Epistemological Framework for Reuse

1
Epistemological Framework for Reuse
Generic Task
Problem Solving Method
Task Ontology
Method Ontology
Mapping Knowledge
Application-specific Problem-Solving Knowledge
Mapping Ontology
Application Configuration
Multi-Functional Domain
Application Model
Domain Ontology
2
A Library of Components for Classification
Problem Solving
3
Main Goals

• To carry out a knowledge-level analysis of
  classification
• To develop a practical resource to support the
  development of classification applications
• To provide a concrete set of components to act as
  a test case for IBROW brokering system and IRS

4
Classification

• Classification can be seen as the problem of
  finding the solution (class), which best explains
  a set of known facts (observables), according to
  some criterion

Observables
Candidate Sols.
Classification
Solution
Solution Criterion
5
Example
Observables
backgroundgreen areachina...
chinese-granny, dutch-granny, etc..
Classification
Candidate Sols.
Solution
chinese-granny
Criterion
Complete-coverage-criterion (every observable has
to be explained)
6
Observables

• Observables set_of (Observable)
• Observable feature, value.
• Well defined Observables (obs)
• (f1, v1 ? obs ? f1, v2 ? obs) -gt v1 v2
• (f1, v1 ? obs) -gt legal_feature_value (f1, v1 )

7
Solutions

• Solution set_of (Feature_Spec)
• Feature_Spec Feature, Feature_value_spec
• Feature_value_spec Unary_Relation
• Well defined Solution (sol)
• f1, s1 ? sol ? holds (s1, v1 ) -gt
  legal_feature_value (f1, v1 )

8
Matching

• Observablef1, v1 matches Solutionsol iff
• f1, c ? sol ? holds (c, v1 )

9
Matching Sets of Obs to a Solution

• Sol fsol1, c1...fsolm, cm Obs fob1,
  v1...fobn, vn
• Four possible cases
• fj, cj ? sol ? fj, vj ? obs ? holds (cj, vj)
  -gt Explained (fj)
• fj, cj ? sol ? fj, vj ? obs ? not holds (cj,
  vj) -gt Inconsistent(fj)
• fj, vj ? obs ? fj, cj ? sol -gt Unexplained
  (fj)
• fj, vj ? obs ? fj, cj ? sol -gt Missing (fj)

10
Default Match Criterion

• Match Score
• Vector ltI, E, U, Mgt
• Match Comparison Relation
• S1 (i1, e1, u1, m1) S2 (i2, e2, u2, m2)
• S1 better_score than S2 iff
• (i1 lt i2) ?
• (i2 i1 ? e2 lt e1) ?
• (i2 i1 ? e2 e1 ? u1 lt u2) ?
• (i2 i1 ? e2 e1 ? u2 u1 ? m1 lt m2)

11
Other Match Criteria

• SUBSET-BASED-MATCH-CRITERION
• Uses subset instead of lt to determine best match
• ABSTRACTION-AWARE-MATCH-CRITERION
• Matching mechanism able to handle both
  observables and abstracted data
• EXPLANATION-CENTRED-MATCH-CRITERION
• Focuses on explanation power
• EQUAL-RATING-MATCH-CRITERION
• Very stupid one, which gives every solution the
  same score

12
Possible Solution Criteria

• Positive Coverage
• Some feature is explained and none is
  inconsistent
• Complete Coverage
• All features are explained and none is
  inconsistent
• No missing features
• No inconsistent features and no missing features

13
Hierarchy of Criteria
Match Criterion
Match Score Mechanism
Match Score Comparison Rel
Macro Score Mechanism
Feature Score Mechanism
14
Observables

• (def-class observables (set) ?obs
• "This is simply a set of observables.
• An important constraint is that there cannot
  be two values for the same feature
• in a set of observables"
• iff-def (every ?obs observable)
• constraint (not (exists (?ob1 ?ob2)
• (and (member ?ob1
  ?obs)
• (member ?ob2
  ?obs)
• 
  (has-observable-feature ?ob1 ?f)
• 
  (has-observable-feature ?ob2 ?f)
• 
  (has-observable-value ?ob1 ?v1)
• 
  (has-observable-value ?ob2 ?v2)
• (not ( ?v1
  ?v2))))))

15
Solutions

• (def-class solution () ?x
• "A solution is a set of feature definitions"
• iff-def (every ?x feature-definition))
• (def-class feature-definition () ?x
• ((has-feature-name type feature)
• (has-feature-value-spec type unary-relation))
• constraint (gt (and (has-feature-name ?x ?f)
• (has-feature-value-spec ?x
  ?spec))
• (gt (holds ?spec ?v)
• (legal-feature-value ?f
  ?v))))

