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Knowledge Management: A CBR Perspective Sources: • David W. Aha • My own • Knowledge Management: A CBR Perspective Sources: • David W. Aha • My own • Thomas H. Davenport, Laurence Prusak, 1998

The Beginning: The Apollo 13 Situation • The oxygen tanks had originally been designed The Beginning: The Apollo 13 Situation • 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 Economics Manufacturing Tangible Consumable Structural Service Intangible Inconsumable Intellectual The Changing Game The New Economics Manufacturing Tangible Consumable Structural Service Intangible Inconsumable 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, Knowledge Management (KM) An increasingly important new business movement that promotes the creation, sharing, & leveraging of knowledge within an organization to maximize business results. Problems: Financial constraints Loss of organizational knowledge Needs Organizational Dynamics Develop a culture for knowledge sharing Technology Needs Effective tools to capture, leverage & reuse knowledge

Knowledge Management: Issues • Technical and Business Expertise: ØProficiencies ØKnow-How ØSkills • Work Practice Knowledge Management: Issues • Technical and Business Expertise: ØProficiencies ØKnow-How ØSkills • Work Practice Execution: ØProcesses ØMethodologies ØPractices ØLessons learned

Why Knowledge Management? • Leverages Core Business Competence • Accelerates Innovation (Time to Market) 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 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 CBRWorks e. Gain e. Service Enterprise (E 3) KM

KM Project Domains: CBR Applicable? (KM World, 1/99, Dan Holtshouse, Xerox) KM Domains/Tasks CBR KM Project Domains: CBR Applicable? (KM World, 1/99, Dan Holtshouse, Xerox) KM Domains/Tasks CBR Applicable? Yes 1. Sharing knowledge and best practices No 2. Instilling responsibility for knowledge sharing Yes 3. Capturing and reusing past experiences 4. Embedding knowledge (products/services/processes) Yes 5. Producing knowledge as a product 6. Driving knowledge generation for innovation No 7. Mapping networks of experts No Yes 8. Building/mining customer knowledge bases 9. Understanding/mining customer knowledge bases No Yes 10. Leveraging intellectual assets.

Recent Events Related to KM/CBR 1999 Summer Workshops: – AAAI: Exploring the Synergies between 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, 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 researchers 2. Report on recent CBR approaches for KM tasks 3. Share cautions, knowledge, & experiences Some observations: 1. Embedded/integrated in knowledge processes 2. Benefits of semi-structured case representations 3. Interactive (“conversational”) systems

Limitations of CBR for KM (from the 1999 AAAI KM/CBR Workshop) 1. Main limitation 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 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 (Organization) Problem acquisition Experience base Reuserelated knowledge Experience presentation Experience Experience Management vs CBR (Organization) Problem acquisition Experience base Reuserelated knowledge Experience presentation Experience adaptation BOOK CBR Experience evaluation and retrieval Case Library 1. Retrieve 4. Retain Background Knowledge (IDSS) 3. Revise Development and Management Methodologies Experience Management Complex problem solving 2. Reuse

Relating KM with AI AI CCBR Knowledge-Based Systems Human Factors KM Business Processing Relating KM with AI AI CCBR Knowledge-Based Systems Human Factors KM Business Processing

AFRL Proposed KM Environment EXTERNAL MONITORING INFORMATION SOURCES Library catalog Online databases Spiders MIS AFRL Proposed KM Environment EXTERNAL MONITORING INFORMATION SOURCES Library catalog Online databases Spiders MIS WORKSPACE Profiles Workflow Scheduling E-journals PERSONAL PORTAL Records Management How-to guides Suspenses Document Management Alerts E-mail Buckets OA tools Collaboration Bulletin boards Document Delivery Service (multi? ) impersonal

Personalization Assistant Agent Semantic Web Ontologies Case Repository Causal Model Current Problem Distributed data Personalization Assistant Agent Semantic Web Ontologies Case Repository Causal Model Current Problem Distributed data sources DS 1 User Ontologies DS 2 Personal Portal/ Workspace Information Sources DS 3

Finance Data Systems Individualized Portal Buckets Virtual Library Personnel Information Domains Executive Information System Finance Data Systems Individualized Portal Buckets Virtual Library Personnel Information Domains Executive Information System

Out-of-Family Disposition (OOFD) Process KM e Prof. I. Becerra-Fernandez NASA-Kennedy Space Center: xpert ise Out-of-Family Disposition (OOFD) Process KM e Prof. I. Becerra-Fernandez NASA-Kennedy Space Center: xpert ise Shuttle Processing Directorate Pre-flight, launch, landing, recovery CBR expertise Topic: Performing project tasks outside range of expertise • Lack of task familiarity Motivations: Downsizing, employee loss, technology pace Resources: Interim problem reports • Standardized text documents for reporting problems/solutions • Given: 12 of these reports Another example: legal constraints

