@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}
}
@inproceedings {43213,
	title = {Constraint-guided Test Execution Scheduling: An Experience Report at ABB Robotics},
	journal = {SAFECOMP2023 42nd International Conference on Computer Safety, Reliability and Security  19-22 September 2023, Toulouse, France},
	year = {2023},
	doi = {10.1007/978-3-031-40923-3_6},
	author = {Gotlieb, Arnaud and Mossige, Morten and Spieker, Helge}
}
@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}
}
@misc {43277,
	title = {Trustworthy AI: Scientific, Industrial, and Societal Impact},
	howpublished = {OsloMet},
	year = {2023},
	type = {Public Outreach},
	author = {Spieker, Helge}
}
@misc {28495,
	title = {AutoCSP - Self-Supervised Neuro-Symbolic Solvers for Constraint Satisfaction},
	howpublished = {Machine Learning and Artificial Intelligence Lab, University of Bonn, Germany},
	year = {2022},
	author = {Spieker, Helge}
}
@article {27688,
	title = {A Fine-grained Data Set and Analysis of Tangling in Bug Fixing Commits},
	journal = {Empirical Software Engineering},
	year = {2022},
	publisher = { Springer},
	doi = {10.1007/s10664-021-10083-5},
	author = {Herbold, Steffen and Trautsch, Alexander and Ledel, Benjamin and Aghamohammadi, Alireza and Taher Ahmed {Ghaleb} and Kuljit Kaur {Chahal} and Bossenmaier, Tim and Nagaria, Bhaveet and Makedonski, Philip and Matin Nili {Ahmadabadi} and Szabados, Kristof and Spieker, Helge and Madeja, Matej and Hoy, Nathaniel and Lenarduzzi, Valentina and Wang, Shangwen and Rodr{\'\i}guez-P{\'e}rez, Gema and Ricardo Colomo {Palacios} and Verdecchia, Roberto and Singh, Paramvir and Qin, Yihao and Chakroborti, Debasish and Davis, Willard and Walunj, Vijay and Wu, Hongjun and Marcilio, Diego and Alam, Omar and Aldaeej, Abdullah and Amit, Idan and Turhan, Burak and Eismann, Simon and Wickert, Anna-Katharina and Malavolta, Ivano and Sulir, Matus and Fard, Fatemeh and Austin Z. {Henley} and Kourtzanidis, Stratos and Tuzun, Eray and Treude, Christoph and Simin Maleki {Shamasbi} and Pashchenko, Ivan and Wyrich, Marvin and Davis, James and Serebrenik, Alexander and Albrecht, Ella and Ethem Utku {Aktas} and Str{\"u}ber, Daniel and Erbel, Johannes}
}
@inproceedings {42689,
	title = {FoCA: Failure-oriented Class Augmentation for Robust Image Classification},
	journal = {The 34th IEEE International Conference on Tools with Artificial Intelligence (ICTAI)},
	year = {2022},
	publisher = { IEEE},
	doi = {10.1109/ICTAI56018.2022.00144},
	url = {https://ieeexplore.ieee.org/abstract/document/10098003},
	author = {Mohit Kumar {Ahuja} and Sahil, Sahil 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}
}
@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 {42550,
	title = {The interplay of AI and software testing for resilient software systems},
	howpublished = {Inria Rennes - Bretagne Atlantique, France},
	year = {2022},
	abstract = {AI-based software are of increasing relevance but also add additional challenges for the software testing process, such as the oracle problem of uncertainty in the precise expected outcome of the software or gigantic numbers of potential test scenarios. At the same time, AI techniques, both data-driven machine learning and logic-driven constraint solving, can aid testing techniques to be more cost-effective and focus on finding faults early. In this talk I will discuss a selection of applications at this intersection, involving metamorphic testing, reinforcement learning, and constraint optimization.},
	author = {Spieker, Helge}
}
@misc {42507,
	title = {Software Engineering: The Next 20 Years},
	howpublished = {Simula 20th Anniversary Celebration},
	year = {2022},
	author = {Spieker, Helge}
}
@inproceedings {33066,
	title = {Testing Deep Learning Models: A First Comparative Study of Multiple Testing Techniques},
	journal = {Artificial Intelligence in Software Testing @ 2022 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)},
	year = {2022},
	publisher = { IEEE},
