@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 {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}
}
@misc {43280,
	title = {Qualitative Constraint Acquisition},
	howpublished = {University of Southern Denmark},
	year = {2023},
	author = {Belmecheri, Nassim}
}
@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 {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}
}
