Ai in knowledge management
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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

Solution

CBR 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


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