learning by near-miss an example of using & coding knowledge

preamble... learns “concept models” real objects/events/etc coded as Kn (following example uses tuples) model is refined using examples +ve examples relax/generalise -ve examples restrict/specialise

1.initial example (isa x block) (isa y block) (isa z block) (supports y x) (supports z x) (pos x horis) (pos y vert ) (pos z vert )

2.-ve example difference (supports y x) (supports z x) changes (imp supports y x) (imp supports z x)

3.another -ve example differences (pos x horis) (not touches y z) changes (imp pos x horis) note use of... 'general Kn‘ most important diff.s

4.& another -ve example differences (not touches y z) changes (imp not touches y z)

5.a +ve example differences (isa x wedge) changes (isa x (wedge block))

the refined description original (isa x block) (isa y block) (isa z block) (supports y x) (supports z x) (pos x horis) (pos y vert ) (pos z vert ) refined (isa x (wedge block)) (isa y block) (isa z block) (imp supports y x) (imp supports z x) (imp pos x horis) (pos y vert ) (pos z vert ) (imp not touches y z)

the process compare new & old descriptions if +ve example generalise express diffs in terms of new select most sig diffs extend old by diff list else if –ve example specialise express diffs in terms of old select most sig diffs enforce old by diff list

comparing representations

simple approaches:try all matches better approaches:best 1 st search

using best 1 st search start with “open” labelling add new label with each successor state rank diffs to generate “diff score” explore state with min diff score