# Case Based Reasoning Lecture 1: Introduction Professor Susan Craw

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Case Based Reasoning Lecture 1: Introduction Professor Susan Craw
B18a (via secretary) Lecture/Lab Notes available on the Virtual Campus and

Outline The Limitations of Rules Solving Problems Case Based Reasoning

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

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 Solution Review Retain Adapt Database Retrieve Similar
New Problem

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: Two other assumptions:
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

Within 9 months of introducing a CBR 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 filtering (anti-SPAM) Michael Long, BSc(Hons) 2004, SPAM filtering Amandine Orecchioni, 2005, 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