I am an Associate Professor (Lecturer, Maître de Conférences) of Computer Science at Paris II Panthéon-Assas University in the Lemma laboratory. I was a postdoctoral researcher at CRIL, University of Artois between 2022 and 2024. Prior to this, I defended my PhD thesis in Artificial Intelligence entitled "Explainability of possibilistic and fuzzy rule-based systems" (eXplainable Artificial Intelligence, Knowledge Representation and Reasoning, Possibility theory, Fuzzy reasoning) on January 27th, 2022 at Sorbonne University (LIP6 laboratory). PDF - slides - code - french summary.
I studied before mathematics and computer science applied to cryptology at Paris 7 Diderot University and artificial intelligence at Paris 5 Descartes University.
ORCID: https://orcid.org/0000-0001-5135-8924
(illustration from this book)
Baaj, Ismaïl. "On learning capacities of Sugeno integrals with systems of fuzzy relational equations." arXiv preprint arXiv:2408.07768 (2024). PDF/DOI.
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}Baaj, Ismaïl. "Maximal Consistent Subsystems of Max-T Fuzzy Relational Equations." arXiv preprint arXiv:2311.03059 (2023). PDF/DOI.
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title = "{Maximal Consistent Subsystems of Max-T Fuzzy Relational Equations}",
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}Baaj, Ismaïl. "Handling the inconsistency of systems of min→ fuzzy relational equations." arXiv preprint arXiv:2308.12385 (2023). PDF/DOI.
@article{2023arXiv230812385B,
author = {Baaj, Ismaïl},
title = {Handling the inconsistency of systems of min→ fuzzy relational equations},
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year = 2023,
month = aug,
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}Baaj, Ismaïl. "Chebyshev distances associated to the second members of systems of Max-product/Lukasiewicz Fuzzy relational equations." arXiv preprint arxiv.2302.08554 (2023). PDF/DOI.
@article{https://doi.org/10.48550/arxiv.2302.08554,
doi = {10.48550/ARXIV.2302.08554},
url = {https://arxiv.org/abs/2302.08554},
author = {Baaj, Ismaïl},
keywords = {Artificial Intelligence (cs.AI), Logic in Computer Science (cs.LO), Logic (math.LO), FOS: Computer and information sciences, FOS: Computer and information sciences, FOS: Mathematics, FOS: Mathematics},
title = {Chebyshev distances associated to the second members of systems of Max-product/Lukasiewicz Fuzzy relational equations},
publisher = {arXiv},
year = {2023},
copyright = {Creative Commons Attribution 4.0 International}
}
Baaj, Ismaïl. "Max-min Learning of Approximate Weight Matrices From Fuzzy Data." arXiv preprint arxiv.2301.06141 (2023). PDF/DOI.
@misc{https://doi.org/10.48550/arxiv.2301.06141,
doi = {10.48550/ARXIV.2301.06141},
url = {https://arxiv.org/abs/2301.06141},
author = {Baaj, Ismaïl},
keywords = {Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Max-min Learning of Approximate Weight Matrices from Fuzzy Data},
publisher = {arXiv},
year = {2023},
copyright = {Creative Commons Attribution 4.0 International}}
Baaj, I. (2025 July). On learning capacities of Sugeno integrals with systems of fuzzy relational equations. FUZZ IEEE 2025. To appear (accepted). Preprint.
Strauss, 0., Rico, A. & Baaj, I. (2025, July). A new backpropagation method dedicated for learning a max-min neural network. FUZZ IEEE 2025. To appear (accepted).
Baaj, I. (2024). On the handling of inconsistent systems based on max-product and max-Lukasiewicz compositions. Fuzzy Sets and Systems. ISSN 0165-0114. DOI.
Baaj, I. (2024, November). Sur l’apprentissage de capacités pour les intégrales de Sugeno avec des systèmes d’équations relationnelles floues. Cepadues. To appear.
Strauss, 0., Rico, A., Baaj, I. & Chevrollier-Mehat, A. (2024, November). Apprentissage d'un réseau de neurones MaxMin. Cepadues. To appear.
Baaj, I., Bouraoui, Z., Cornuéjols, A., Denœux, T., Destercke, S., Dubois, D., Lesot M., Marques-Silva J., Mengin J., Prade H., Schockaert S., Serrurier M., Strauss O. & Vrain, C. (2024). Synergies Between Machine Learning and Reasoning - An Introduction by the Kay R. Amel group. International Journal of Approximate Reasoning, 109206. DOI.
