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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

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ai in knowledge management

AI in Knowledge Management

Professor Robin Burke

CSC 594

outline
Outline
  • Introduction to the class
  • Overview
    • Knowledge management
    • AI
    • Case-based reasoning
objectives
Objectives
  • Content
    • Explore AI applications in knowledge management
      • specifically case-based reasoning
  • Skills
    • Reading research literature
    • Building an informal knowledge base
course design
Course design
  • Seminar format
    • student presentations
    • in-class exercises
  • Attendance VERY IMPORTANT!
  • Reading VERY IMPORTANT!
reading
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
Assessment
  • Presentations – 40%
    • two presentations / student
    • 1 case study
    • 1 research paper
  • Participation – 50%
    • course librarian
    • discussion
  • Final Project – 10%
    • more later
typical class session
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
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
  • 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
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
Example: cont’d
  • solution: reconceptualize business
    • oilfield expertise
  • benefits
    • everyone needs know-how
    • expertise is always valuable
hierarchy of knowledge
Hierarchy of knowledge
  • Knowledge
    • expert analysis
    • synthesis
    • integration with experience
  • Information
    • reports on data
    • summarization
  • Data
    • recorded information
  • The world
    • stuff happens
knowledge assets
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
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
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
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
case based reasoning
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

paradox of the expert
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
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
cbr solving problems
SolutionCBR Solving Problems

Review

Retain

Database

Adapt

Retrieve

Similar

New

Problem

cbr system components
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)
r 4 cycle
R4 Cycle

RETRIEVE

find similar

problems

RETAIN

integrate in

case-base

CBR

REUSE

propose solutions

from retrieved cases

REVISE

adapt and repair

proposed solution

cbr assumption
P

P

P

P

P

P

P

P

P

S

S

S

S

S

S

S

S

S

CBR Assumption
  • New problem can be solved by
    • retrieving similar problems
    • adapting retrieved solutions
  • Similar problems have similar solutions

?

X

ai in knowledge management1
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
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
CBR Knowledge Containers
  • Cases
  • Case representation language
  • Retrieval knowledge
  • Adaptation knowledge
cases
Cases
  • Contents
    • lesson to be learned
    • context in which lesson applies
  • Issues
    • case boundaries
      • time, space
case representation language
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
Retrieval knowledge
  • Contents
    • features used to index cases
    • relative importance of features
    • what counts as “similar”
  • Issues
    • “surface” vs “deep” similarity
nearest neighbour retrieval
Nearest Neighbour Retrieval
  • Retrieve most similar
  • k-nearest neighbour
    • k-NN
  • Example
  • 1-NN
  • 5-NN
how do we measure similarity
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
Adaptation knowledge
  • Contents
    • circumstances in which adaptation is needed
    • how to modify
  • Issues
    • role of causal knowledge
      • “why the case works”
learning
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
What this class is about
  • We will study examples of KM-related CBR applications
  • We will study CBR technology and research
next week
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
Administrativa
  • Sign up for presentations
  • Sign up for librarian slots
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