16
Solution Criterion

• (def-class solution-admissibility-criterion () ?c
• ((applies-to-match-score-type type
  match-score-type)
• (has-solution-admissibility-relation type
  unary-relation))
• constraint (gt (and (solution-admissibility-crit
  erion ?c)
• (has-solution-admissibility-
  relation ?c ?r)
• (domain ?r ?d))
• (subclass-of ?d match-score)))

17
Monotonicity of Admissibile Solutions

• (def-axiom admissibility-is-monotonic
• "This axiom states that the admissibility
  criterion is monotonic. That is, if a solution,
  ?sol, is admissible, then any solution which is
  better than ?sol will also be admissible"
• (forall (?sol1 ?sol2 ?obs ?criterion)
• (gt (and (admissible-solution
• ?sol1 (apply-match-criterion
  ?criterion ?obs ?sol1)
  ?criterion)
• (better-match-than ?sol2 ?sol1
  ?obs ?criterion))
• (admissible-solution
• ?sol2 (apply-match-criterion
  ?criterion ?obs ?sol2)
  ?criterion))))

18
Complete Coverage

• (def-instance complete-coverage-admissibility-crit
  erion
• solution-admissibility-criterion
• ((applies-to-match-score-type default-match-score
  )
• (has-solution-admissibility-relation
• complete-coverage-admissibility-relation)))
• (def-relation complete-coverage-admissibility-rela
  tion (?score)
• "a solution should be consistent and explain
  all features"
• constraint (default-match-score ?score)
• iff-def (and ( (length (first ?score)) 0)
  no inconsistency
• ( (length (third ?score)) 0)))
  no unexplained

19
Classification Task Ontology

• 70 Definitions
• Provides both a theory of classification and a
  vocabulary to describe classification problems
• Ontology is separated from task specifications

20
Generic Classification Task

• Input roles
• Candidate Solutions, Match Criterion, Solution
  Criterion, Observables
• Precondition
• Both observables and candidate solutions have to
  be provided
• Goal
• To find a solution from the candidate solutions
  which is admissible with respect to the given
  observables, solution criterion and match
  criterion

21

• (def-class classification-task (goal-specification
  -task) ?task
• "Classification is defined here as finding one
  or more admissible solutions
• out of a predefined solution space, which
  explain the features of a
• given set of observables, in accordance with a
  given match criterion and
• solution admissibility criterion.
• Because different variants of the goal can be
  formulated, the goal
• of the task is given here only as a default"
• ((has-input-role value has-candidate-solutions
• value has-observables
• value has-match-criterion
• value has-solution-admissibili
  ty-criterion)
• (has-output-role value has-solutions)
• (has-candidate-solutions type solution-space)
• (has-observables type observables)
• (has-match-criterion type match-criterion
• default-value
  default-match-criterion)
• (has-solution-admissibility-criterion
• type solution-admissibility-criterion
• default-value default-solution-admissibility-
  criterion)

22

• (has-precondition
• value (kappa (?task)
• (exists (?x ?y)
• (and (member ?x
  (role-value ?task 'has-observables))
• (member ?y
  (role-value
  ?task 'has-candidate-solutions))))))
• (has-goal-expression
• default-value
• (kappa (?task ?sols)
• (forall ?sol
• (gt (member
• ?sol
• (role-value ?task
  'has-solutions))
• (admissible-solution
• ?sol
• (apply-match-criterion
• (role-value ?task
  'has-match-criterion)
• (role-value ?task
  'has-observables)
• ?sol)
• (role-value

23
Specific Classification Tasks

• Single-Solution Classification Task
• Single-solution assumption
• Optimal Classification Tasks
• Goal requires optimality

24
Problem Solving Library

• Based on heuristic classification model
• Includes both data-directed and solution-directed
  methods
• Supported by a method ontology

25
Method Ontology Main Concepts

• Abstractors
• Mechanism for performing abstraction on
  observables
• Abstractor Obs -gt Obs
• Refiners
• Mechanism for specialising a solution
• Refiner Sol -gt Sol
• Candidate Exclusion Criterion
• A criterion which is used to decide when a search
  path is a dead-end
• Default criterion rules out inconsistent solutions