OOFD: Problem Categorization (Ontology) Micro-switch Malfunctioning 7 11 11. 2 1 1. 2 3 OOFD: Problem Categorization (Ontology) Micro-switch Malfunctioning 7 11 11. 2 1 1. 2 3 Seal Port Dynatube 5 Catch Bottle Relief Valve 6 Stress Corrosion Cracking 10 10. 1 10. 2 10. 3 Debris Detected in Stiffener ring Materials 4 Backup HGDS Mechanical 12 Helium ISO Valves Prompt Problem FCMS GMT Discrepancy Unexplained Power Drops Electrical 2 PCM 3 Shows up in PCM 2 Computer Data Drop Out 8 8. 1 8. 2 8. 3 Cracked A 8 U Panel 9 9. 1 9. 2 2. 1 2. 2 2. 3 2. 4 11. 3 2. 5 11. 4 11. 5 11. 6

Example KM Aplication: SMART KM Portal SMART: Science Mission Assistant & Research Tool Categorization: Example KM Aplication: SMART KM Portal SMART: Science Mission Assistant & Research Tool Categorization: An interactive, web-based tool suite Purpose: Reduce time/cost required to define new science initiatives Uncertainty

SMART is Architected as a Web Portal SMART Intelligent Resource Prospector SMART Web Browser SMART is Architected as a Web Portal SMART Intelligent Resource Prospector SMART Web Browser Intelligent Data Prospector Find data sets Intelligent Resource Prospector Find an observatory SMART User Intelligent Mission Design Asst Design a science mission Browse Observatory Knowledge Base Map Tree Observatory Lists Search Observatory Knowledge Base Word/Phrase Search Interactive Dialog Discussions Experts (applet) SMART Hierarchical Directory Viewer (KM tool service) SMART Database Views (server DB access) http: //smart. gsfc. nasa. gov/irp/ SMART http: //smart. gsfc. nasa. gov SMART Concept Map Viewer: Observatories Intelligent Mission Design Asst Browse Mission Knowledge Base Map Tree Mission Lists Search Mission Knowledge Base Word/Phrase Search Interactive Dialog Discussions Experts Design a Mission http: //smart. gsfc. nasa. gov/imda/ SMART Conversational CBR Question/Response Interface SMART Collaborative Discussions Interface SMART IMDA Design a Mission Create/Edit a Mission Validate Design Power Design Advisor Thermal Design Advisor Communications Design Advisor … (applet) (KM tool service) Invoke Design Validation Agent (expert systems)

Searching for Missions Using CCBR SMART Conversational Mission Search Engine Describe what you are Searching for Missions Using CCBR SMART Conversational 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” Q 17 What portion of the spectrum is observed? 60 Q 7 What launch vehicle? 50 Q 32 What mission phase? 20 Q 23 Low or high inclination orbit? 10 Q 41 Cryogenically-cooled instrument? Ranked cases: Score Name 90 XTE 90 AXAF 30 GRO 30 EUVE Title X-Ray Timing Explorer Chandra X-Ray Observatory Gamma Ray Observatory Extreme Ultra-Violet Explorer Question: Q 17 Title: What portion of the spectrum is observed? Description: What portion of the electro-magnetic spectrum are you interested in? Select your answer: Visible light Ultra-violet X-Ray Gamma Ray Infra-red Microwave Radiowave

SMART Browse/Search Process Web-based Document Library URL Concept Maps Select Concept of Interest URL SMART Browse/Search Process Web-based Document Library URL Concept Maps Select Concept of Interest URL Browse KB Select Document of Interest Browse Hierarchy Search Keywords Form/ DB Query URL Query Result Object of Interest Search KB Objects URL SMART Users have a variety of browse and search tools to find documents, objects, and external knowledge sources. Search KB Objects URL URL Find CCBR Word doc Presentation Spreadsheet Bookmark URL External Knowledge Sources

SMART Knowledge Base Objects Visual. X ML Editor Forms/DB Interface Knowledge capture/view XML Objects SMART Knowledge Base Objects Visual. X ML Editor Forms/DB Interface Knowledge capture/view XML Objects Knowledge capture/view Case Bases Fact Bases Case entry/ search Analysis CCBR Tool Knowledge Agents e. g. Cmap Search Agent Design Validation Agent Spreadsheet Interface RDB Knowledge capture Cmaps Cmap edit/ view Cmap Tool Wizards SMART uses XML as the standard representation of knowledge base objects.

