOL Robert F. Barry

North Korea: military of North Korea

US Led Coalition: will of the people of United States

Al Qaeda 2003:Terrorist Cells of Al QaedaMuslim non-state actors neutral to Al Qaeda

US Coalition 2003: will of the people of USMuslim non-state actors neutral to Al Qaeda

Iraq:Saddam Hussein

US led coalition:will of the people of United Stateswill of the people of Great Britain

Israel: financial capacity of Israel

Palestine: external support from Arab Countries to Palestine Liberation Organization

Spring 2003 scenarios and COGs selected


Demonstration

Disciple

Strategic leader’s assistant


OverviewOverview










Structure the architecture into a reusable domain-independent learning agent shell and domain specific modules

Generality-Power Tradeoff

Disciple Agent

Domain IndependentModules

Domain DependentPlug-in Modules

Learning Agent Shell

Graphical User Interface

Customized User Interface

Customized Problem Solver

Problem Solver

Knowledge Acquisition and Learning

Knowledge Base Manager

Knowledge Repository

Cognitive Functions

Implement each cognitive module as a collaborative agent, in a mixed-initiative framework

Make separate modules for each cognitive function, such as communication, problem solving, learning, and knowledge management

Disciple Agent

Domain IndependentModules

Domain DependentPlug-in Modules

Learning Agent Shell

Graphical User Interface

Customized User Interface

Customized Problem Solver

ProblemSolver

Knowledge Acquisition

and Learning

Knowledge Base Manager

Knowledge Repository

Disciple: each module is

implemented as a set of collaborative

agents

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

S11 S1n

S111 S11mT11mT111

T1nT11

T1

…

…


Question-answering based task reductionQuestion-answering based task reduction

S1

S11a

S1n

S11b1 S11bm

T11bmT11b1

T1nT11a

…

…

T1

Q1

S11bT11b

A1n S11A11

……

A11b1 A11bm

S11bQ11b

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.

Rule_3

Rule_2

Identify and test a strategic COG candidate for Allied_Forces_1943 which is a multi_member_force

What type of strategic COG candidate should I consider for this multi_member_force?

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

I consider a candidate corresponding to a member of the multi_member_force

Therefore we need to

US_1943

Identify and test a strategic COG candidate for US_1943

Which is a member of Allied_Forces_1943?


Identify and test a strategic COG candidate for Sicily_1943

We need to

Which is an opposing_force in the Sicily_1943 scenario?

Allied_Forces_1943

Identify and test a strategic COG candidate for Allied_Forces_1943


Is Allied_Forces_1943 a single_member_force or a multi_member_force?

Allied_Forces_1943 is a multi_member_force


Task Reduction Example: COG Analysis

Rule_1

Rule_4

Rules

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

IF the task isIdentify the strategic COG candidates with respect to theindustrial civilization of a state which is a member of a force

The state is ?O2The force is ?O1

THENConclude that an economic factor is a strategic COGcandidate for a state which is a member of a force

The state is ?O2The force is ?O1The economic factor is ?O3

Plausible Upper Bound Condition?O1 IS Force?O2 IS Force

has_as_industrial_factor ?O3?O3 IS Industrial_factor

is_a_major_generator_of ?O4?O4 IS Strategically_essential_goods_o_materiel

Plausible Lower Bound Condition?O1 IS Anglo_allies_1943?O2 IS US_1943

has_as_industrial_factor ?O3?O3 IS Industrial_capacity_of_US_1943

is_a_major_generator_of ?O4?O4 IS War_materiel_and_transports_of_US_1943

explanation?O2 has_as_industrial_factor ?O3?O3 is_a_major_generator_of ?O4?O4 IS strategically_essential_goods_or_materiel


















































































































































































































OntologyForce

<object>

Scenario

Strategic_goal

Force_goal

Strategic_COG_relevant_factor

Demographic_factor

International_factor

Psychosocial_factorEconomic_factor

Geographical_factor

Historical_factor

Military_factor

Political_factor

Other_relevant_factor

Operational_goal

Civilization_factor

resource_ or_ infrastructure_element

Disciple: Ontology Fragment

A hierarchical representation of the objects and types of

objects.

A hierarchical representation of the types of

features.

has_as_controlling_leaderDomain: agentRange: person

has_as_religious_leaderDomain: governing_bodyRange: person

has_as_military_leaderDomain: governing_bodyRange: person

has_as_god_kingDomain: governing_bodyRange: person

has_as_monarchDomain: governing_bodyRange: person

has_as_political_leaderDomain: governing_bodyRange: person

has_as_head_of_stateDomain: governing_bodyRange: person

has_as_head_of_governmentDomain: governing_bodyRange: person

has_as_commander_in_chiefDomain: forceRange: person

<object>

Scenario Strategic_COG_relevant_factor

Established_governing_body

Resource_or_ infrastructure_element

Psychosocial_factor

Geographic_factor

Historic_factor

Military_factor

Political_factor Other_relevant_factor

Civilization_factor

War_scenario

Ad_hoc_governing_body

Other_type_of_governing_body

Demographic_factor

Agent Force_goal

International_factor

Other_political_factor

Controlling_element

Group_governing_body

Governing_body

State_government

Economic_factor

Operation_other_than_war

Disciple: Example of a Task Reduction Rule

Question Which is a member of ?O1 ?Answer ?O2

INFORMAL STRUCTURE

IFIdentify and test a strategic COG candidate corresponding to a member of the ?O1

