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AI in Knowledge
Management
Professor Robin Burke
CSC 594
Outline
Introduction to the class
 Overview

Knowledge management
 AI
 Case-based reasoning

Objectives

Content

Explore AI applications in knowledge
management
• specifically case-based reasoning

Skills
Reading research literature
 Building an informal knowledge base

Course design

Seminar format
student presentations
 in-class exercises

Attendance VERY IMPORTANT!
 Reading VERY IMPORTANT!

Reading

Two main readings each week
case study
 research article


Admission ticket
1-2 page reaction paper
 what did you find interesting?
 a discussion question

Assessment

Presentations – 40%




Participation – 50%



two presentations / student
1 case study
1 research paper
course librarian
discussion
Final Project – 10%

more later
Typical class session

Case study
30 min. presentation
 15 min. discussion


Research paper
30 min. presentation
 15 min. questions

Librarian’s reports
 Group exercise

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
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
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
Example: cont’d

solution: reconceptualize business


oilfield expertise
benefits
everyone needs know-how
 expertise is always valuable

Hierarchy of knowledge




Knowledge
 expert analysis
 synthesis
 integration with experience
Information
 reports on data
 summarization
Data
 recorded information
The world
 stuff happens
Knowledge assets

Usually intangible


in worker’s heads
How to make experience explicit?
not just what?
 but also why, how, and why not?

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
Problem-solving

One of the first two areas tackled by
AI research


other is natural language
How do we solve problems?

researchers looked at logic puzzles
and problems of robot control
Rule-based reasoning

What are the steps to the solution?



Forward-chaining


reason forward from the problem
Backward-chaining


problem situation
desired result
reason backward from the desired state
Build up large rule bases

also control knowledge
Case-based reasoning
An alternative to rule-based problemsolving
 “A case-based reasoner solves new
problems by adapting solutions used
to solve old problems”
-- Riesbeck & Schank 1987

Paradox of the expert

Experts should have more rules
can solve more problems
 can be much more precise


But experts are faster than novices


who presumably have fewer rules
What does experience provide if it
isn’t just “more rules”?
Problems we solve this way

Medicine


Law



English/US law depends on precedence
case histories are consulted
Management


doctor remembers previous patients
especially for rare combinations of
symptoms
decisions are based on past experience
Financial

performance is predicted by past results
CBR Solving Problems
Solution
Retain
Adapt
Database
Retrieve
Similar
New
Problem
Review
CBR System Components

Case-base



Retrieval of relevant cases




database of previous cases (experience)
episodic memory
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)
R4 Cycle
RETRIEVE
RETAIN
find similar
problems
integrate in
case-base
CBR
REUSE
propose solutions
from retrieved cases
REVISE
adapt and repair
proposed solution
CBR Assumption

New problem can be solved by
retrieving similar problems
 adapting retrieved solutions


Similar problems have similar solutions
P
P? P
P P
S S
S X
P
P
S
PP
S
S
S
S
S
AI in Knowledge
Management

Apply the CBR model to the
organization rather than the individual
Retain the experience of the firm
 Apply it in new situations
 Do this in a consistent, automated
way

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?

CBR Knowledge Containers




Cases
Case representation language
Retrieval knowledge
Adaptation knowledge
Cases

Contents
lesson to be learned
 context in which lesson applies


Issues

case boundaries
• time, space
Case representation
language

Contents


features and values of
problem/solution
Issues
more detail / structure = flexible reuse
 less detail / structure = ease of
encoding new cases

Retrieval knowledge

Contents
features used to index cases
 relative importance of features
 what counts as “similar”


Issues

“surface” vs “deep” similarity
Nearest Neighbour Retrieval
Retrieve most similar
 k-nearest neighbour



k-NN
Example
1-NN
 5-NN

How do we measure
similarity?

Can be strictly numeric
weighted sum of similarities of
features
 “local similarities”


May involve inference

reasoning about the similarity of items
Adaptation knowledge

Contents
circumstances in which adaptation is
needed
 how to modify


Issues

role of causal knowledge
• “why the case works”
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
What this class is about
We will study examples of KM-related
CBR applications
 We will study CBR technology and
research

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
Administrativa
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 Sign up for librarian slots
