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Knowledge Management: A CBR Perspective

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 to run off the 28 volt DC The tanks were redesigned to also run off the 65 volt DC .

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Knowledge Management: A CBR Perspective

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

  2. 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

  3. The Changing Game The New Economics Manufacturing Service Tangible Intangible Consumable Inconsumable Structural 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)

  4. Needs Organizational Dynamics Technology Develop a culture for knowledge sharing Needs Effective tools to capture, leverage & reuse knowledge 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

  5. Knowledge Management: Issues • Technical and Business Expertise: • Proficiencies • Know-How • Skills • Work Practice Execution: • Processes • Methodologies • Practices • Lessons learned

  6. 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

  7. CBRWorks KM eGain eService Enterprise (E3) 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

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

  9. 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

  10. 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 knowledgeprocesses • 2. Benefits of semi-structured case representations • 3. Interactive (“conversational”) systems

  11. 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

  12. 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

  13. Complex problem solving Problem acquisition Development and Management Methodologies Experience evaluation and retrieval Reuse-related knowledge 1. Retrieve Experience base 4. Retain Experience presentation Experience adaptation Case Library 2. Reuse Background Knowledge 3. Revise Experience Management vs CBR Experience Management (Organization) BOOK CBR (IDSS)

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

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

  16. Information Sources Personal Portal/ Workspace Personalization Assistant Agent Semantic Web Ontologies Case Repository Causal Model Current Problem Distributed data sources DS1 User Ontologies DS2 DS3

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

  18. NASA-Kennedy Space Center: Shuttle Processing Directorate Out-of-Family Disposition (OOFD) Process KM expertise Pre-flight, launch, landing, recovery Prof. I. Becerra-Fernandez 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

  19. Data Drop Out 2 2.2 2.3 2.4 2.5 2.1 Computer FCMS GMT Discrepancy 12 PCM 3 Shows up in PCM 2 4 Electrical 7 Micro-switch Malfunctioning Unexplained Power Drops 11 11.1 11.2 11.3 11.4 11.5 11.6 Prompt Problem Helium ISO Valves 1 1.1 1.2 Backup HGDS 3 Mechanical Seal Port Dynatube 5 Catch Bottle Relief Valve 6 Stress Corrosion Cracking 10 10.1 10.2 10.3 Debris Detected in Stiffener ring 8 8.1 8.2 8.3 Materials Cracked A8U Panel 9 9.1 9.2 OOFD: Problem Categorization (Ontology)

  20. Uncertainty 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

  21. SMART is Architected as a Web Portal SMARTConcept Map Viewer: Observatories SMARTIntelligent Resource Prospector (applet) Browse Observatory Knowledge Base MapTreeObservatory ListsSearch Observatory Knowledge Base Word/Phrase Search Interactive DialogDiscussionsExperts SMARTHierarchical DirectoryViewer (KM toolservice) SMART Web Browser Intelligent Data Prospector Find data sets Intelligent Resource Prospector Find an observatory Intelligent Mission Design Asst Design a science mission SMARTDatabase Views (serverDBaccess) http://smart.gsfc.nasa.gov/irp/ SMART User SMARTConversational CBRQuestion/ResponseInterface (applet) SMARTIntelligent Mission Design Asst http://smart.gsfc.nasa.gov Browse Mission Knowledge Base MapTreeMission ListsSearch Mission Knowledge Base Word/Phrase Search Interactive DialogDiscussionsExperts Design a Mission SMARTCollaborativeDiscussions Interface (KM toolservice) InvokeDesign ValidationAgent SMART IMDADesign a Mission Create/Edit a MissionValidate DesignPower Design AdvisorThermal Design AdvisorCommunicationsDesign Advisor … (expertsystems) http://smart.gsfc.nasa.gov/imda/

  22. Searching for Missions Using CCBR SMARTConversational Mission Search Engine Question: Q17 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 Infra-redUltra-violet MicrowaveX-Ray RadiowaveGamma Ray 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

  23. SMART Browse/Search Process Web-basedDocumentLibrary Select Document of Interest Word docPresentation Spreadsheet Bookmark URL Browse HierarchySearch Keywords Form/DBQuery ConceptofInterest URL Select URL ConceptMaps Query Result ObjectofInterest URL Browse KB Search KB Objects URL URL Find SMART Users have a variety of browse and search tools to find documents, objects, and external knowledge sources. URL CCBR Search KB Objects ExternalKnowledgeSources URL

  24. SMART Knowledge Base Objects Case entry/search Knowledgecapture/view CCBRTool XMLObjects CaseBases VisualXMLEditor Analysis KnowledgeAgents Forms/DBInterface Knowledgecapture/view FactBases e.g. Cmap Search Agent Design Validation Agent RDB SpreadsheetInterface Cmapedit/view CmapTool Cmaps Knowledgecapture Wizards SMART uses XML as the standard representation of knowledge base objects.

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

  26. 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 • Keywords • Observation • Discussion • Lesson learned • Recommended action

  27. 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 DoE: US Department of Energy Lessons Learned • Other • o Canadian Army Lessons Learned Centre • o United Nations: UN Lessons Learned in Peacekeeping Operations

  28. Decision-Support Tool Documented Lessons Relevant lessons Search queries Center for Lessons Learned Lessons Learned Repository Retrieval Tool Interface Lessons Learned System Lessons Learned Repositories: Functionality

  29. 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.

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

  31. Demo

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

  33. 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, application-specific, 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?)

  34. 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)

  35. Task Decomposition task it decomposes Interactive Task Subtasks Lessons Learned Case Library Case Type Lesson Learned lesson’s conditions Automated Arbitrary modifications to System’s objects Index Similarity Assessment Action 4. Embedded LL Behavior: A Critiquing Agent Decision Support Tool Operator selection U S E R Objects := Apply(Op,Objects) Objects, Operators Alerts, Recommendations Autonomous LL Agent (CBR Engine)

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

  37. 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

  38. Knowledge Discovery from Databases Process: Database Acquisition Data Warehousing • KDD Focus: • Large databases • Autonomous pattern recognition Data Cleansing Data Mining Data Maintenance Distinguishing KM from Data Mining • KM Focus: • Capturing organizational dynamics processes • Interaction (i.e., decision support)

  39. 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

  40. 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

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