Knowledge Management: A CBR Perspective

Sources:

• David W. Aha• My own• Thomas H. Davenport, Laurence Prusak, 1998

The Beginning: The Apollo 13 Situationhttp://www.youtube.com/watch?v=nEl0NsYn1fU

• The oxygen tanks had originally been designed to run off the 28 volt DC

• The tanks were redesigned to also run off the 65 volt DC

The Changing Game

The New EconomicsManufacturing ServiceTangible IntangibleConsumable InconsumableStructural Intellectual

Tobin’s Q ratio company’s stock market value / value of its physical assets

Is increasing dramatically. What does this mean?

Increasing importance of intellectual capital in the United States (Barr & Magaldi, 1996)

Knowledge Management (KM)

An increasingly important new business movement that promotes the creation, sharing, & leveraging of knowledge

within an organization to maximize business results.

Effective tools to capture, leverage & reuse knowledge

Technology

Develop a culturefor knowledge sharing

Organizational DynamicsNeeds

Financial constraintsLoss of organizational knowledge

Needs

Problems:

Knowledge Management: Issues

• Technical and Business Expertise:ProficienciesKnow-HowSkills

• Work Practice Execution:ProcessesMethodologiesPracticesLessons learned

Why Knowledge Management?

• Leverages Core Business Competence

• Accelerates Innovation (Time to Market)

• Improves Cycle Times (Market to Collection)

• Improves Decision Making

• Strengthens Organizational Commitment

• Builds sustainable differentiation

CBR: The Knowledge Management Plunge

“Case-based reasoning programs have been shown to bring about marked improvements in customer service.”

- Thomas H. Davenport, Laurence Prusak, 1998 - Working Knowledge: How Organizations Manage What They Know

KM

CBRWorks

eGain eService Enterprise (E3)

KM Project Domains: CBR Applicable? (KM World, 1/99, Dan Holtshouse, Xerox)

1. Sharing knowledge and best practices2. Instilling responsibility for knowledge sharing3. Capturing and reusing past experiences4. Embedding knowledge (products/services/processes)5. Producing knowledge as a product 6. Driving knowledge generation for innovation7. Mapping networks of experts8. Building/mining customer knowledge bases9. Understanding/mining customer knowledge bases10. Leveraging intellectual assets.

KM Domains/Tasks CBR Applicable?YesNoYesYes Yes No

YesNoYes

No

Recent Events Related to KM/CBR

1999 Summer Workshops:– AAAI: Exploring the Synergies between KM and CBR (Co-chair)– ICCBR: Practical CBR Strategies for Building/Maintaining Corporate Memories– ICCBR: Integration of CBR in Business Processes– IJCAI: Automating the Construction of CBRs

Special issues:– Human-Computer Studies (1999)– Knowledge-based Systems (2000)

AAAI 2000 Spring Symposium:– Bringing Knowledge to Business Processes

2003 German CBR Workshops is now German KM Workshop

1999 AAAI KM/CBR Workshop~45 attendees: Siemens, Schlumberger, Motorola, NEC, British

Airways, General Motors, Boeing, Ford Motor Company, World Bank

Goals:1. Explain KM issues to CBR researchers2. Report on recent CBR approaches for KM tasks3. Share cautions, knowledge, & experiences

Some observations:1. Embedded/integrated in knowledge processes2. Benefits of semi-structured case representations3. Interactive (“conversational”) systems

Limitations of CBR for KM(from the 1999 AAAI KM/CBR Workshop)

1. Main limitation is time and effort? (Wess/Haley)

2. Limitations from working with simple representations (Haley)– Becoming less problematic (e.g., with development of textual CBR)

3. Rule-based integrations– Suffer from old problems of rule acquisition– But KM problem-solving techniques are combating this (Studer)

4. More intuitive case authoring capabilities

5. Tools for working with heterogeneous data sources

Panel: Lessons & Suggested Directions

CBR Roles:– Accumulate, extend, preserve, distribute, reuse corporate knowledge– Extracting tacit knowledge– Customer relationship management

Lessons & Observations:– Integrate CBR with KM tasks & task models– Integrate case retrieval with presentation with tools/workplaces– Integrate case construction/indexing with work product development– Need more advanced (automated) case authoring tools– Must consider effects on user groups, time, organizational impact– CBR not a complete KM solution

Experience Management vs CBR

Experience Management

CBR

(Organization)

