Case Based Reasoning

Outline

The Limitations of Rules

Solving Problems

Case Based Reasoning

Applications

Reading

The Limitations of Rules

The success of rule-based expert systems is due to several factors: They can mimic some human problem-solving

strategies Rules are a part of everyday life, so people can

relate to them However, a significant limitation is the knowledge

elicitation bottleneck Experts may be unable to articulate their expertise

Heuristic knowledge is particularly difficult Experts may be too busy…

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

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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

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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

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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

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Retain Review

Adapt

Retrieve

Database

NewProblem

Similar

SolutionSolution

CBR Solving Problems

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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)

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R4 Cycle

REUSEREUSEpropose solutions from retrieved cases

REVISEREVISEadapt and repair

proposed solution

CBRCBR

RETAINRETAINintegrate in

case-base

RETRIEVERETRIEVEfind similar problems

Elephants Never Forget!

Some biologists suggest that elephants’ success in harsh environments may be due to their memories.

A herd of elephants retains a collective memory of problems and their solutions: E.g., they remember where water can usually

be found during a drought. Elephants can solve problems without using

models or rules.

Databases

Database technology would seem ideally suited to the task of retrieving known solutions to problems

Databases are excellent at finding exact matches…

But are poor at near or fuzzy matches

I’ve got the Answer What’s the Question?

The CBR Cycle

SolutionSolutionReview Retain

Adapt

RetrieveSimilar

NewProblem

R4 Cycle

Retrieve the cases from the case-base whose problem is most similar to the new problem.

Reuse the solutions from the retrieved cases to create a proposed solution for the new problem.

Revise the proposed solution to take account of the problem differences between the new problem and the problems in the retrieved cases.

Retain the new problem and its revised solution as a new case for the case-base if appropriate.

Definitions of CBR

Case-based reasoning is […] reasoning by remembering

A case-based reasoner solves new problems by adapting solutions that were used to solve old problems

Case-based reasoning is a recent approach to problem solving and learning […]

Leake, 1996

Riesbeck & Schank, 1989

Aamodt & Plaza, 1994

CBR Assumption(s)

The main assumption is that: Similar problems have similar solutions:

e.g., an aspirin can be taken for any mild pain

Two other assumptions: The world is a regular place: what holds true

today will probably hold true tomorrow (e.g., if you have a headache, you take aspirin,

because it has always helped) Situations repeat: if they do not, there is no

point in remembering them (e.g., it helps to remember how you found a

parking space near that restaurant)

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

Good / Bad Applications for CBR

Classification tasks (good for CBR) Diagnosis - what type of fault is this? Prediction / estimation - what happened

when we saw this pattern before? Synthesis tasks (harder for CBR)

Engineering Design Planning Scheduling

Success Stories for CBR

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

Success Stories for CBR

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 colouring plastics for

General Electric tablets for AstraZeneca

Impact on Business @ Microsoft

Within 9 months of introducing a CBR system @ Microsoft’s call centre in Glasgow

Microsoft reported: 10% increase in customer satisfaction rating 28% increase in “first-time-fix” success rate 13% increase in the “agent is informed” customer

survey score A significant reduction in the time required to train

new agents More consistent responses delivered by agents,

regardless of the problem

CBR Honours Project Ideas

CBR for email filtering (anti-SPAM) Michael Long, BSc(Hons) 2004, SPAM filtering Amandine Orecchioni, 2005, Email Management

CBR for Diagnosis Katya Ponce do Leon, MSc 2005, Fish Diagnosis for Marine

Lab Grant Gauld, BSc(Hons) 2005, CBR Helpdesk for Chevron-

Texaco

CBR for Planning Abhishek Chakraborty, MSc 2005, CBR Healthcare Planning

for Partners Research Emergency Nutrition Scott Morrice, BSc(Hons) 2004, “Killer Bunnies” game

If you are interested in a CBR project next year see me or Nirmalie Wiratunga

Reading

Article Tenth anniversary of the plastics color formulation tool,

William Cheetham, AI Magazine, Vol 26, Fall, 2005. www.aaai.org/Library/Magazine/Vol26/vol26.html www.findarticles.com/p/articles/mi_m2483/is_3_26/ai_n15691555

Books I. Watson. Applying Knowledge Management:

Techniques For Building Corporate Memories. Morgan Kaufmann, 2003.

I. Watson. Applying Case-Based Reasoning: Techniques for Enterprise Systems. Morgan Kaufmann, 1997.

CBR Resources

CBR on the web http://groups.yahoo.com/group/case-based-reasoning/

CBR Commercial Solutions Orenge from www.empolis.com Kaidara Adviser from (www.kaidara.com) eGain (www.egain.com)

Customer Service & Contact Centre Software CBR Tools in our School

CBR-Works from www.empolis.com ReCall from www.isoft.fr Weka from www.cs.waikato.ac.nz