1. learning by near-missan example of using & codingknowledge

2. 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

3. 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 )

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

5. 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

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

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

8. 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 refined description

9. 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

10. comparing representations

11. comparing representations • simple approaches: try all matches • better approaches: best 1st search

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