(IDSS)2. Reuse3. Revise

4. Retain

Case Library

1. RetrieveBackground Knowledge

Experience base

Reuse-related

knowledge

Problem acquisition

Experience evaluation and retrieval

Experience adaptation

Experience presentation

Complex problem solving

Developm

ent and M

anagement

Methodologies

BO

OK

Relating KM with AI

AI

Knowledge-BasedSystems

HumanFactors KM Business

Processing

CCBR

EXTERNAL MONITORING

AlertsSpiders

Workflow

Scheduling

CollaborationSuspenses

Records Management

Document Management

E-mail

OA tools

Library catalog

Online databases

E-journals

How-to guides

Document Delivery Service

Bulletin boards

Buckets

Profiles

MIS

INFORMATION SOURCES

WORKSPACE

PERSONAL PORTAL

AFRL Proposed KM Environment

(multi?) impersonal

Personalization

Semantic Web Ontologies

DS1

DS2

DS3

Distributeddata sources

AssistantAgent

Case Repository

Causal ModelCurrent Problem

User Ontologies

Personal Portal/Workspace

InformationSources

NEWS

BULLETINBOARDS

SUSPENSES/TASKS RESEARCH ASSISTANT

CALENDAR/SCHEDULING

4 5 6 81 2 3 7

WORKSPACE

E-Mail

WHO’SWHO

GUIDES

FAVORITEWEB SITES

Microsoft Word.lnk Micro sof t Pow erPo int.ln k Micros oft Excel.lnk

Individualized Portal

Information Domains

Data Systems

Virtual Library

BucketsFinance

Personnel

A B C D

Executive Information System

Out-of-Family Disposition (OOFD) Process

NASA-Kennedy Space Center: Shuttle Processing Directorate

KM expertise

CBR expertise

Topic: Performing project tasks outside range of expertise• Lack of task familiarity

Motivations: Downsizing, employee loss, technology paceResources: Interim problem reports

• Standardized text documents for reporting problems/solutions• Given: 12 of these reports

Pre-flight, launch, landing, recovery

Prof. I. Becerra-Fernandez

Another example: legal constraints

OOFD: Problem Categorization (Ontology)

Prompt Problem

Computer

Electrical

Materials

Mechanical

Data Drop Out

FCMS GMT Discrepancy

PCM 3 Shows up in PCM 2

Micro-switch Malfunctioning

Unexplained Power Drops

Helium ISO Valves

Backup HGDS

Seal Port Dynatube

Catch Bottle Relief Valve

Stress Corrosion Cracking

Debris Detected in Stiffener ring

Cracked A8U Panel

2 2.1 2.2 2.3 2.4 2.5

12

4

7

11 11.1 11.2 11.3 11.4 11.5 11.6

1

3

5

6

10

8

9

1.1 1.2

10.1 10.2 10.3

8.1 8.2 8.3

9.1 9.2

Has this data alreadybeen gathered? If so,

WHERE? NO, needto gather data

NO, neednew mission

ScienceDataNeed

YES, here is the DATA! YES, recommend thisOBSERVATORY!

Science Mission Parameters

Science Mission Assistant and Research Tool (SMART)

Intelligent DataProspector (IDP) Intelligent Resource

Prospector (IRP)

Design Assistant(IMDA)

Intelligent Mission

Can anexisting resource

obtain the data for me?If so,

WHAT?

I would like toformulate

a new mission…HOW?

Example KM Aplication: SMART KM Portal

SMART: Science Mission Assistant & Research ToolCategorization: An interactive, web-based tool suitePurpose: Reduce time/cost required to define new science initiatives

Uncertainty

SMART is Architected as a Web Portal

SMART User

WebBrowser

http://smart.gsfc.nasa.gov

SMART

Intelligent Data Prospector Find data sets

Intelligent Resource Prospector Find an observatory

Intelligent Mission Design Asst Design a science mission

http://smart.gsfc.nasa.gov/irp/

Browse Observatory Knowledge Base Map

TreeObservatory Lists

Search Observatory Knowledge BaseWord/Phrase Search Interactive Dialog

DiscussionsExperts

SMARTIntelligent Resource Prospector

http://smart.gsfc.nasa.gov/imda/

Browse Mission Knowledge Base MapTreeMission Lists

Search Mission Knowledge BaseWord/Phrase Search Interactive Dialog

DiscussionsExperts

Design a Mission

SMARTIntelligent Mission Design Asst

SMARTConcept Map Viewer: Observatories

SMARTHierarchical DirectoryViewer

SMARTDatabase Views

SMARTConversational CBRQuestion/ResponseInterface

SMARTCollaborativeDiscussions Interface

SMART IMDADesign a Mission

Create/Edit a MissionValidate Design

Power Design AdvisorThermal Design AdvisorCommunications

Design Advisor…

InvokeDesign

ValidationAgent

(applet)

(serverDBaccess)

(applet)

(KM toolservice)

(KM toolservice)

(expertsystems)

Searching for Missions Using CCBR

SMARTConversational Mission Search Engine

Describe what you are looking for:“I’m looking for astronomy missions in low-Earth orbit.”

