Présentation au forum des sciences cognitives et du TAL, Nancy, 17/11/2021

1. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
RECENTRER L'INTELLIGENCE
ARTIFICIELLE SUR LES
CONNAISSANCES
Mathieu d’Aquin - @mdaquin
Professeur d’informatique
Université de Lorraine, LORIA/IDMC

2. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Avertissement
Cette présentation contient des éléments de travaux en cours
non publiés

3. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51

4. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Blackbird https://www.itma.ie/digital-library/image/blackbird-john-obrien-ms

5. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Blackbird https://www.itma.ie/digital-library/score/padraig-okeeffe-ms-bk-3-10

6. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Détecter la tonalité
1 3 6 8 10
0 2 4 5 7 9 11

7. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Blackbird
Ré 4 7 5 4 0 7 0 4 0 0 10 7 4 5 7 4 5 4 0 2 4
Sol 9 0 10 9 5 0 5 9 5 5 3 0 9 10 0 9 10 9 5 7 9

8. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Algorithmes et heuristiques
Précisions, sur un jeu de données de plus de 1200 mélodies annotées
manuellement par Danny:
KS Algorithme de Krumhansl-Shmuckler avec des poids par défaut 73.2%
SW KS avec des poids adaptés 75.57%
AE KS avec les poids de Aarden/Essen 73.2%
BB KS avec les poids de Bellman Budge 72.55%
TKP KS avec les poids de Temperly/Kostka/Payne 78.19%
AT Comme indiqué dans la transcription 86.27%
FiN La note final 69.85%
FrN La note la plus fréquente 41.5%

9. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Algorithmes et heuristiques
Précisions, sur un jeu de données de plus de 1200 mélodies annotées
manuellement par Danny:
KS Algorithme de Krumhansl-Shmuckler avec des poids par défaut 73.2%
SW KS avec des poids adaptés 75.57%
AE KS avec les poids de Aarden/Essen 73.2%
BB KS avec les poids de Bellman Budge 72.55%
TKP KS avec les poids de Temperly/Kostka/Payne 78.19%
AT Comme indiqué dans la transcription 86.27%
FiN La note final 69.85%
FrN La note la plus fréquente 41.5%

10. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
L’apprentissage automatique à la rescousse !
KS
SW
AE
BB
TKP
AT
FiN
FrN
Apprentissage
par arbre de
décision
Tonalités manuellement trouvées
Tonalités apprises
Précision : ~89%

11. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Valeurs importantes
KS SW AE BB TKP AT FiN FrN FAN

12. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51

13. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
116 règles, par exemple :
1 if TKP <= 3.0 and AT > 8.0 and AE <= 1.0 and SW <= 3.5

14. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Avec la programmation génétique
9 règles pour une précision de ~90%
AT if SW < max(FiN,FAN-AT)
AT if AT > 9
4 if BB == 9
FiN if min(max(min(11,BB),11),SW) == min(max(AE,5),FiN)
FiN if FrN == 0
min(AE,2) if KS == 9
TKP if max(min(11,min(max(min(7,KS),FAN),AE)),FiN) == 7
max(AT,min(max(2,max(4,FiN)),min(max(min(AT,5),9),KS))) if AT < 5
TKP if max(max(2,AT),FiN) == 7

15. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Modéliser les connaissances (a.k.a GOFAI)
Système à base de
connaissances
Base de
connaissances
Entrée
Nouvelles
connaissances,
classification,
recommandation, etc.

16. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Modéliser les connaissances (a.k.a GOFAI)
Système à base de
connaissances
Base de
connaissances
Entrée
Nouvelles
connaissances,
classification,
recommandation, etc.
Règles, logiques, ontologies, etc.

17. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Modéliser les connaissances (a.k.a GOFAI)
Système à base de
connaissances
Base de
connaissances
Entrée
Nouvelles
connaissances,
classification,
recommandation, etc.
Règles, logiques, ontologies, etc.
Déduction, induction, analogie, etc.

18. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Des milliers de graphes de
connaissances et d’ontologies
dans des domaines plus ou
moins spécifiques.
Le Web Sémantique à
la rescousse !

19. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Des milliers de graphes de
connaissances et d’ontologies
dans des domaines plus ou
moins spécifiques.
Le Web Sémantique à
la rescousse !
Gene
Ontology
FMA
Ontology
LODE
BIBO
Geo
Ontology
DBPedia
Ontology
Dublin
Core
FOAF
DOAP
SIOC
Music
Ontology
Media
Ontology
rNews

20. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
DBpedia.org wikidata.org

21. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
google

22. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
google

23. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Exemple : dans l’enseignement
supérieur

24. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Modéliser les connaissances (a.k.a GOFAI)
Système à base de
connaissances
Base de
connaissances
Entrée
Nouvelles
connaissances,
classification,
recommandation, etc.
Règles, logiques, ontologies, etc.
Déduction, induction, analogie, etc.

25. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Réutiliser les connaissances
Système à base de
connaissances
Entrée
Nouvelles
connaissances,
classification,
recommandation, etc.
Graphes de connaissances,
ontologies
Déduction, induction, analogie, etc.

26. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Utiliser les connaissances dans l’analyse de données
Les hommes sont plus
éduqués que les femmes
Les femmes sont plus
éduquées que les hommes
Egalité

27. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Utiliser les connaissances dans l’analyse de données
Pays où les hommes sont plus éduqués
skos:exactMatch - dbp:hdiRank ≥ 126 87.8%
skos:exactMatch - dc:subject
db:Category:Least_Developed_Countries
74.7%
skos:exactMatch - dbp:gdpPPPPerCapitaRank ≥ 89 68.3%
Pays où les femmes sont plus éduquées
skos:exactMatch - dbp:hdiRank ≤ 119 63.4%
skos:exactMatch - dbp:gdpPPPPerCapitaRank ≤ 56 62.3%
Pays où les hommes et le femmes ont le même niveau d’éducation
skos:exactMatch - dbp:gdpPPPRank ≥ 64 62.0%
skos:exactMatch - dbp:gdpPPPPerCapitaRank ≥ 29 61.0%
Somalia Ethiopia India
db:Somalia db:Ethiopia db:India
Graphe de connaissance DBpedia
skos:exactMatch
600/pp Africa 1200/pp 3800/pp SouthAsia
gdp
gdp gdp subject
subject
4000/pp
Least_Developped_
Countries
< subject

28. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Pourquoi autant de recherches sur
“A song of ice and fire” ?

29. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Durant les semaines pendant
lesquels des événements en lien
avec la série “Game of Thrones”
ont eu lieu.
Durant les semaine pendant
lesquelles des événements en lien
avec la première ligue
ukrainienne ont eu lieu.

30. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Parenthèse : IA explicable et les explications

31. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Lier les modèles d’apprentissage aux connaissances
En utilisant une biographie comme
Tiziano Vecelli (1488/1490 – 27 August 1576), known in English
as Titian, was an Italian painter, the most important member
of the 16th-century Venetian school. He was born in Pieve di
Cadore, near Belluno, in Veneto (Republic of Venice). His
painting methods would profoundly influence future
generations of Western Art.
Prédire si les oeuvres de l’artiste (Titian) sont
exposées dans un grand musée européen ?
The model says no.

32. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Lier les modèles d’apprentissage aux connaissances
En utilisant un biographie, connectée à un
graphe de connaissances, comme
Tiziano Vecelli (1488/1490 – 27 August 1576), known in English
as Titian, was an Italian painter, the most important member
of the 16th-century Venetian school. He was born in Pieve di
Cadore, near Belluno, in Veneto (Republic of Venice). His
painting methods would profoundly influence future
generations of Western Art.
Prédire si les oeuvres de l’artiste (Titian) sont
exposées dans un grand musée européen ?
The model says no.

33. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Lier les modèles d’apprentissage aux connaissances
En utilisant un biographie, connectée à un
graphe de connaissances, comme
Tiziano Vecelli (1488/1490 – 27 August 1576), known in English
as Titian, was an Italian painter, the most important member
of the 16th-century Venetian school. He was born in Pieve di
Cadore, near Belluno, in Veneto (Republic of Venice). His
painting methods would profoundly influence future
generations of Western Art.
Prédire si les oeuvres de l’artiste (Titian) sont
exposées dans un grand musée européen ?
Le modèle dit que non…

34. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Lier les modèles d’apprentissage aux connaissances
En utilisant un biographie, connectée à un
graphe de connaissances, comme
Tiziano Vecelli(+0.04) (1488/1490 – 27 August 1576), known in
English as Titian, was an Italian(-0.03) painter, the most
important member of the 16th-century Venetian school(+0.01).
He was born in Pieve di Cadore(-0.03), near Belluno(-0.01), in
Veneto(-0.01) (Republic of Venice(-0.01)). His painting methods
would profoundly influence future generations of Western
Art(+0.001).
Prédire si les oeuvres de l’artiste (Titian) sont
exposées dans un grand musée européen ?
Le modèle dit que non… pourquoi ?

35. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Lier les modèles d’apprentissage aux connaissances

36. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Lier les modèles d’apprentissage aux connaissances
● Les peintres des Pays Bas qui faisaient partie de la Guilde de Saint-Luc
d’Anvers ont plus de chance d'être classifiés positivement par le modèle.
● Les peintres dont la biographie mentionne des oeuvres représentant la
Vierge Marie ont plus de chance d'être classifiés positivement par le
modèle.
● Les peintres de certaines régions d’Italie ont plus de chance d'être
classifiés négativement par le modèle.
● Les footballeurs néerlandais ont plus de chance que les footballeurs
italiens d’être classifiés positivement par le modèle.

37. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Entités liées aux peintres que le réseau de
neurones a classifiés comme ayant des
œuvres dans un grand musée européen
Entités liées aux peintres que le réseau de
neurones a classifiés comme n’ayant pas d’
œuvre dans un grand musée européen

38. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
A venir : regarder à l’intérieur des modèles
Exemple : analyse de sentiment dans les avis sur des films
IMDB
avis sur
25000 films
Réseau de
neurones
Positif (1) ou négatif (0)

39. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
A venir : regarder à l’intérieur des modèles
Exemple : analyse de sentiment dans les avis sur des films
IMDB
avis sur
25000 films
Réseau de
neurones
Positif (1) ou négatif (0)
ensembles
de
mots
...
Plongement
(embeddings)
Convolution
(1D)
MaxPooling Aplatissement
et couche dense
Sortie

40. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
A venir : regarder à l’intérieur des modèles
Exemple : analyse de sentiment dans les avis sur des films
IMDB
avis sur
25000 films
Réseau de
neurones
Positif (1) ou négatif (0)
ensembles
de
mots
...
Plongement
(embeddings)
Convolution
(1D)
MaxPooling Aplatissement
et couche dense
Sortie
Précision : ~88.5%

41. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
A venir : regarder à l’intérieur des modèles
Exemple : analyse de sentiment dans les avis sur des films
IMDB
avis sur
25000 films
Réseau de
neurones
Positif (1) ou négatif (0)
un
avis
...
Plongement
(embeddings)
Convolution
(1D)
MaxPooling Aplatissement
et couche dense
Sortie
Précision : ~88.5%
Le niveau d’activation des neurones de la
dernière couche correspond à une
représentation interne abstraite de chaque
avis pour la prédiction du sentiment.

42. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
A venir : regarder à l’intérieur des modèles
Exemple : analyse de sentiment dans les avis sur des films
IMDB
avis sur
25000 films
Réseau de
neurones
Positif (1) ou négatif (0)
un
avis
...
Plongement
(embeddings)
Convolution
(1D)
MaxPooling Aplatissement
et couche dense
Sortie
Précision : ~88.5%
Le niveau d’activation des neurones de la
dernière couche correspond à une
représentation interne abstraite de chaque
avis pour la prédiction du sentiment.
Idée : Partitionner les avis en fonction de
cette représentation pour trouver des
“catégories” utilisées par le modèle pour la
classification.

43. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Combien de catégories

44. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Combien de catégories
40

45. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Partitionnement
32 partitions de plus de 10
éléments, représentés par les
vecteurs moyens d’activation
des neurones de la dernière
couche.

46. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Classification moyenne de chaque partition

47. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Classification moyenne de chaque partition
Positif
Négatif

48. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Partitionnement
La même figure avec les vecteurs
d’activation moyens des 32
partitions, mais dans l’ordre du
résultat moyen de la classification.

49. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Étudier chaque partition ?
Partition 0 :
Partition 16 :
Partition 31 :
20 mots les plus représentatifs : christians, norman, corporate, competent,
bergman, priceless, sentence, kitchen, bay, coffee, namely, willis, credible, asked,
blake, monkeys, jr, capturing, interest, 6
Classification avec les 500 mots les plus représentatifs: 5.12x10-12
20 mots les plus représentatifs : 00, synopsis, molly, wizard, personalities, wears,
account, genres, useful, atrocious, subsequent, rangers, slap, price, colour, killers,
ass, hired, st, saga
Classification avec les 500 mots les plus représentatifs: 0.44
20 mots les plus représentatifs : 1972, instant, 35, sadistic, relationships, piano,
cross, merit, moon, lengthy, shirley, wishes, randomly, excuse, terror, n,
recommended, godzilla, jake, america
Classification avec les 500 mots les plus représentatifs: 1.0

50. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Conclusions
Il y a énormément à faire pour permettre un
alignement entre les modèles d’apprentissage et
les modèles de connaissances.
Pas seulement utile pour l’IA explicable, mais aussi
pour comprendre les modèles, les diriger en
utilisant les connaissances existantes et pour en
extraire de nouvelles connaissances.

51. 11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
11 -
],x[8])),x[0]))) - x[5
(x[6],min(x[8],min(max(x[3],x[4]),x[1])
)))
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4]
11)
# x[6] if (((5 > 7) and ((7 - x[6]) < 0)) or (4
x[0]))
# x[7] if ((min(x[1],x[8]) - 9) != 11)
# 5 if ((0 > (5 - x[4])) or (max(4,x[6]) >
min(max(max(min(x[7],max(x[7],x[6])),x[0]),x[8]),5
# x[6] if ((x[3] < x[2]) and (7 != x[8]))
# x[3] if (not (0 >
max(max(max(min(x[4],4),max(x[8],2)),max(4,2)),x[3])
and (not (((11 != (7 - x[0])) or
(max(x[7],max(7,x[1])) == max(7,x[6]))) or (min(2,4)
!= x[0])) or (2 == x[6])))
# x[4] if ((min(x[1],x[7]) - ((x[0] - 11) - x[4])) !=
11)
# x[0] if (x[8] != min(x[1],9))
# Training accuracy: 0.9213483
# Test accuracy: 0.893
# rule applic
# [51
Merci
@mdaquin
avec des travaux de :
Ilaria Andriy Danny Abdul
Tiddi Nikolov Diamond Shahid