Case Adaptation

Sources:–Chapter 8–www.iiia.csic.es/People/enric/AICom.html–www.ai-cbr.org

Adaptation

Adaptation

New problem

Selected case

Adaptation knowledge

Solution

Classes of Adaptation• No-adaptation

• Transformational Analogy

Substitution Adaptation Feedback based constraint based

Compositional adaptation

• Generative Solution Adaptation

Transformational AnalogyDerivational Analogy

No Adaptation • For classification/diagnosis tasks

• If C is the NN for a new problem P then class(P) class(C)

• If {C1,…,Ck} are the k-NN for a new problem P thenClass(C) F({C1,…,Ck})

For example: F({C1,…,Ck}) = “majority class among {C1,…,Ck}”

Substitution Adaptation• Let C = (P,S); A problem P and a solution S

• Adaptation problem:

Given

A problem P’ A case C such that P is similar to P’

Search a substitution such that (S) solves P’

corresponds to an application of a rule transforming parts of the case (so it is not a substitution in the traditional sense)

Example

Support for PC sale:

• Cases are configuration episodes of PCs

• User specifies his/hers requirements

• System selects best PC (e.g., using CCBR) and change some components

Example rules (Substitutional Adaptation):

If (query.application = ‘database’ and case.diskSpace < 2GB) then target.diskSpace 4GB

Example (2)

Example rules (Substitutional Adaptation):

If (query.application = ‘games’ and case.application ‘games’) then AddObject target.addJoystick AddObject target.addSoundCard

Other rules to configure joystick and sound

Substitutional Feedback-basedCar type: sportColor: redSeating: 2Valves: 48Type: 5.7L

Model name: name1Price: 200,000 Year: 2003

Feedback: not successfulCause: price is too high

Car type: sportColor: redSeating: 2Valves: 48Type: 5.7L

Model name: name1Price: 200,000 Year: 2003

Feedback: successful

Car type: sportColor: redSeating: 2Valves: 40Type: 3.6L

Model name: name 2Price: 150,000 Year: 2000

Feedback: successful

AdaptCaseC (adapted)

CaseA (new) CaseB (old)

Retrieve

Copy

Rule:if price is too highthen model previous model

Substitutional Constraint-based

Case ID: 123Speed: highPrice: middleUsage: sportAntitheft performance: high

Model Name: Toyota Sedan 07Price: 10,500Antitheft system: Product A

Case ID: 456Speed: highPrice: middleUsage: sportAntitheft performance: middle

Model Name: Toyota Sedan 07Price: 10,500Antitheft system: Product A

Case ID: 123Speed: highPrice: middleUsage: sportAntitheft performance: high

Model Name: Toyota Sedan 07+Price: 11,000Antitheft system: Product B

CaseA (new) CaseB (old)

Retrieve

Copy

adapt

CaseC (adapted) Rule:if need higher Antitheft performance and Antitheft System = product Athen Antitheft System product B Price Price + 500

Compositional Adaptation

• Let C = (P,S); A problem P and a solution S

• Adaptation problem:

Given

A problem P’ A case C such that P is similar to P’

Search a sequence of substitutions 1, …, n such that:

S’ is a solution for P’

(P,C) … (P’,S’)1 2 n

Adaptation Operators (2)

Uses rule-based systems during adaptation

Roles of operators/rules:

• General knowledge about the domain

•

(P,C) … (P’,S’)1 2 n

Adaptation knowledge