Download - CBR
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