@techreport {43374,
	title = {Acquiring Qualitative Explainable Graphs for Automated Driving Scene Interpretation},
	year = {2023},
	publisher = {Simula},
	abstract = {The future of automated driving (AD) is rooted in the development of robust, fair and explainable artificial intelligence methods. Upon request, automated vehicles must be able to explain their decisions to the driver and the car passengers, to the pedestrians and other vulnerable road users and potentially to external auditors in case of accidents. However, nowadays, most explainable methods still rely on quantitative analysis of the AD scene representations captured by multiple sensors. This paper proposes a novel representation of AD scenes, called Qualitative eXplainable Graph (QXG), dedicated to qualitative spatiotemporal reasoning of long-term scenes. The construction of this graph exploits the recent Qualitative Constraint Acquisition paradigm. Our experimental results on NuScenes, an open real-world multi-modal dataset, show that the qualitative eXplainable graph of an AD scene composed of 40 frames can be computed in real-time and light in space storage which makes it a potentially interesting tool for improved and more trustworthy perception and control processes in AD.},
	author = {Belmecheri, Nassim and Gotlieb, Arnaud and Lazaar, Nadjib and Spieker, Helge}
}
@inproceedings {43261,
	title = {Active Disjunctive Constraint Acquisition},
	journal = {Knowledge Representation (KR), Research Track, Rhodes, Greece, September 2-8},
	year = {2023},
	author = {Menguy, Gr{\'e}goire and Bardin, Sebastien and Lazaar, Nadjib and Gotlieb, Arnaud}
}
@inproceedings {43229,
	title = {Approche g{\'e}n{\'e}rique pour l{\textquoteright}acquisition de contraintes qualitatives},
	journal = {JFPC},
	year = {2023},
	abstract = {De nombreux probl{\`e}mes de planification et d{\textquoteright}ordonnance-ment impliquent la conception de subtiles combinaisons lo-giques de contraintes temporelles ou spatiales. D{\textquoteright}une part,la mod{\'e}lisation pr{\'e}cise de ces contraintes, qui sont formu-l{\'e}es dans diverses alg{\`e}bres de relations, implique un cer-tain nombre de combinaisons logiques possibles et n{\'e}ces-site une expertise en mod{\'e}lisation bas{\'e}e sur les contraintes.D{\textquoteright}autre part, l{\textquoteright}acquisition active de contraintes (AC) a {\'e}t{\'e}utilis{\'e}e avec succ{\`e}s pour aider les utilisateurs non exp{\'e}-riment{\'e}s {\`a} apprendre les r{\'e}seaux de contraintes conjonc-tives par la g{\'e}n{\'e}ration d{\textquoteright}une s{\'e}quence de requ{\^e}tes. Danscet article, nous proposons GEQCA, pour Generic Quali-tative Constraint Acquisition, une m{\'e}thode d{\textquoteright}AC active quiapprend les contraintes qualitatives via le concept de re-qu{\^e}tes qualitatives. GEQCA combine les requ{\^e}tes quali-tatives avec la coh{\'e}rence de chemin limit{\'e}e dans le temps(PC pour Path Consistency) et la propagation des connais-sances de base pour acqu{\'e}rir les contraintes qualitatives.Nous prouvons la correction, la compl{\'e}tude et la terminai-son de GEQCA et nous donnons une {\'e}valuation exp{\'e}ri-mentale qui montre (i) l{\textquoteright}efficacit{\'e} de notre approche dansl{\textquoteright}apprentissage des contraintes temporelles et, (ii) l{\textquoteright}utilisa-tion de GEQCA sur des instances r{\'e}elles d{\textquoteright}ordonnance-ment.},
	author = {Mohamed Bachir {Belaid} and Belmecheri, Nassim and Lazaar, Nadjib and Gotlieb, Arnaud and Spieker, Helge}
}
@article {43214,
	title = {Boosting the Learning for Ranking Patterns},
	journal = {Algorithms},
	volume = {16},
	number = {218},
	year = {2023},
	month = {04/2023},
	pages = {26},
	publisher = {MDPI},