THENIdentify and test a strategic COG candidate for ?O2

US_1943


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


EXAMPLE OF REASONING STEP

FORMAL STRUCTURE

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

The force is ?O1

THENIdentify and test a strategic COG candidate for a force

The force is ?O2

Plausible Upper Bound Condition ?O1 is multi_member_force

has_as_member ?O2 ?O2 is force

Plausible Lower Bound Condition ?O1 is equal_partners_multi_state_alliance

has_as_member ?O2 ?O2 is single_state_force


LEARNED RULE

Partially Learned Knowledge

Universe of Instances

Concept

Plausible Upper Bound

Plausible Lower Bound

Plausible version space (PVS)

Allow the representation, use, and refinement of partially learned knowledge

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

Integrated Problem Solving and Learning

Input Task

Generated Reduction

Mixed-Initiative Problem Solving

Ontology + Rules

Reject ReductionAccept ReductionNew Reduction

Rule Refinement

Task RefinementRule Refinement

Modeling

Learning

Solution

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

US_1943



We need to



Provides an example

1

Rule_4Learns

2

Rule_4

?

Applies

Germany_1943

Identify and test a strategic COG candidate for Germany_1943

Which is a member of European_Axis_1943?


3

Accepts the example

4 Rule_4Refines

5We need to

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

…

Modeling Learning

Problem Solving Refining

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)

Example of atask reduction

step

Plausible version space rule

analogy

PLB

PUB

Knowledge Base

Incompletejustification

Analogy and HintGuided Explanation

Analogy-basedGeneralization


Find an explanation of why the example is correct

US_1943has_as_member

Allied_Forces_1943

The explanation is an approximation of the question and the answer, in the object ontology.

US_1943



We need to




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

The force is Allied_Forces_1943

Identify and test a strategic COG candidate for a force

The force is US_1943

We need to


Generate the PVS rule

Condition?O1 is Allied_Forces_1943

has_as_member ?O2?O2 is US_1943

Most general generalization

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

The force is ?O1

THENIdentify and test a strategic COG candidate for a force

The force is ?O2

Plausible Upper Bound Condition ?O1 is multi_member_force

has_as_member ?O2 ?O2 is force

Plausible Lower Bound Condition ?O1 is equal_partners_multi_state_alliance

has_as_member ?O2 ?O2 is single_state_force

explanation?O1 has_as_member ?O2

Most specific generalization

US_1943has_as_member

Allied_Forces_1943

Rewriteas

has_as_member domain: multi_member_force range: force

Multistrategy Learning

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

Failureexplanation

PVSRule

Example of task reductionsgenerated by the agent

Incorrectexample

Correctexample

Learning fromExplanations

Learning by AnalogyAnd Experimentation

Learning from Examples

Knowledge BaseIF<task>

THEN<subtask 1>…<subtask m>

Condition<condition 1>

Except when condition<condition 2>…Except when condition<condition n>

Disciple End to End Agent Development Methodology

DiscipleRKF

INITIAL MODELING ANDONTOLOGY DEVELOPMENT

KNOWLEDGE BASEINTEGRATION,

EXPORT, AND USE

KNOWLEDGE ELICITATION AND MODELING

LEARNING AND PROBLEM SOLVING

Initial Modeling andOntology Specification

Ontology Import

Ontology Development

Task Learning

Modeling the ProblemSolving Process

Scenario Elicitation

Rule Learning

Rule Refining

Problem Solving

Knowledge BasesIntegration

Knowledge Base Export

Use of Disciple Agent

Ontology Learning

KNOWLEDGEBASE


Demonstration

DiscipleDemo

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


OverviewOverview











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

Present research problemPresent research problem


1. Automating the domain modeling process that consists of making explicit, at an informal level, the way the expert solves problems.

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.

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.

What are the main technical challenges What are the main technical challenges


1. 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.

2. 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.

3. Develop methods to import/reuse ontological knowledge from previously developed knowledge bases or repositories.

4. Develop a learnable knowledge representation that can express partially learned knowledge and can be used in reasoning.

5. Develop multistrategy learning methods that synergistically integrate several learning strategies.

6. Develop methods for integrated teaching and learning where the SME helps the agent to learn, and the agent helps the SME to teach it.

7. Use of plausible reasoning to hypothesize solutions based on incomplete and partially incorrect knowledge.

How are these challenges addressedHow are these challenges addressed


LearningAgent

Modeling

Non-disruptiveLearning

Ontology Elicitation

Rule & Ontology Learning

Rule & OntologyRefining

User ModelLearning

ExceptionHandling

KBMaintenance

Implicit reasoning ofhuman expert Explicit reasoning in

natural language Ontology extensions

Le

arn

ed

rule

s,

on

tolo

gy

Re

fine

d ru

les

, o

nto

log

y

User modelCases, rules

Ru

les

w/o

e

xc

ep

tio

ns

1. Multistrategy teaching and learning 2. Mixed

-initiative p

rob

lem so

lving

and

learnin

g

3. Autonomous (and interactive) multistrategy learning

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

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

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

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

Research goal: Life-long continuous agent learning4.

KB

mai

nte

nan

ce a

nd

op

tim

izat

ion


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.

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.

Long term research vision Long term research vision


Vision on the future of software development

MainframeComputers

Software systems developed and used by computer experts

PersonalComputers

Software systems developedby computer experts

and used by persons thatare not computer experts

LearningAgents

Software systems developed and used by persons that are

not computer experts

DISCIPLE

Inte

rfac

e

ProblemSolving

Learning

Ontology+ Rules


Vision on the use of Disciple in Education

teachesDiscipleAgent KB

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



…

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



Recommended readingRecommended 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/data/2004/2004_Disciple-architecture.pdf

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Knowledge Acquisition and Problem Solving

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