@inproceedings {43281,
	title = {A Review of Validation and Verification of Neural Network-based Policies for Sequential Decision Making},
	journal = {Rencontres des Jeunes Chercheurs en Intelligence Artificielle},
	year = {2023},
	abstract = {In sequential decision making, neural networks (NNs) are nowadays commonly used to represent and learn the agent{\textquoteright}s policy. This area of application has implied new software quality assessment challenges that traditional validation and verification practises are not able to handle. Subsequently, novel approaches have emerged to adapt those techniques to NN-based policies for sequential decision making. This survey paper aims at summarising these novel contributions and proposing future research directions. We conducted a literature review of recent research papers (from 2018 to beginning of 2023), whose topics cover aspects of the test or verification of NN-based policies. The selection has been enriched by a snowballing process from the previously selected papers, in order to relax the scope of the study and provide the reader with insight into similar verification challenges and their recent solutions. 18 papers have been finally selected. Our results show evidence of increasing interest for this subject. They highlight the diversity of both the exact problems considered and the techniques used to tackle them.},
	keywords = {Neural Networks, Sequential decision making, Software Testing},
	url = {https://pfia23.icube.unistra.fr/conferences/rjcia/Actes/RJCIA2023_paper_5.pdf},
	author = {Mazouni, Quentin and Spieker, Helge and Gotlieb, Arnaud and Acher, Mathieu}
}
@inproceedings {25386,
	title = {Efficient and Complete FD-Solving for Extended Array Constraints},
	journal = {Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence},
	year = {2017},
	month = {08/2017},
	pages = {1231-1238},
	publisher = {IJCAI},
	doi = {10.24963/ijcai.2017/171},
	author = {Plazar, Quentin and Acher, Mathieu and Bardin, Sebastien and Gotlieb, Arnaud}
}
@techreport {AFM_Synthesis,
	title = {Synthesis of Attributed Feature Models From Product Descriptions: Foundations},
	number = {RR-8680},
	year = {2015},
	publisher = {INRIA},
	abstract = {Feature modeling is a widely used formalism to characterize a set of products (also called configurations). As a manual elaboration is a long and arduous task, numerous techniques have been proposed to reverse engineer feature models from various kinds of artefacts. But none of them synthesize feature attributes (or constraints over attributes) despite the practical relevance of attributes for documenting the different values across a range of products. In this report, we develop an algorithm for synthesizing attributed feature models given a set of product descriptions. We present sound, complete, and parametrizable techniques for computing all possible hierarchies, feature groups, placements of feature attributes, domain values, and constraints. We perform a complexity analysis w.r.t. number of features, attributes, configurations, and domain size. We also evaluate the scalability of our synthesis procedure using randomized configuration matrices. This report is a first step that aims to describe the foundations for synthesizing attributed feature models.},
	url = {https://hal.inria.fr/hal-01116663},
	author = {B{\'e}can, Guillaume and Behjati, Razieh and Gotlieb, Arnaud and Acher, Mathieu}
}
@inproceedings {23639,
	title = {Synthesis of attributed feature models from product descriptions},
	journal = {Proceedings of the 19th International Conference on Software Product Line, SPLC{\textquoteright}15, Nashville, USA, July 20-24.},
	year = {2015},
	month = {07/2015},
	pages = {1-10},
	isbn = {978-1-4503-3613-0},
	author = {B{\'e}can, Guillaume and Behjati, Razieh and Gotlieb, Arnaud and Acher, Mathieu}
}