Baaj, I. (2024). On the handling of inconsistent systems of max-min fuzzy relational equations, Fuzzy Sets and Systems. 2024. 108912. ISSN 0165-0114. DOI.
Baaj, I. (2023, November). Approximations de Tchebyshev d'un système incompatible d'équations relationnelles floues de type max-T. In Rencontres francophones sur la logique floue et ses applications. Cepadues. PDF.
Baaj, I., Dubois, D., Faux, F., Prade, H., Rico A. & Strauss, O. (2022, October). Réseau de neurones et logique: un cadre qualitatif. Cepadues. PDF.
Baaj, I., & Rico, A. (2022, October). Intégrales qualitatives avec l'implication et la conjonction de Gödel : élicitation et extraction de règles. Cepadues.
Baaj, I. (2022, October). Apprentissage des paramètres des règles d’un système à base de règles possibilistes. In Rencontres francophones sur la logique floue et ses applications. Cepadues. PDF.
Baaj, I. (2022, July). Learning Rule Parameters of Possibilistic Rule-Based System. In 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1-8). IEEE. DOI - code.
@INPROCEEDINGS{baaj2022learning,
author={Baaj, Ismaïl},
booktitle={2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
title={Learning Rule Parameters of Possibilistic Rule-Based System},
year={2022},
volume={},
number={},
pages={1-8},
abstract={In this paper, we introduce a learning paradigm of the rule parameters of a possibilistic rule-based system, given training data. For a rule-based system composed of n if-then parallel possibilistic rules, we introduce an equation system denoted (Σn), which is analogous to the Farreny-Prade equation system. The unknown part of the system (Σn) is a vector composed of the rule parameters, whose values must be determined according to training data.We establish necessary and sufficient conditions for the system (Σn) to be consistent. If this is the case, we show that the set of solutions of the system is a Cartesian product of subintervals of [0, 1] whose bounds are computed. Then, we deduce that there are a unique maximal solution and, as it is well known by Sanchez’s work on the solving of min-max fuzzy relational equations, a unique minimal one. These results are proved by relating the solutions of (Σn) to those of the equation system given by the first n − 1 possibilistic rules equipped with a second member which is constructed from that of (Σn).Finally, our results are illustrated by an example.},
keywords={},
doi={10.1109/FUZZ-IEEE55066.2022.9882626},
ISSN={1558-4739},
month={July},
}
Baaj, I., & Rico, A. (2022, July). Qualitative integrals with Gödel's implication and conjunction: elicitation and if-then rules extraction. In 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1-8). IEEE. DOI - code.
@INPROCEEDINGS{baaj2022qualitative,
author={Baaj, Ismaïl and Rico, Agnès},
booktitle={2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
title={Qualitative integrals with Gödel’s implication and conjunction: elicitation and if-then rules extraction},
year={2022},
volume={},
number={},
pages={1-8},
abstract={In this article, we explore the properties of the two generalized Sugeno integrals that we obtain by substituting in the expression of a classical Sugeno integral, the Kleene-Dienes conjunction and the Kleene-Dienes implication by the Gödel conjunction and the Gödel implication, respectively.A major difference compared to the classical Sugeno integrals is that the implication-based Gödel integral and the conjunction-based Gödel integral do not return the same result. In this paper, we investigate the adaptation of classical results for Sugeno integrals to Gödel integrals. Namely, their elicitation according to a piece of data and the extraction of selection and elimination if-then rules. The selection rules are obtained from the focal sets of the capacity underlying a conjunction-based Gödel integral, while the elimination rules are extracted from the focal sets of the conjugate of the capacity defining an implication-based Gödel integral.To illustrate our results, we apply our constructions to a real data example already used for Sugeno integrals.},
keywords={},
doi={10.1109/FUZZ-IEEE55066.2022.9882800},
ISSN={1558-4739},
month={July},
}
Baaj, I. (2022). Explainability of possibilistic and fuzzy rule-based systems (Doctoral dissertation, Sorbonne Université). PDF - slides - code - french summary.
