Transcript Closing some loose ends

Closing Some Loose Ends
Sources:
•David W. Aha
•My own
•Thomas H. Davenport, Laurence Prusak, 1998
Classifiers
Classifiers
• Case-based reasoning (CBR) classifier
• Induction of decision trees (IDT)
• CBR+IDT classifier
• Others (e.g., covered in the Data Mining course):
 Support Vector Machines
 Linear regression
 Neural networks
 …
So which one is best?
No Free Lunch Theorem
• Each of these classifiers have a bias
• To explain the bias, let us examine a situation where
instances (or cases) are pairs of numeric features and a binary
classification problem:
((x,y),class)
• Let us draw the space: CBR, K-d trees (K=2), Support vector
machines
• Let us construct examples where each of these classifiers
works best
 How does the other classifiers work on these
examples?
• Formulation of the no free lunch theorem
Knowledge Management
The Beginning: The Apollo 13 Situation
http://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 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, & 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:
Proficiencies
Know-How
Skills
•Work Practice Execution:
Processes
Methodologies
Practices
Lessons 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
CBRWorks
eGain eService Enterprise (E3)
KM
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
3. Capturing and reusing past experiences
Yes
4. Embedding knowledge (products/services/processes) Yes
Yes
5. Producing knowledge as a product
6. Driving knowledge generation for innovation
No
7. Mapping networks of experts
No
8. Building/mining customer knowledge bases
Yes
9. Understanding/mining customer knowledge bases No
Yes
10. Leveraging intellectual assets.
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 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
(Organization)
Problem
acquisition
Experience
base
Reuserelated
knowledge
Experience
presentation
Experience
adaptation
BOOK
CBR
Experience
evaluation
and retrieval
Development and
Management
Methodologies
Experience
Management
Complex
problem
solving
Case
Library
1. Retrieve
4. Retain
Background
Knowledge
(IDSS)
3. Revise
2. Reuse
Relating KM with AI
AI
CBR
Knowledge-Based
Systems
Human
Factors
KM
Business
Processing
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)
Process-Oriented CBR
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
Motivation for Design Project
• Embedding CBR into existing tools has been shown to be
an effective way to insert CBR into KM processes
 We saw it this year in a number of projects:
Help-desk for LTS
Recommender for university events
Companies processes
•
We discuss two applications
 They have a similar flavor to most of the design
projects
Two Examples
AFRL Proposed KM Environment
EXTERNAL MONITORING
INFORMATION SOURCES
Library catalog
MIS
WORKSPACE
Online
databases
Profiles
Workflow
Spiders
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 sources
DS1
User Ontologies
DS2
Personal Portal/
Workspace
Information
Sources
DS3
Finance
Data
Systems
Individualized Portal
Buckets
Virtual
Library
Personnel
Information
Domains
Executive Information System
A
B
C
D
Out-of-Family Disposition (OOFD) Process
NASA-Kennedy Space Center:
Shuttle Processing Directorate
Prof. I. Becerra-Fernandez
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
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
Science Mission Assistant and Research Tool (SMART)
Science
Data
Need
Intelligent Mission
Design Assistant
Intelligent Data
Prospector (IDP)
Has this data already
been gathered? If so,
WHERE?
(IMDA)
Intelligent Resource
Prospector (IRP)
Can an
existing resource
obtain the data for me?
If so,
NO, need
NO, need
WHAT?
to gather data
new mission
YES, here is the DATA!
YES, recommend this
OBSERVATORY!
I would like to
formulate
a new mission…
HOW?
Science Mission Parameters
Uncertainty
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 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
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: 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
 Ultra-violet
 X-Ray
 Gamma Ray
Infra-red
Microwave
 Radiowave


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
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-)
– 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
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
Lessons Learned Repositories: Functionality
Decision-Support
Tool
Search queries
Retrieval
Tool
Interface
Relevant
lessons
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,
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 Agent: (CBR)
• Relevance
Assessment
• Retrieval
• Interpretation
Retrieval
Tool
Interface
Relevant
lessons
Lessons
Learned
Repository
Lessons Learned System
Center for
Lessons
Learned
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
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
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