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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

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
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
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
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
SolutionCBR Solving Problems

Review

Retain

Database

Adapt

Retrieve

Similar

New

Problem

© The Robert Gordon University, Aberdeen

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)

© The Robert Gordon University, Aberdeen

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

© The Robert Gordon University, Aberdeen

applications
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
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
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
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

filler DCP 92.3%

binder GEL 2.1%

lubricant MGS 1.0%

disintegrant CRO 2.1%

surfactant SLS 0.3%

© The Robert Gordon University, Aberdeen

tablet formulation knowledge
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
SolutionCBR 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
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
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
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
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
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
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
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
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
?

?

300

200

100

0

High

Low

Decision Trees as an Index

Solubility?

low

high

?

Dose?

<200

>200

?

© The Robert Gordon University, Aberdeen

case retrieval
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
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
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
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
Case-base

Similarity

Matching

Index

Adaptation

CBR

System

Profiles

CBRA

Adaptation

rules

Acquiring Knowledge

Database

of previous

formulations

© The Robert Gordon University, Aberdeen

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

© The Robert Gordon University, Aberdeen

cbr resources
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
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
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
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
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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