	abstract = {Pattern mining is a valuable tool for exploratory data analysis, but identifying relevant patterns for a specific user is challenging. Various interestingness measures have been developed to evaluate patterns, but they may not efficiently estimate user-specific functions. Learning user-specific functions by ranking patterns has been proposed, but this requires significant time and training samples. In this paper, we present a solution that formulates the problem of learning pattern ranking functions as a multi-criteria decision-making problem. Our approach uses an analytic hierarchy process (AHP) to elicit weights for different interestingness measures based on user preference. We also propose an active learning mode with a sensitivity-based heuristic to minimize user ranking queries while still providing high-quality results. Experiments show that our approach significantly reduces running time and returns precise pattern ranking while being robust to user mistakes, compared to state-of-the-art approaches.},
	author = {Belmecheri, Nassim and Noureddine, Aribi and Lazaar, Nadjib and Lebbah, Yahia and Loudni, Samir}
}
@inproceedings {43262,
	title = {Towards Trustworthy-AI-by-Design Methodology for Intelligent Radiology Systems},
	journal = {{\textquoteright}Sant{\'e} et IA s{\'e}minaire{\textquoteright} at PFIA 23, Strasbrourg, France. July 6},
	year = {2023},
	author = {Braye, Clotilde and Clech, J{\'e}r{\'e}my and Gotlieb, Arnaud and Lazaar, Nadjib and Mall{\'e}a, Patrick}
}
@inproceedings {42448,
	title = {Automated Program Analysis: Revisiting Precondition Inference through Constraint Acquisition},
	journal = {31st International Joint Conference on Artificial Intelligence and the 25th European Conference on Artificial Intelligence (IJCAI-ECAI 22)},
	year = {2022},
	publisher = {IJCAI},
	author = {Menguy, Gr{\'e}goire and Bardin, Sebastien and Lazaar, Nadjib and Gotlieb, Arnaud}
}
@misc {42654,
	title = {GEQCA: Generic Qualitative Constraint Acquisition},
	howpublished = {Lernen. Wissen. Daten. Analysen. (LWDA) - KDML Track},
	year = {2022},
	url = {https://lwda2022.de},
	author = {Mohamed Bachir {Belaid} and Belmecheri, Nassim and Gotlieb, Arnaud and Lazaar, Nadjib and Spieker, Helge}
}
@inproceedings {28050,
	title = {GEQCA: Generic Qualitative Constraint Acquisition},
	journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
	volume = {36},
	year = {2022},
	month = {06/2022},
	pages = {3690-3697},
	publisher = {AAAI},
	abstract = {Many planning, scheduling or multi-dimensional packing problems involve the design of subtle logical combinations of temporal or spatial constraints. On the one hand, the pre- cise modelling of these constraints, which are formulated in various relation algebras, entails a number of possible logical combinations and requires expertise in constraint-based mod- elling. On the other hand, active constraint acquisition (CA) has been used successfully to support non-experienced users in learning conjunctive constraint networks through the gen- eration of a sequence of queries. In this paper, we propose GEQCA, which stands for Generic Qualitative Constraint Acquisition, an active CA method that learns qualitative con- straints via the concept of qualitative queries. GEQCA com- bines qualitative queries with time-bounded path consistency (PC) and background knowledge propagation to acquire the qualitative constraints of any scheduling or packing prob- lem. We prove soundness, completeness and termination of GEQCA by exploiting the jointly exhaustive and pairwise disjoint property of qualitative calculus and we give an ex- perimental evaluation that shows (i) the efficiency of our ap- proach in learning temporal constraints and, (ii) the use of GEQCA on real scheduling instances.},
	doi = {https://doi.org/10.1609/aaai.v36i4.20282},
	url = {https://ojs.aaai.org/index.php/AAAI/article/view/20282},
	author = {Mohamed Bachir {Belaid} and Belmecheri, Nassim and Gotlieb, Arnaud and Lazaar, Nadjib and Spieker, Helge}
}
@misc {28240,
	title = {Constraint Programming for Itemset Mining with Multiple Minimum Supports},
