ai in knowledge management professor robin burke csc 594
TRANSCRIPT
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AI in Knowledge Management
Professor Robin Burke
CSC 594
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Outline
Introduction to the class Overview
Knowledge managementAICase-based reasoning
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Objectives
Content Explore AI applications in knowledge
management• specifically case-based reasoning
SkillsReading research literatureBuilding an informal knowledge base
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Course design
Seminar formatstudent presentationsin-class exercises
Attendance VERY IMPORTANT! Reading VERY IMPORTANT!
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Reading
Two main readings each weekcase studyresearch article
Admission ticket1-2 page reaction paperwhat did you find interesting?a discussion question
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Assessment
Presentations – 40% two presentations / student 1 case study 1 research paper
Participation – 50% course librarian discussion
Final Project – 10% more later
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Typical class session
Case study30 min. presentation15 min. discussion
Research paper30 min. presentation15 min. questions
Librarian’s reports Group exercise
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Artificial intelligence
The subfield of computer science concerned with the concepts and methods of symbolic inference by computer and symbolic knowledge representation for use in making inferences.
AI can be seen as an attempt to model aspects of human thought on computers. It is also sometimes defined as trying to solve by computer any problem that a human can solve faster.
-- FOLDOC
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Knowledge management
Knowledge management involves the acquisition, storage, retrieval, application, generation and review of the knowledge assets of an organization in a controlled way.
-- I. Watson
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Example: oil industry
old model own oil wells pump oil sell it
problem how to grow when there’s no more wells to
own? volatility of oil market low margins for commodity products high costs
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Example: cont’d
solution: reconceptualize businessoilfield expertise
benefitseveryone needs know-howexpertise is always valuable
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Hierarchy of knowledge
Knowledge expert analysis synthesis integration with experience
Information reports on data summarization
Data recorded information
The world stuff happens
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Knowledge assets
Usually intangiblein worker’s heads
How to make experience explicit?not just what?but also why, how, and why not?
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AI + Knowledge Management
Model aspects of human thought on computers
Which aspects?the storage and use of experience
What sub-field of AI studies this?case-based reasoning
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Problem-solving
One of the first two areas tackled by AI researchother is natural language
How do we solve problems?researchers looked at logic puzzles
and problems of robot control
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Rule-based reasoning
What are the steps to the solution? problem situation desired result
Forward-chaining reason forward from the problem
Backward-chaining reason backward from the desired state
Build up large rule bases also control knowledge
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Case-based reasoning
An alternative to rule-based problem-solving
“A case-based reasoner solves new problems by adapting solutions used to solve old problems”
-- Riesbeck & Schank 1987
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Paradox of the expert
Experts should have more rulescan solve more problemscan be much more precise
But experts are faster than noviceswho presumably have fewer rules
What does experience provide if it isn’t just “more rules”?
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Problems we solve this way Medicine
doctor remembers previous patients especially for rare combinations of symptoms
Law English/US law depends on precedence case histories are consulted
Management decisions are based on past experience
Financial performance is predicted by past results
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Retain Review
Adapt
Retrieve
Database
NewProblem
Similar
SolutionSolution
CBR Solving Problems
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CBR System Components
Case-base database of previous cases (experience) episodic memory
Retrieval of relevant cases index for cases in library matching most similar case(s) retrieving the solution(s) from these case(s)
Adaptation of solution alter the retrieved solution(s) to reflect differences
between new case and retrieved case(s)
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R4 Cycle
REUSEREUSEpropose solutions from retrieved cases
REVISEREVISEadapt and repair
proposed solution
CBRCBR
RETAINRETAINintegrate in
case-base
RETRIEVERETRIEVEfind similar problems
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CBR Assumption New problem can be solved by
retrieving similar problemsadapting retrieved solutions
Similar problems have similar solutions
?
SSS
SS S
SS S
PP
PPPP
P
PP
X
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AI in Knowledge Management
Apply the CBR model to the organization rather than the individualRetain the experience of the firmApply it in new situationsDo this in a consistent, automated
way
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How to do this?
Very situation-specific What is a case? What counts as similar? What do you need to know to adapt
old solutions? How do you find and remove obsolete
cases?
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CBR Knowledge Containers
Cases Case representation language Retrieval knowledge Adaptation knowledge
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Cases
Contentslesson to be learnedcontext in which lesson applies
Issuescase boundaries
• time, space
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Case representation language
Contentsfeatures and values of
problem/solution Issues
more detail / structure = flexible reuseless detail / structure = ease of
encoding new cases
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Retrieval knowledge
Contentsfeatures used to index casesrelative importance of featureswhat counts as “similar”
Issues“surface” vs “deep” similarity
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Nearest Neighbour Retrieval
Retrieve most similar k-nearest neighbour
k-NN Example1-NN5-NN
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How do we measure similarity?
Can be strictly numericweighted sum of similarities of
features“local similarities”
May involve inferencereasoning about the similarity of items
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Adaptation knowledge
Contentscircumstances in which adaptation is
neededhow to modify
Issuesrole of causal knowledge
• “why the case works”
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Learning Case-base
inserting new cases into case-base updating contents of case-base to avoid mistakes
Retrieval Knowledge indexing knowledge
• features used• new indexing knowledge
similarity knowledge• weighting• new similarity knowledge
Adaptation knowledge
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What this class is about
We will study examples of KM-related CBR applications
We will study CBR technology and research
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Next week
Case study R. Burke & A. Kass (1994) "Tailoring
Retrieval to Support Case-Based Teaching." Proceedings of the 12th Annual Conference on Artificial Intelligence.
Research A. Aamodt & E. Plaza (1994) "Case-based
reasoning: Foundational issues, methodological variations, and system approaches." AI Communications, 7:39-59
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Administrativa
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