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"|--------|-------|-------------|\n",
"| Domaine | $\\mathbb{Z}$ (entiers infinis) | $\\{0, \\ldots, 2^n - 1\\}$ (modulo $2^n$) |\n",
"| Debordement | Pas de debordement | Wrapping modulaire |\n",
"| Opérations bit a bit | Non disponibles | `&`, `|`, `^`, `~`, `<<`, `>>` |"
"| Opérations bit a bit | Non disponibles | `&`, `\\|`, `^`, `~`, `<<`, `>>` |"
]
},
{
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"| ORDER BY | `ORDER BY ?name` | Tri ascendant |\n",
"\n",
"**rdflib vs dotNetRDF** :\n",
"\n",
"| Opération | rdflib | dotNetRDF |\n",
"|-----------|---------|----------|\n",
"| Executer requête | `g.query(\"SELECT...\")` | `g.ExecuteQuery(\"SELECT...\")` |\n",
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"| Opérateur | Description | Exemple |\n",
"|-----------|-------------|----------|\n",
"| Comparaisons | `=`, `!=`, `<`, `>`, `<=`, `>=` | `FILTER (?age > 4)` |\n",
"| Logique | `&&`, `||`, `!` | `FILTER (?age > 4 && ?age < 10)` |\n",
"| Logique | `&&`, `\\|\\|`, `!` | `FILTER (?age > 4 && ?age < 10)` |\n",
"| Tests de type | `isURI()`, `isBlank()`, `isLiteral()` | `FILTER (isURI(?x))` |\n",
"| Regex | `REGEX()` | `FILTER (REGEX(?name, \"^C\"))` |\n",
"| Existence | `BOUND()` | `FILTER (BOUND(?optional))` |\n",
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"| Ordre-dépendance | Oui | Non |\n",
"| Backtracking | Possible | Jamais |\n",
"| Bruit | Robuste (peut revenir) | Fragile (VS vide) |\n",
"| Complexite memoire | O(1) | `O(|G| + |S|)` |"
"| Complexite memoire | O(1) | `O(\\|G\\| + \\|S\\|)` |"
]
},
{
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"\n",
"| Composant | Rôle | Complexite |\n",
"|-----------|------|------------|\n",
"| `explain()` | Construit l'arbre de preuve lineaire | `O(n * |règles|)` ou n = profondeur de simplification |\n",
"| `variabilize()` | Substitution constante -> variable | `O(n * |mapping|)` |\n",
"| `explain()` | Construit l'arbre de preuve lineaire | `O(n * \\|règles\\|)` ou n = profondeur de simplification |\n",
"| `variabilize()` | Substitution constante -> variable | `O(n * \\|mapping\\|)` |\n",
"| `extract_rule()` | Projection des feuilles | O(n) |\n",
"| `learn()` | Pipeline complet | Lineaire en la taille de la preuve |\n",
"| `simplify_with_learned()` | Utilisation des règles | O(1) en cas de hit, `O(n*|règles|)` sinon |\n",
"| `simplify_with_learned()` | Utilisation des règles | O(1) en cas de hit, `O(n*\\|règles\\|)` sinon |\n",
"\n",
"L'intérêt principal est le **lookup O(1)** : une règle apprise permet de court-circuiter la chaîne de preuve complete.\n",
"\n",
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"| Détermination minimale | `MinimalConsistentDet` (BFS par taille) |\n",
"| Borne PAC | `PacBound` (m ≥ (1/ε)·(ln2^n + ln(1/δ))) |\n",
"| Parallèle OWL | `FunctionalViolations` (owl:FunctionalProperty) |\n",
"| Sélection par MI | `MutualInformation` (`H(target) − H(target|attr)`) |\n",
"| Sélection par MI | `MutualInformation` (`H(target) − H(target\\|attr)`) |\n",
"\n",
"La lib Python (sklearn/AIMA) fournit ces constructions en boîte noire ; l'implémentation from-scratch rend **visible**\n",
"pourquoi la détermination minimale réduit exponentiellement l'espace d'hypothèses (2^n → 2^d) et rend le nombre d'exemples linéaire en d.\n",
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"- Interpretation : \"Il faut ignorer 3 variables pour rendre la KB coherente\"\n",
"\n",
"**Mesures basees sur la distance de Dalal :**\n",
"\n",
"| Mesure | Valeur | Interpretation |\n",
"|--------|--------|----------------|\n",
"| **DSum** | 3.0 | Somme des distances aux mondes les plus proches |\n",
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"| `a` | False | Imposé par clause dure `!a` |\n",
"| `b` | True | Imposé par clause dure `b` |\n",
"| `c` | False | Préférence molle `!c` (poids 15) respectée |\n",
"| `d` | True | Nécessaire car `c=False` et clause dure `c || d` |\n",
"| `f` | True | Nécessaire car `c=False, g=False` et clause dure `f || c || g` |\n",
"| `d` | True | Nécessaire car `c=False` et clause dure `c \\|\\| d` |\n",
"| `f` | True | Nécessaire car `c=False, g=False` et clause dure `f \\|\\| c \\|\\| g` |\n",
"| `g` | False | Choix libre, minimise les variables vraies |\n",
"\n",
"**Coût total : 25** (seule la clause molle `a || !b` est violée, car `a=False` et `b=True`)\n",