Ranked questions:Score Answer Name Title

“X-ray” Q17 What portion of the spectrum is observed?60 Q7 What launch vehicle?50 Q32 What mission phase?20 Q23 Low or high inclination orbit?10 Q41 Cryogenically-cooled instrument?Ranked cases:Score Name Title90 XTE X-Ray Timing Explorer90 AXAF Chandra X-Ray Observatory30 GRO Gamma Ray Observatory30 EUVE Extreme Ultra-Violet Explorer

Question: Q17Title: What portion of the spectrum is observed?Description: What portion of the electro-magnetic spectrum are you interested in?Select your answer:

Visible light Infra-red Ultra-violet Microwave X-Ray Radiowave Gamma Ray

SMART Browse/Search Process

ConceptMaps

Conceptof

Interest

Form/DB

Query

Web-basedDocument

Library

ExternalKnowledge

Sources

CCBR

Objectof

Interest

Document of

Interest

Select

Query Result

URL

Find

Word docPresentationSpreadsheetBookmark

Browse KB

Browse HierarchySearch Keywords

Search KB Objects

Search KB Objects

Select

URL

URL

URL

URL

URL

URL

URL

SMART Users have a variety of browse and search tools to find documents, objects, and external knowledge sources.

SMART Knowledge Base Objects

XMLObjects

RDB

CaseBases

FactBases

Cmaps CmapTool

CCBRTool

KnowledgeAgents

VisualXML

Editor

Forms/DBInterface

SpreadsheetInterface

Wizards

Case entry/search

Analysis

e.g. Cmap Search Agent Design Validation Agent

Cmapedit/view

Knowledgecapture/view

Knowledgecapture/view

Knowledgecapture

SMART uses XML as the standard representation of knowledge base objects.

Lessons LearnedKeywords: Philippines, evacuation, disaster relief, c2, NEO, Fiery Vigil, etc.

Observation: Assignment of air traffic controllers to augment host country controllers was critical to safe evacuation airfield operation.

Discussion: The rapid build-up of military flight operations…overloaded the civilian host nation controllers. Military controllers maintained 24 hour operations. ...

Lesson Learned: Military air traffic controllers are required whenever a civilian airport is transformed into an intensive military operating area for contingency operations.

Recommended Action: Ensure controllers and liaison teams are part of the evacuation package, and establish early liaison with host nation to coordinate an agreement on operational procedures.

What

How

When

Joint Unified Lessons Learned System (JULLS)

Database: 908 “scrubbed” lessons from the CINC’s (1991-)– Unclassified subset: 150 lessons (Armed Forces Staff College)

• 33 relate to NEOs

Lesson Format: 43 attributes– e.g., ID Number, submitting command, subject, date– Unified Joint Task List number– Content attributes: All in text format

6 Keywords6 Observation6 Discussion6 Lesson learned6 Recommended action

Some Lessons Learned Centers/SystemsAir Force o Air Force Automated Lessons Learned Capture and Retrieval System o Air Force Center for Knowledge Sharing Lessons Learned o Air Combat Command Center for Lessons Learned o Automated Lessons Learned Collection & Retrieval SystemArmy o Center for Army Lessons Learned (CALL) o SARDA: Contracting Lessons Learned o US Army Europe - Lessons Learned SystemCoast Guard o Coast Guard Universal Lessons LearnedJoint Forces o JCLL: Joint Center for Lessons LearnedMarine Corps o Marine Corps Lessons Learned SystemNavy o NDC: Navy Doctrine Command Lessons Learned System o NAWCAD: Navy Combined Automated Lessons Learned o NAVFAC: Naval Facilities Engineering Command Lessons Learned System Government (non-military) o NASA Lessons Learned Information System o International Safety Lessons Learned Information System o NASA-Goddard: RECALL: Reusable Experience with CBR for Automating Lessons Learned) o NIST: Best Practices Hyperlinks o DoE: US Department of Energy Lessons Learned Other o Canadian Army Lessons Learned Centre o United Nations: UN Lessons Learned in Peacekeeping Operations

Lessons Learned Repositories: Functionality

Center forLessons Learned

Documented Lessons

Decision-SupportTool

RetrievalTool

Interface

LessonsLearned

Repository

Lessons Learned System

Search queries Relevantlessons

Lessons Learned Systems: Unrealistic Assumptions

The decision maker1. has time to search for lessons,2. knows where to search for lessons,3. knows how to search for lessons, and4. knows how to interpret retrieved lessons for their

current decision-making context.