	howpublished = {LIRMM, Montpellier, France},
	year = {2021},
	url = {http://www.lirmm.fr/~lazaar/caviar.html},
	author = {Mohamed Bachir {Belaid} and Lazaar, Nadjib}
}
@inproceedings {28048,
	title = {Constraint Programming for Itemset Mining with Multiple Minimum Supports},
	journal = {2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI)},
	year = {2021},
	pages = {598-603},
	publisher = { IEEE},
	abstract = {The problem of discovering frequent itemsets includ- ing rare ones has received a great deal of attention. The mining process needs to be flexible enough to extract frequent and rare regularities at once. On the other hand, it has recently been shown that constraint programming is a flexible way to tackle data mining tasks. In this paper, we propose a constraint programming approach for mining itemsets with multiple minimum supports. Our approach provides the user with the possibility to express any kind of constraints on the minimum item supports. An experimental analysis shows the practical effectiveness of our approach compared to the state of the art.},
	doi = {10.1109/ICTAI52525.2021.00095},
	author = {Mohamed Bachir {Belaid} and Lazaar, Nadjib}
}
@misc {28142,
	title = {Constraint Programming for Itemset Mining with Multiple Minimum Supports},
	howpublished = {IEEE International Conference on Tools with Artificial Intelligence (ICTAI)},
	year = {2021},
	type = {Paper presentation},
	url = {https://drive.google.com/file/d/1Wx-bltW08cQ9mpcZFkSuYzsGHHhMLYP-/view?usp=sharing},
	author = {Mohamed Bachir {Belaid} and Lazaar, Nadjib}
}
@techreport {28051,
	title = {Frequent Itemset Mining with Multiple Minimum Supports: a Constraint-based Approach},
	year = {2021},
	publisher = {arxiv},
	abstract = {The problem of discovering frequent itemsets including rare ones has received a great deal of attention. The mining process needs to be flexible enough to extract frequent and rare regularities at once. On the other hand, it has recently been shown that constraint programming is a flexible way to tackle data mining tasks. In this paper, we propose a constraint programming approach for mining itemsets with multiple minimum supports. Our approach provides the user with the possibility to express any kind of constraints on the minimum item supports. An experimental analysis shows the practical effectiveness of our approach compared to the state of the art.},
	url = {https://arxiv.org/pdf/2109.07844.pdf},
	author = {Mohamed Bachir {Belaid} and Lazaar, Nadjib}
}
@misc {28093,
	title = {Solve Optimization Problems with Unknown Constraint Networks},
	howpublished = {PTHG workshop in CP (online)},
	year = {2021},
	type = {Workshop presentation },
	url = {https://www.youtube.com/watch?v=JvX7yKm_WS4\&t=8s},
	author = {Mohamed Bachir {Belaid} and Gotlieb, Arnaud and Lazaar, Nadjib}
}
@inproceedings {28049,
	title = {Solve Optimization Problems with Unknown Constraint Networks},
	journal = {PTHG workshop in CP},
	year = {2021},
	abstract = {In most optimization problems, users have a clear understanding of the function to optimize (e.g., minimize the makespan for schedul- ing problems). However, the constraints may be difficult to state and their modelling often requires expertise in Constraint Program- ming. Active constraint acquisition has been successfully used to sup- port non-experienced users in learning constraint networks through the generation of a sequence of queries. In this paper, we propose Learn\&amp;Optimize, a method to solve optimization problems with known objective function and unknown constraint network. It uses an active constraint acquisition algorithm which learns the unknown constraints and computes boundaries for the optimal solution during the learning process. As a result, our method allows users to solve op- timization problems without learning the overall constraint network.},
	author = {Mohamed Bachir {Belaid} and Gotlieb, Arnaud and Lazaar, Nadjib}
