Case Based Reasoning
Lecture 5: Reuse, Adaptation and Retention
Outline
Re-use How to re-use retrieved solutions
Adaptation Why might we want to revise the solution? Types of adaptation
Retention Why might we wish to retain cases?
Re-Using Retrieved Solutions
Single retrieved solution Re-use this solution
Multiple retrieved solutions Vote/average of retrieved solutions
Weighted according to Ranking Similarity
Iterative retrieval Solve components of the solution one at a
time
Multiple Retrievals
Whole solution generated in single retrieval
Single components generated in each retrieval Parallel Incremental
? ?
Problem
SuggestedSolution
?
Subproblem
?
Subproblem
Suggested Part Solution
SuggestedPart Solution
? ?
Subproblem
SuggestedPart Solution
?
Subproblem
SuggestedSolution
When is Adaptation Needed?
Classification All solutions likely to be
represented in case-base Adaptation corrects for lack
of cases Constructive problem
solving All “designs” unlikely to be
represented in case-base Retrieved cases suggest
initial “design” Adaptation alters the
“design” to reflect novel feature values
Redesign of Gas-taps (Copreci, Spain)
Assumptions for Adaptation
Similar problems have similar solutions
The effort required to adapt a retrieved solution will be less the more similar it is to the required solution
How to Adapt the Solution
Adaptation alters proposed solution takes account of differences between new and
retrieved problems Null adaptation - copy retrieved solution
Used by CBR-Lite systems Manual or interactive adaptation
User adapts the retrieved solution (Adapting is easier than solving?)
Automated adaptation CBR system is able to adapt the retrieved solution Adaptation knowledge required
Automated Adaptation Methods
Substitution change some part(s) of the retrieved solution simplest and most common form of adaptation
Transformation alters the structure of the solution
Generative replays the method of deriving the retrieved
solution on the new problem method of solution is part of retrieved case most complex form of adaptation
Examples of Adaptation
CHEF CBR system to plan Szechuan recipes
Hammond (1990) Substitution adaptation
substitute ingredients in the retrieved recipe to match the menu
Retrieved recipe contains beef and broccoli New menu requires chicken and snowpeas Replace chicken for beef, snowpeas for broccoli
Transformation adaptation Add, change or remove steps in the recipe
Skinning step added for chicken, not done for beef
Examples of Adaptation
Car diagnosis example Symptoms, faults and repairs for brake lights are
analogous to those for headlight Substitution: brake light fuse
Planning example Train journeys and flights are analogous Transformation: flights need check-in step added
Adaptation in CBR-Works
Provides adaptation rules IF a THEN b classic production rules
Example Add £1000 to the price of a new car for a
different colour
Recalculate price for new colour
?? Query::Colour isRegular?? Retrieved::Colour isRegular?? Query::Colour<>Retrieved::Colour?? ?OldPrice := Retrieved::Price?? ?OldPrice be_of_type Integer?? ?NewPrice := ?OldPrice + 1000?? ?NewPrice be_of_type Integer!! Result::Price := ?NewPrice!! Result::Colour := Query::Colour
Conditions
Actions
Adaptation in CBR-Works: Example
Retrieval without adaptation
Adaptation in CBR-Works: Example
Retrieval + adaptation
Predicting value of the price attribute
Adaptation in CBR-Works: Example
Adaptation rule to predict the value of Price
Other Rules in CBR-Works
CBR-Works also uses completion rules to calculate a dependent attribute value set default value alter the feature weights in certain circumstances
Used to complete a query fill-in missing data during case creation alter similarity calculations for retrieval
Two Schools of Thought in CBR
Adaptation is the most contentious issue in CBR One group believes adaptation is not important
The problem cannot be solved using CBR A CBR system without adaptation capabilities is
called CBR Retrieval System Others believe it is vital
Without adaptation and generation of new solutions there is no reasoning in CBR
A CBR system with adaptation capabilities is called fully-fledged CBR system
Retention
What can be learned New experience to be retained as new case Representing the new case
Contents of new case Indexing of new case
Forgetting cases For efficiency or because out of date Deleting an old case
Old is not necessarily bad Does it leave a gap?
Example
Outlook=Cloudy Temp.=Cool Humidity=High Windy=False Play= Yes
Outlook=Cloudy Temp.=Mild Humidity=High Windy=False Play= No
Do we need to retain the new case? Do we need to rebuild the decision tree index?
outlook
Yes
sunnycloudy
rainy
humidity
No Yes
high normal
windy
No Yes
true false
Summary
Reuse Initial solution from retrieved cases
Revise Adapt initial solution to reflect differences
between new and retrieved problems CBR-Works adaptation rules
Retain When to retain and whether to replace Representation and indexing
Reading
Research Papers S. Craw, J. Jarmulak & R. Rowe. Learning and
Applying Case-Based Adaptation Knowledge. Proceedings 4th ICCBR Conference, p131-145, 2001. www.comp.rgu.ac.uk/staff/smc/papers/iccbr01smc.pdf
B. Smyth & M. T. Keane. Adaptation-Guided Retrieval: Questioning the similarity assumption. Artificial Intelligence 102:249-293, 1998. www.cs.ucd.ie/staff/mkeane/SmythKeane98.pdf