Decision SupportTool

UserInterface

Active Lessons Learned Repositories

Center forLessons Learned

Documented Lessons

RetrievalTool

Interface

LessonsLearned

Repository

Lessons Learned System

LL Agent: (CBR)• Relevance

Assessment• Retrieval• Interpretation

Search queries Relevantlessons

Issues for Active Lessons Learned

Documented Lessons

LL Agent(CBR)User

Case Library

Case extraction

Decision SupportTool

Decision-Making Process

1. Case extraction methods2. Case representation3. Choice of decision support tool4. Embedded LL agent behavior

Case Extraction Methods

Textual CBR Tasks:– Case retrieval (FAQ analysis, travel planning) (Lenz et al. 1998)– Extract/highlight relevant portions of case text (Daniels, 1998)– Assigning indices to case texts (Bruninghaus & Ashley, 1999)– Reasoning with cases as text (Weber et al., 2000?)

Textual CBR: • Involves CBR applications where cases are available as texts.• Retrieve, highlight, assign indices to or reason about textual

cases automatically.• Apply CBR knowledge representation frameworks, application-

specific, problem-solving knowledge and other knowledge.

Textual CBR Info Sources

1. Meaning of terms in documents (e.g., thesauri, glossaries)2. Document structure3. Annotated excerpts and summaries4. Citation information5. Linguistic knowledge (i.e., to identify phrases, negation, etc.)6. Frame-based structures for case representation (e.g.,

CMaps)7. Abstraction hierarchies (i.e., relating indices to abstract

concepts)8. Contextual relationship of words (i.e., in manually-classified

texts)

4. Embedded LL Behavior: A Critiquing Agent

USER

LessonsLearned

Case LibraryAutonomous LL Agent

(CBR Engine)

Decision Support Tool

Objects, Operators

Alerts,Recommendations

IndexSimilarity Assessment

Action

Case TypeTask Decompositiontask it decomposesInteractiveTask Subtasks

Lesson Learnedlesson’s conditionsAutomatedArbitrary modifications to System’s objects

Objects := Apply(Op,Objects)Operator selection

Lessons Learned: NEO Critiquing Example

Compose an Intermediate Stage Base

Tasks

Scenario:• 50 miles from ISB #1• 30 miles from ISB #2

• Commercial airfield

Resources:• Transport vehicles

• …• Joint Air Command

• Military air traffic controller• ...

Objects:1. Planning tasks2. Resources3. Assignments4. Task relations5. Scenario

Coordinatewith localsecurity forces

Coordinate withairfield traffic controllers

...

Lesson Learned #13167-92740:• Index: Coordinate w/ traffic controllers• Lesson: If ISB is a commercial airfield,

then assign military air traffic controllers to the evacuation package

Transport militaryair traffic controller to ISB

Process-Oriented CBR(“It’s the Process, Stupid!”)

Most KM tasks are performed in the context of a well-defined (e.g., business) process, and any techniques

designed to support KM must be embedded in this process

KM examples (many):• Enterprise resource planning (O’Leary)• Project process (Maurer & Holz)

CBR examples (few):•Leake et al.: Feasibility assessment in design process•Moussavi, Shimazu: Cases represent processes•Reddy & Munoz-Avila: Project Planning

Distinguishing KM from Data Mining

KDD Focus:• Large databases• Autonomous pattern recognition

Knowledge Discovery from Databases Process:

Database Acquisition

Data Warehousing

Data Cleansing

Data Mining

Data Maintenance

KM Focus:• Capturing organizational dynamics processes• Interaction (i.e., decision support)

KM/CBR: Possible Future Directions

1. Applications– e-Commerce– Decision support systems

• Personalized– Knowledge discovery for databases?

• Yet KDD stresses need for many automated tasks

2. Multimodal systems– e.g., Shimazu: Audio tapes of customer dialogues– Information gathering– Learning assistants

3. Process-focused emphases:– Retrieval, adaptation, and composition of processes

Summary

• There is a real need for Knowledge Management

• Out-of-Family Disposition (OOFD) Process as a particular kind of KM problem

• Studied a concrete application: SMART (NASA)

• Lesson Learned

• Future research applications