26
Monotonicity of Exclusion Criterion

• (def-axiom exclusion-is-monotonic
• (forall (?sol1 ?sol2 ?obs ?criterion)
• (gt (and (ruled-out-solution
• ?sol1 (the-match-score ?sol1)
  ?criterion)
• (not (better-match-than ?sol2
  ?sol1 ?obs
• 
  ?criterion)))
• (ruled-out-solution
• ?sol2 (the-match-score
  ?sol2)?criterion))))

27
Axiom of Congruence

• (def-axiom CONGRUENT-ADMISSIBILITY-AND-EXCLUSION-C
  RITERIA
• (forall (?sol ?task)
• (gt (member ?sol (the-solution-space
  ?task))
• (not (and (admissible-solution
• ?sol
• (the-match-score ?sol)
• (role-value
• ?task
• 'has-solution-admissibil
  ity-criterion))
• (ruled-out-solution
• ?sol
• (the-match-score ?sol)
• (role-value
• ?psm
  'has-solution-exclusion-criterion)))))))

28
Three Heuristic Classification PSMs

• Two Data-directed
• Admissible Solution Classifier
• Finds one admissible solution according to the
  given criteria
• Uses backtracking hill climbing
• Optimal Classifier
• Performs complete search looking for optimal
  solution
• Uses best-first strategy
• Uses candidate exclusion criterion to prune
  search space
• One Solution-directed
• Goes down the solution hierarchy, acquiring
  observables as needed
• Ask for observables with max discrimination power

29
Four Assumptions in Main PSMs

• No cycles in abstraction hierarchy
• No cycles in refinement hierarchy
• At least one class in the solution space is an
  admissible solution
• The solution refinement hierarchy is consistent
  with the candidate exclusion criterion. That is
  if sol is ruled out, all refinements of sol can
  also be ruled out

30
Task-Method Hierarchy
31
Example

• Apple Domain
• Originally developed in Amsterdam
• Solutions Apple Types granny, noble,
  delicious...
• Hierarchy of Apple Types
• Features bkg-colour, fg-colour, rusty....
• Pretty trivial really!

32
(No Transcript)
33
Mapping Solutions and Obs to Apples

• (def-relation-mapping solution up
• ((solution ?x)
• if
• (or ( ?x apple)
• (subclass-of ?x apple))))
• (def-relation-mapping observable up
• ((observable ?x)
• if
• (or ( ?X (?f ?v ?obs))
• ( ?x (?f ?v)))))

34
More Relation Mappings

• (def-relation-mapping has-observable-feature up
• ((has-observable-feature ?x ?f)
• if
• (or ( ?X (?f ?v ?obs)) ( ?x (?f ?v)))))
• (def-relation-mapping has-observable-value up
• ((has-observable-value ?x ?v)
• if
• (or ( ?X (?f ?v ?obs)) ( ?x (?f ?v)))))
• (def-relation-mapping directly-abstracts-from up
• ((directly-abstracts-from ?ob ?obs)
• if
• ( ?ob (?f ?v ?obs))))

35
Sample Abstractor

• (def-instance sugar-abstractor abstractor
• ((has-body '(lambda (?obs)
• (in-environment
• ((?v . (observables-feature-value
  ?obs 'sugar)))
• (cond ((gt ?v 70)
• (list-of 'sweet-level
  'high
• (list-of
  (list-of 'sugar ?v))))
• ((and (lt ?v 70) (gt ?v 40))
• (list-of 'sweet-level
  'medium
• (list-of
  (list-of 'sugar ?v))))
• ((lt ?v 40)
• (list-of 'sweet-level
  'low
• (list-of
  (list-of 'sugar ?v))))))))
• (applicability-condition
• (kappa (?obs) (member 'sugar
• (all-features-in-observa
  bles
• ?obs))))))

36
Generic (reusable) Refiner

• (def-instance refinement-through-subclass-of-links
  refiner
• "If the solution space is specified by means of
  classes arranged in a subclass-of hierarchy, then
  this is a good refiner to use"
• ((has-body '(lambda (?sol)
• (setofall ?sub (direct-subclass-of
  
• ?sub ?sol))))
• (applicability-condition
• (kappa (?sol) (and (class ?sol)
• (exists ?sub
• (direct-subclass-of ?sub
  ?sol)))))))

37
Evaluation/Results

• Library provides an analytical tool to understand
  classification problem solving
• Library tested on a number of domains
• ECAI Paper classification
• Paleontology
• Medical Diagnosis
• No changes the organization of library needed
• Only adding new components, e.g. new match
  criteria
• Used by van Harmelen and Ten Teije to study
  automatic PSM adaptation

38
(No Transcript)