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Case Based Reasoning. Advanced Knowledge Based Systems Module CM4023. How do we solve problems? . By knowing the steps to apply from symptoms to a plausible diagnosis But not always applying causal knowledge diseases cause symptoms symptoms do not cause diseases!

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Case based reasoning l.jpg

Case Based Reasoning

Advanced Knowledge Based Systems Module CM4023

Susan Craw

Room SAS B18a

[email protected]

http://www.comp.rgu.ac.uk/staff/smc/teaching/kbp3/


How do we solve problems l.jpg

How do we solve problems?

  • By knowing the steps to apply

    • from symptoms to a plausible diagnosis

  • But not always applying causal knowledge

    • diseases cause symptoms

    • symptoms do not cause diseases!

  • How does an expert solve problems?

    • uses same “book learning” as a novice

    • but quickly selects the right knowledge to apply

  • Heuristic knowledge (“rules of thumb”)

    • “I don’t know why this works but it does and so I’ll use it again!”

    • difficult to elicit

© The Robert Gordon University, Aberdeen


Another way we solve problems l.jpg

Another way we solve problems?

  • By remembering how we solved a similar problem in the past

  • This is Case Based Reasoning (CBR)!

    • memory-based problem-solving

    • re-using past experiences

  • Experts often find it easier to relate stories about past cases than to formulate rules

© The Robert Gordon University, Aberdeen


Problems we solve this way l.jpg

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 often based on past rulings

  • Financial

    • performance is predicted by past results

© The Robert Gordon University, Aberdeen


Cbr solving problems l.jpg

Solution

CBR Solving Problems

Review

Retain

Database

Adapt

Retrieve

Similar

New

Problem

© The Robert Gordon University, Aberdeen


Cbr system components l.jpg

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)

© The Robert Gordon University, Aberdeen


R 4 cycle l.jpg

R4 Cycle

RETRIEVE

find similar

problems

RETAIN

integrate in

case-base

CBR

REUSE

propose solutions

from retrieved cases

REVISE

adapt and repair

proposed solution

© The Robert Gordon University, Aberdeen


Applications l.jpg

Failure prediction

ultrasonic NDT of rails for Dutch railways

water in oil wells for Schlumberger

Failure analysis

Mercedes cars for DaimlerChrysler

semiconductors at National Semiconductor

Maintenance scheduling

Boeing 737 engines

TGV trains for SNCF

Planning

mission planning for US navy

route planning for DaimlerChrysler cars

Applications

© The Robert Gordon University, Aberdeen


More applications l.jpg

e-Commerce

sales support for standard products

sales support for customised products

Personalisation

TV listings from Changing Worlds

music on demand from Kirch Media

news stories via car radios for DaimlerBenz

Re-Design

gas taps for Copreci

Formulation (recipes)

rubber for racing tyres for Pirelli

tablets for AstraZeneca

More Applications

© The Robert Gordon University, Aberdeen


What s in a tablet l.jpg

provides bulk to be large enough to handle and compress (~65%)

makes it cohesive to hold together

filler

binder

enables it to come out of the die

allows rapid break down after swallowing

lubricant

disintegrant

drug

aids wetting and dissolution of drug

active ingredient (~25%)

surfactant

What’s in a Tablet?

© The Robert Gordon University, Aberdeen


Tablet formulation problem l.jpg

Tablet Formulation Problem

  • Given:

    • physical and chemical properties of a drug

    • desired dose

  • Knowing:

    • properties of available excipients

  • Goal:

    • choose 5 excipients and their quantities

    • which achieve the desired mechanical and chemical properties of the tablet

Solution

fillerDCP92.3%

binderGEL 2.1%

lubricantMGS 1.0%

disintegrantCRO 2.1%

surfactantSLS 0.3%

© The Robert Gordon University, Aberdeen


Tablet formulation knowledge l.jpg

Get-Insoluble-Filler

IF: Reqd-Filler-Solubility has value Insoluble

Filler is-on Filler-Agenda

Solubility has value Sol in Filler

Slightly-Soluble has value Slightly-Soluble

Sol < Min-Val (Slightly-Soluble)

THEN refine Filler to be Filler in Formulation

Remove-Excessive-Fillers

IF: Filler is-on Filler-Agenda

Max-Level of Filler is Level

Filler-Concentration has value Conc

Conc > Level

THEN ...