}
@techreport {27664,
	title = {Computational Complexity of Three Central Problems in Itemset Mining},
	year = {2020},
	publisher = {arXiv},
	abstract = {Itemset mining is one of the most studied tasks in knowledge discovery. In this paper we analyze the computational complexity of three central itemset mining problems. We prove that mining confident rules with a given item in the head is NP-hard. We prove that mining high utility itemsets is NP-hard. We finally prove that mining maximal or closed itemsets is coNP-hard as soon as the users can specify constraints on the kind of itemsets they are interested in.},
	url = {https://arxiv.org/pdf/2012.02619.pdf},
	author = {Bessiere, Christian and Mohamed Bachir {Belaid} and Lazaar, Nadjib}
}
@inproceedings {26799,
	title = {RobTest: A CP Approach to Generate Maximal Test Trajectories for Industrial Robots},
	journal = {International Conference on Principles and Practice of Constraint Programming},
	volume = {12333},
	year = {2020},
	month = {08/2020},
	pages = {1-17},
	publisher = {Springer},
	edition = {LNCS},
	author = {Collet, Mathieu and Gotlieb, Arnaud and Lazaar, Nadjib and Carlsson, Mats and Marijan, Dusica and Mossige, Morten},
	editor = {Simonis, Helmut}
}
@inproceedings {26474,
	title = {Deploying Smart Program Understanding on a Large Code Base},
	journal = {2019 IEEE International Conference On Artificial Intelligence Testing (AITest)},
	year = {2019},
	publisher = {IEEE},
	doi = {10.1109/AITest.2019.000-4},
	author = {Ieva, Carlo and Gotlieb, Arnaud and Kaci, Souhila and Lazaar, Nadjib}
}
@inproceedings {26475,
	title = {Stress Testing of Single-Arm Robots Through Constraint-Based Generation of Continuous Trajectories},
	journal = {2019 IEEE International Conference On Artificial Intelligence Testing (AITest)},
	year = {2019},
	month = {04/2019},
	pages = {121-128},
	publisher = {IEEE},
	address = {San Francisco, CA, USA},
	author = {Collet, Mathieu and Gotlieb, Arnaud and Lazaar, Nadjib and Mossige, Morten}
}
@inproceedings {25964,
	title = {Discovering Program Topoi Through Clustering},
	journal = {Proceedings of the Thirty-Second IAAI/AAAI Conference on Innovative Applications of Artificial Intelligence},
	year = {2018},
	publisher = {AAAI Press},
	url = {https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16045},
	author = {Ieva, Carlo and Gotlieb, Arnaud and Kaci, Souhila and Lazaar, Nadjib}
}
@article {Simula.simula.1072,
	title = {A CP Framework for Testing CP},
	journal = {Constraints},
	volume = {17},
	number = {2},
	year = {2012},
	month = {February},
	pages = {123-147},
	publisher = {Springer US},
	abstract = {The success of several constraint-based modeling languages such as OPL, ZINC or COMET, appeals for better software engineering practices, particularly in the testing phase. This paper introduces a testing framework enabling automated test case generation for constraint programming. We propose a general framework of constraint program development which supposes that a first declarative and simple constraint model is available from the problem specifications analysis. Then, this model is refined using classical techniques such as constraint reformulation, surrogate, redundant, implied, global constraint and symmetry-breaking to form an improved constraint model that must be thoroughly tested before being used to address real-sized problems. We think that most of the faults are introduced in this refinement step and propose a process which takes the first declarative model as an oracle for detecting non-conformities and derive practical test purposes from this process. We implemented this approach in a new tool called {\sf CPTEST} that was used to automatically detect non-conformities on classical benchmark programs, such as the Golomb rulers, n-queens, social golfer and the car-sequencing problems.},
	author = {Lazaar, Nadjib and Gotlieb, Arnaud and Lebbah, Yahia}
}