@phdthesis{baaj:tel-03647652,
TITLE = {{Explainability of possibilistic and fuzzy rule-based systems}},
AUTHOR = {Baaj, Ismaïl},
URL = {https://tel.archives-ouvertes.fr/tel-03647652},
NUMBER = {2022SORUS021},
SCHOOL = {{Sorbonne Universit{\'e}}},
YEAR = {2022},
MONTH = Jan,
KEYWORDS = {Explainable artificial intelligence ; Knowledge representation and reasoning ; Rule-based system ; Possibility theory ; Fuzzy logic ; Conceptual graphs ; Intelligence artificielle explicable ; Repr{\'e}sentation des connaissances et raisonnement ; Syst{\`e}me {\`a} base de r{\`e}gles ; Th{\'e}orie des possibilit{\'e}s ; Logique floue ; Graphes conceptuels},
TYPE = {Theses},
PDF = {https://tel.archives-ouvertes.fr/tel-03647652/file/BAAJ_Ismail_2022.pdf},
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Baaj, I., Poli, J. P., Ouerdane, W., & Maudet, N. (2021, October). Inférence min-max pour un système à base de règles possibilistes. In Rencontres francophones sur la logique floue et ses applications (pp. 233-240). Cepadues. PDF - code.
@inproceedings{baaj:cea-03402616,
TITLE = {{Inf{\'e}rence min-max pour un syst{\`e}me {\`a} base de r{\`e}gles possibilistes}},
AUTHOR = {BAAJ, Isma{\"i}l and Poli, Jean-Philippe and Ouerdane, Wassila and Maudet, Nicolas},
URL = {https://hal-cea.archives-ouvertes.fr/cea-03402616},
NOTE = {I.S.B.N. : 9782364939066},
BOOKTITLE = {{Rencontres francophones sur la logique floue et ses applications}},
ADDRESS = {Paris, France},
ORGANIZATION = {{Universit{\'e} de la Sorbonne}},
PUBLISHER = {{Cepadues}},
SERIES = {Rencontres francophones sur la logique floue et ses Applications 2021},
PAGES = {233-240},
YEAR = {2021},
MONTH = Oct,
KEYWORDS = {Possibility theory ; rule-based system ; neural network ; artificial intelligence ; Machine learning ; r{\'e}seau de neurones ; syst{\`e}me {\`a} base de r{\`e}gles ; Th{\'e}orie des possibilit{\'e}s},
PDF = {https://hal-cea.archives-ouvertes.fr/cea-03402616/file/LFA__inf_rence_min_max_pour_un_syst_me___base_de_r_gles_possibilistes.pdf},
HAL_ID = {cea-03402616},
HAL_VERSION = {v1},
}
Baaj, I., Poli, J. P., Ouerdane, W., & Maudet, N. (2021, September). Representation of Explanations of Possibilistic Inference Decisions. In European Conference on Symbolic and Quantitative Approaches with Uncertainty (pp. 513-527). Springer, Cham. DOI - code.
@inproceedings{baaj2021representation,
title={Representation of Explanations of Possibilistic Inference Decisions},
author={Baaj, Isma{\"\i}l and Poli, Jean-Philippe and Ouerdane, Wassila and Maudet, Nicolas},
booktitle={European Conference on Symbolic and Quantitative Approaches with Uncertainty},
pages={513--527},
year={2021},
organization={Springer}
}
Baaj, I., Poli, J. P., Ouerdane, W., & Maudet, N. (2021, July). Min-max inference for Possibilistic Rule-Based System. In 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1-6). IEEE. DOI - code.
@inproceedings{baaj2021min,
title={Min-max inference for Possibilistic Rule-Based System},
author={Baaj, Isma{\"\i}l and Poli, Jean-Philippe and Ouerdane, Wassila and Maudet, Nicolas},
booktitle={2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)},
pages={1--6},
year={2021},
organization={IEEE}
}
Baaj, I., Poli, J. P., & Ouerdane, W. (2019). Some insights towards a unified semantic representation of explanation for explainable artificial intelligence. In Proceedings of the 1st Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence (NL4XAI 2019) (pp. 14-19). DOI.
@inproceedings{baaj2019some,
title={Some insights towards a unified semantic representation of explanation for explainable artificial intelligence},
author={Baaj, Isma{\"\i}l and Poli, Jean-Philippe and Ouerdane, Wassila},
booktitle={Proceedings of the 1st Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence (NL4XAI 2019)},
pages={14--19},
year={2019}
}
Baaj, I., & Poli, J. P. (2019, June). Natural language generation of explanations of fuzzy inference decisions. In 2019 IEEE international conference on fuzzy systems (FUZZ-IEEE) (pp. 1-6). IEEE. DOI.
@inproceedings{baaj2019natural,
title={Natural language generation of explanations of fuzzy inference decisions},
author={Baaj, Isma{\"\i}l and Poli, Jean-Philippe},
booktitle={2019 IEEE international conference on fuzzy systems (FUZZ-IEEE)},
pages={1--6},
year={2019},
organization={IEEE}
}
curl https://ismailbaaj.fr/ibpk.asc | gpg --import