Drug Properties

Excipient Properties

Drug/Excipient Stabilities

Chemical Relationships

Physical Relationships

Heuristics

Try to balance physical

properties with stable excipients

to achieve a tablet with

viable properties

Tablet Formulation Knowledge

© The Robert Gordon University, Aberdeen


Cbr for tablet formulation l.jpg

Solution

CBR for Tablet Formulation

Review

Retain

Database

Adapt

formulations

for existing

tablets

soluble drug?

=> insoluble filler

larger dose?

=> less filler

Retrieve

Similar

Dose &

Properties

of New Drug

tablets of similar dose

whose drugs have

similar properties

© The Robert Gordon University, Aberdeen


Cbr assumption l.jpg

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

© The Robert Gordon University, Aberdeen


Why was filler x chosen l.jpg

Why was filler X chosen?

  • The tablet in the case-base whose

    • drug properties are most similar

    • dose is most similar

      is Drug-Y-50 and its filler is Z

  • However adaptation is needed

    • because of a significant difference

    • the stability of Z with the new drug is much lower

  • Adaptation proposes filler Xinstead:

    • greater stability with new drug

    • similar properties to Z

© The Robert Gordon University, Aberdeen


Cbr knowledge containers l.jpg

CBR Knowledge Containers

  • Cases

    • lesson to be learned

    • context in which lesson applies

  • Description Language

    • features and values of problem/solution

  • Retrieval Knowledge

    • featuresused to indexcases

    • relative importance of features used for similarity

  • Adaptation Knowledge

    • circumstances when adaptation is needed

    • alteration to apply

© The Robert Gordon University, Aberdeen


Corporate memory l.jpg

Corporate Memory

  • Cases from database, archive, . . .

  • Issues

    • case bias? currency? coverage?

    • description language e.g. agreement on terms

  • Case-base cannot contain allformulations

    • good coverage

    • prototypicaland exceptional cases

  • Opportunity for multiple sources

    • several expert formulators

    • shared knowledge across companies

© The Robert Gordon University, Aberdeen


Case representation l.jpg

Case Representation

  • feature-value representation

  • Problem

    • drug properties and dose

  • Solution

    • excipients and their amounts

  • Extra tablet properties

    • constrained features of resulting tablet

© The Robert Gordon University, Aberdeen


Cbr tool l.jpg

Database

Relevant Cases

Most Similar

Cases

Vote

C4.5 Index

progress of retrieval

K Nearest Neighbour

Similarity

Matching

Gshadg hjshfd

fhdjf hjkdhfs hjdshfl

hfdjsfhdjs hjdhfl hsdfhl

hd hdjsh hjsdkh

hfds hhfkfd shk

Gshadg hjshfd

fhdjf hjkdhfs hjdshfl

hfdjsfhdjs hjdhfl hsdfhl

hd hdjsh hjsdkh

hfds hhfkfd shk

Tcl for adaptation

CBR Tool

© The Robert Gordon University, Aberdeen


Nearest neighbour retrieval l.jpg

Nearest Neighbour Retrieval

  • Retrieve most similar

  • k-nearest neighbour

    • k-NN

    • like scoring in bowls or curling

  • Example

  • 1-NN

  • 5-NN

© The Robert Gordon University, Aberdeen


How do we measure similarity l.jpg

How do we measure similarity?

  • Distances between values of individual features

    • problem and case have values p and c for feature f

    • Numeric features

      • f(problem,case) = |p - c|/(max difference)

    • Symbolic features

      • f(problem,case)= 0 if p = c = 1 otherwise

  • Distance is (problem,case)

    • weighted sum of f(problem,case) for all features

  • Similarity(problem, case) = 1/(1+ (problem,case))

© The Robert Gordon University, Aberdeen


Decision trees as an index l.jpg

?

?

300

200

100

0

High

Low

Decision Trees as an Index

Solubility?

low

high

?

Dose?

<200

>200

?

© The Robert Gordon University, Aberdeen


Case retrieval l.jpg

Case Retrieval

  • Typical implementation

    • e.g.