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date: '2026-09-12'
by: myia-po-2023:CoursIA
python_sha: 5dd328469df8355c1ae2a72655b95db3a8daee74
csharp_sha: 46a408620adbc5b189ba37b79508fbc2e9338fb4
content_python_sha: f851a5980fe5b9afac1f552d4c7ee959c2f1fc28d8e2ef896aa6eb0350881f5a
content_csharp_sha: 43c8ec183654ff0567abba904c7ff24bdd4c3c55c62af84227f6cfc6abc31173
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date: '2026-09-12'
by: myia-po-2023:CoursIA
python_sha: 1a11d279a41d9b90b6fae8b7c9b47b88bc0d40eb
csharp_sha: 90ecbe92d294a491f177daa1b9cd8a18c0cfbe0c
content_python_sha: dc1cf871555f4a61e2d44b477b60bb77b46cd523b989ac19737e347b885cbe58
content_csharp_sha: 32e0e17b4f2e444d5ec35a90262ae94f6d2f498aed235652fd4277b03ade26ac
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date: '2026-09-12'
by: myia-po-2023:CoursIA
python_sha: 7bebaf66d4fd857286b2eadcfa2bf8b29cdfcaf5
csharp_sha: a3d3c6e153f01184c631ae8c374aee76d8768f15
content_python_sha: 9530a212e8ddf916f40df18fe5f0df4f6727dc92d44490bcb26a520f92499140
content_csharp_sha: 1b5dcf9894d9358b0e4a2904cf8c7ad3e312bfa05f65c68209d1b164067e28d6
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date: '2026-09-12'
by: myia-po-2023:CoursIA
python_sha: 56e362f0ab00bf3b281a4c4882306612403edc5d
csharp_sha: 5540da4432053e9012a7eb38d2e47c0b0afd82e0
content_python_sha: 9ab0ac209c919a3e00db6d19de2e76fc49b6a4207542c0d9d63c0add22b4b0a5
content_csharp_sha: 55c38ce47f3c6c4d85aae2ab4aaf6ecd3c2c36b455f4dfbea6703c20f006ffd4
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date: '2026-09-14'
by: myia-po-2023:CoursIA
python_sha: 7d0b2351feaed0b91dc73178fb9285fb55620bcb
csharp_sha: 5540da4432053e9012a7eb38d2e47c0b0afd82e0
content_python_sha: dc720f273b4f286907f9bf6900dcd7607a33ff309cb6b95ed5b1407c4208bc38
content_csharp_sha: 55c38ce47f3c6c4d85aae2ab4aaf6ecd3c2c36b455f4dfbea6703c20f006ffd4
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bridge_verdict: SOTA-OK
bridge_verdict_reason: "Lib-vs-lib meme moteur Java TweetyProject (revision des croyances AGM, contraction/revision) : C# branche IKVM 8.15.0 + tweety-beliefdynamics.dll (tracked) + 8 refs `org.tweetyproject.*` actifs (3 cellules avec using explicite + classes Java `SimpleKbReiter`, `DefaultRevisionOperator` invoquees). Python branche JPype + `from java.util import ArrayList, HashSet` + `from org.tweetyproject.beliefdynamics.* import` + meme bibliotheque Java TweetyProject. Verifie firsthand (c.227, 2026-08-12, po-2023:CoursIA-2) : les deux cotes operent sur la meme base de connaissances Java (BeliefSet) avec les memes operateurs de revision, pont IKVM-bytecode vs JPype-JVM-runtime."
known_differences:
- "2026-09-16 (myia-po-2023:CoursIA) #15795 : REBASELINE apres merge main. L'attestation 0008 du 09-12 (python_sha 9e629ca1) est retiree : index en collision avec 0008-po-2024 de main (regle #14911/#15345, le postérieur garde l'index) ET blob perimé (python avance via #15702 fix subprocess et #16002 renum portés par main). Nouvelle attestation 0009 sur les blobs courants (python_sha 5073b3dc). content_csharp_sha inchangé : le jumeau C# n'est pas touché."
- "Socle commun : revision des croyances (AGM, contraction/revision) via TweetyProject."
- "Lib-vs-lib : C# via IKVM (`using org.tweetyproject.*`). Python via JPype (`from java.util import ArrayList, HashSet` + `from jpype.types import *`). Meme fondement Java, idiomatique de pont differente."
- "Re-baseline 2026-07-25 (po-2024, consolidation #8264) : SHA Python avance depuis l'audit 2026-07-23 via #8170 (markdown hyphenation (Note-de-parite)) ; drift verifie paraphite-preservant (ne touche pas l'axe de parite semantic)."
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date: '2026-09-16'
by: myia-po-2023:CoursIA
python_sha: 5073b3dce3e9bcadbc978e23ad784d93daaf5580
csharp_sha: 2fe788ad0ec751afb1ea6c14d763f8af543ce6a9
content_python_sha: ac5977613ae5b4ffe056789ebd4979a0044b5320e566b7c619c1374a38de32aa
content_csharp_sha: a584b14de637f6800f8809a29b243c09a0dd4bebdd7ab37b5a9cd1eb9cc72ff0
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date: '2026-09-12'
by: myia-po-2023:CoursIA
python_sha: fd8bbb667efac698a714b23e58e8a0d09368f474
csharp_sha: 8a20071b8f16ea0f5291c8d879d6abbe3fcc63ba
content_python_sha: 09ee22c804de41ec8be87a221550799fc802e326bdeaa7ce25eb54e730f28ac8
content_csharp_sha: 512dc4d1c58e544e05133833e533d538747046f0e2f0f66ca49d3afaa119badf
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