Case Based Reasoning Lecture 5: Reuse, Adaptation and Retention.

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