Case-Base indexedusing a decision-tree

Cases are “stored” in the index leaves…

  • from these the most similar are retrieved using similarity matching

© The Robert Gordon University, Aberdeen


Why do we want an index l.jpg

300

200

100

0

High

Low

Why do we want an index?

  • Efficiency

    • if similarity matching is computationallyexpensive

  • Pre-selection of relevant cases

    • some features of new problem may make certain cases irrelevant . . .

    • despite being very similar

© The Robert Gordon University, Aberdeen


Case retrieval parameters l.jpg

Case Retrieval Parameters

  • Selection of features

    • inducing decision tree index

  • Weights for features

    • similarity matching

  • Parameters to induce

    • decision tree index

  • Number of best-matches

    • retrieved by similarity measure

© The Robert Gordon University, Aberdeen


Are cbr systems easy to develop l.jpg

Are CBR Systems Easy to Develop?

Review

Retain

Database

Adapt

Adaptation

Knowledge

Retrieve

Past

Cases

Similar

Not

Necessarily!

OK?

Similarity

Knowledge

© The Robert Gordon University, Aberdeen


Acquiring knowledge l.jpg

Case-base

Similarity

Matching

Index

Adaptation

CBR

System

Profiles

CBRA

Adaptation

rules

Acquiring Knowledge

Database

of previous

formulations

© The Robert Gordon University, Aberdeen


Learning l.jpg

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

© The Robert Gordon University, Aberdeen


Cbr resources l.jpg

CBR Resources

  • CBR Tools

    • ReCall (www.isoft.fr), Orenge (www.tecinno.com) Kaidara (www.kaidarausa.com)

  • CBR Websites

    • www.ai-cbr.org

    • www.aic.nrl.navy.mil/~aha/

    • www.scms.rgu.ac.uk/research/kbs/kacbd/

  • CBR Conferences

    • ICCBR’01: www.iccbr.org/iccbr01/

    • UK-CBR’01: www.ai-cbr.org/ukcbr5/

    • ECCBR 2002: www.scms.rgu.ac.uk/eccbr2002/

© The Robert Gordon University, Aberdeen


Reading l.jpg

Reading

  • Useful texts

    • (Kolodner 1993, Aamodt & Plaza 1994, Thompson 1997)

  • Our papers

    • Case-Based Design for Tablet Formulation. Craw, Wiratunga & Rowe. Proc. 4th European Workshop on CBR, p358-369, Springer, 1998.

    • Self-Optimising CBR Retrieval. Jarmulak, Craw & Rowe. Proc 12th Int Conf on Tools with AI. IEEE Press, 2000.

    • Using Case-Base Data to Learn Adaptation Knowledge for Design. Jarmulak, Craw & Rowe. Proc 17th Int Joint Conf on AI. AAAI Press, 2001.

    • Also see http://www.scms.rgu.ac.uk/research/kbs/kacbd/

© The Robert Gordon University, Aberdeen


Cbr vs rule based kbs l.jpg

CBR vs Rule-based KBS

  • Rule-based

    • a rule is generalised experience

    • applies to range of examples

    • currently do not learn as they solve problems

    • knowledge acquisition bottleneck

  • Case-based reasoning

    • cases include both prototypical cases and exceptions

    • indexing,similarity and adaptation control effectiveness

    • domain does not have an effective underlying theory

    • learning updates case-base

    • knowledge acquisition?

      • retrieval and adaptation knowledge

© The Robert Gordon University, Aberdeen


Pros cons of cbr l.jpg

Pros & Cons of CBR

  • Advantages

    • solutions are quickly proposed

      • derivation from scratch is avoided

    • domains do not need to be completely understood

    • cases useful for open-ended/ill-defined concepts

    • highlights important features

  • Disadvantages

    • old cases may be poor

    • library may be biased

    • most appropriate cases may not be retrieved

    • retrieval/adaptation knowledge still needed

© The Robert Gordon University, Aberdeen


Summary l.jpg

Summary

  • CBR Cycle

    • retrieve, reuse, revise, retain

  • Knowledge containers

    • case-base and description language

    • retrieval and adaptation knowledge

  • CBR tools to ease development of CBR systems

    • C4.5 index and k-NN retrieval

    • adaptation?

  • Knowledge acquisition

    • case knowledge can be easy

    • retrieval/adaptation knowledge may not be easy

© The Robert Gordon University, Aberdeen


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