Lessons Learned Keywords: Philippines, evacuation, disaster relief, c 2, NEO, Fiery Vigil, etc. Observation: Lessons Learned Keywords: Philippines, evacuation, disaster relief, c 2, 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 When civilian host nation controllers. Military controllers maintained 24 hour operations. . What How 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.

Joint Unified Lessons Learned System (JULLS) Database: 908 “scrubbed” lessons from the CINC’s (1991 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 Keywords 6 Observation 6 Discussion 6 Lesson learned 6 Recommended action

Some Lessons Learned Centers/Systems Air Force o Air Force Automated Lessons Learned Capture and Some Lessons Learned Centers/Systems Air 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 System Army o Center for Army Lessons Learned (CALL) o SARDA: Contracting Lessons Learned o US Army Europe - Lessons Learned System Coast Guard o Coast Guard Universal Lessons Learned Joint Forces o JCLL: Joint Center for Lessons Learned Marine Corps o Marine Corps Lessons Learned System Navy 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 Do. E: 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 Decision-Support Tool Search queries Retrieval Tool Interface Relevant lessons Learned Lessons Learned Repositories: Functionality Decision-Support Tool Search queries Retrieval Tool Interface Relevant lessons Learned Repository Lessons Learned System Documented Lessons Center for Lessons Learned

Lessons Learned Systems: Unrealistic Assumptions The decision maker 1. has time to search for Lessons Learned Systems: Unrealistic Assumptions The decision maker 1. has time to search for lessons, 2. knows where to search for lessons, 3. knows how to search for lessons, and 4. knows how to interpret retrieved lessons for their current decision-making context.

Active Lessons Learned Repositories Decision Support Tool Documented Lessons User Interface Search queries LL Active Lessons Learned Repositories Decision Support Tool Documented Lessons User Interface Search queries LL Agent: (CBR) • Relevance Assessment • Retrieval • Interpretation Retrieval Tool Interface Relevant lessons Learned Repository Lessons Learned System Center for Lessons Learned

Demo Demo

Issues for Active Lessons Learned Case extraction Documented Lessons Case Library Decision-Making Process User Issues for Active Lessons Learned Case extraction Documented Lessons Case Library Decision-Making Process User Decision Support Tool LL Agent (CBR) 1. Case extraction methods 2. Case representation 3. Choice of decision support tool 4. Embedded LL agent behavior

Case Extraction Methods Textual CBR: • Involves CBR applications where cases are available as Case Extraction Methods 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, applicationspecific, problem-solving knowledge and other knowledge. 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 Info Sources 1. Meaning of terms in documents (e. g. , thesauri, Textual CBR Info Sources 1. Meaning of terms in documents (e. g. , thesauri, glossaries) 2. Document structure 3. Annotated excerpts and summaries 4. Citation information 5. 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 Decision Support Tool U S E R 4. Embedded LL Behavior: A Critiquing Agent Decision Support Tool U S E R Operator selection Objects : = Apply(Op, Objects) Objects, Operators Alerts, Recommendations Autonomous LL Agent (CBR Engine) Lessons Learned Case Library Case Type Index Similarity Assessment Action Task Decomposition task it decomposes Interactive Task Subtasks Lesson Learned lesson’s conditions Automated Arbitrary modifications to System’s objects

Lessons Learned: NEO Critiquing Example Tasks . . . Coordinate with local security forces Lessons Learned: NEO Critiquing Example Tasks . . . Coordinate with local security forces Compose an Intermediate Stage Base Objects: 1. Planning tasks 2. Resources 3. Assignments 4. Task relations 5. Scenario Coordinate with Resources: airfield traffic controllers • Transport vehicles Transport military air traffic controller to ISB 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 • … • Joint Air Command • Military air traffic controller • . . . Scenario: • 50 miles from ISB #1 • 30 miles from ISB #2 • Commercial airfield

Process-Oriented CBR (“It’s the Process, Stupid!”) Most KM tasks are performed in the context Process-Oriented CBR (“It’s the Process, Stupid!”) Most KM tasks are performed in the context of a welldefined (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 Knowledge Discovery from Databases Process: Database Acquisition Data Warehousing Distinguishing KM from Data Mining Knowledge Discovery from Databases Process: Database Acquisition Data Warehousing KDD Focus: Data Cleansing • Large databases • Autonomous pattern recognition 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 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) 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 • Demo of application • Future research applications