	abstract = {Deep Learning (DL) has revolutionized the capabilities of vision-based systems (VBS) in critical applications such as autonomous driving, robotic surgery, critical infrastructure surveillance, air and maritime traffic control, etc. By analyzing images, voice, videos, or any type of complex signals, DL has considerably increased the situation awareness of these systems. At the same time, while relying more and more on trained DL models, the reliability and robustness of VBS have been challenged and it has become crucial to test thoroughly these models to assess their capabilities and potential errors. To discover faults in DL models, existing software testing methods have been adapted and refined accordingly. In this article, we provide an overview of these software testing methods, namely differential, metamorphic, mutation, and combinatorial testing, as well as adversarial perturbation testing and review some challenges in their deployment for boosting perception systems used in VBS. We also provide a first experimental comparative study on a classical benchmark used in VBS and discuss its results.},
	isbn = {978-1-6654-9628-5},
	doi = {10.1109/ICSTW55395.2022.00035},
	url = {https://ieeexplore.ieee.org/abstract/document/9787976},
	author = {Mohit Kumar {Ahuja} and Gotlieb, Arnaud and Spieker, Helge}
}
@misc {28487,
	title = {AI4EU Robotics Pilot: Vibration sensor measurements in a robotic wrist},
	year = {2021},
	publisher = {Zenodo},
	doi = {10.5281/zenodo.5729818},
	url = {https://doi.org/10.5281/zenodo.5729818},
	author = {Mossige, Morten and Ramachandran, Ragesh and Spieker, Helge}
}
@misc {28488,
	title = {AI4EU Robotics Pilot: Vibration sensor measurements in a robotic pump},
	year = {2021},
	publisher = {Zenodo},
	doi = {10.5281/zenodo.5729187},
	url = {https://doi.org/10.5281/zenodo.5729187},
	author = {Mossige, Morten and Ramachandran, Ragesh and Spieker, Helge}
}
@inproceedings {27850,
	title = {Apprentissage auto-supervis{\'e} pour la d{\'e}tection d{\textquoteright}actions ill{\'e}gales lors de la surveillance du trafic maritime},
	journal = {Applications Pratiques de l{\textquoteright}Intelligence Artificielle},
	year = {2021},
	publisher = {AFIA},
	author = {Bernab{\'e}, Pierre and Gotlieb, Arnaud and Legeard, Bruno and Frank Olaf {Sem-Jacobsen} and Spieker, Helge}
}
@inproceedings {27804,
	title = {Constraint-Guided Reinforcement Learning: Augmenting the Agent-Environment-Interaction},
	journal = {International Joint Conference on Neural Networks (IJCNN)},
	year = {2021},
	publisher = {IEEE},
	isbn = {978-1-6654-3900-8},
	issn = {2161-4407},
	doi = {10.1109/IJCNN52387.2021.9533996},
	author = {Spieker, Helge}
}
@misc {27717,
	title = {Encoding Temporal and Spatial Vessel Context using Self-Supervised Learning Model (Student Abstract)},
	year = {2021},
	address = {AAAI Conference on Artificial Intelligence (AAAI-21), Student Abstract and Poster Program},
	author = {Bernab{\'e}, Pierre and Spieker, Helge and Gotlieb, Arnaud and Legeard, Bruno}
}
@inproceedings {27552,
	title = {Encoding Temporal and Spatial Vessel Context using Self-Supervised Learning Model (Student Abstract)},
	journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
	volume = {35},
	year = {2021},
	pages = {15757-15758},
	publisher = {AAAI},
	author = {Bernab{\'e}, Pierre and Spieker, Helge and Legeard, Bruno and Gotlieb, Arnaud}
}
@inproceedings {27711,
	title = {Improving the Reliability of Autonomous Software Systems through  Metamorphic Testing},
	journal = {Proceedings of the 31st European Safety and Reliability Conference (ESREL)},
	year = {2021},
	pages = {1-page abstract},
	publisher = {ESREL},
	isbn = {ISBN: 978-981-18-2016-8},
	author = {Mohit Kumar {Ahuja} and Mohamed Bachir {Belaid} and Bernab{\'e}, Pierre and Gotlieb, Arnaud and Marijan, Dusica and Sharif, Aizaz and Spieker, Helge},
	editor = {Castanier, Bruno and Cepin, Marko and Bigaud, David and Berenguer, Christophe}
}
@misc {27532,
	title = {Learning to Generate Fault-revealing Test Cases in Metamorphic Testing},
	howpublished = {Software Engineering 2021},
	year = {2021},
	publisher = {Gesellschaft f{\"u}r Informatik e.V.},
	address = {Bonn},
	doi = {10.18420/SE2021_37},
	url = {https://dl.gi.de/handle/20.500.12116/34533},
	author = {Spieker, Helge and Gotlieb, Arnaud}
}
@article {28099,
	title = {Predictive Machine Learning of Objective Boundaries for Solving COPs},
	journal = {AI},
	volume = {2},
	year = {2021},
	month = {12/2021},
	pages = {527 - 551},
	publisher = {MDPI},
	address = {Basel/Switzerland},
	doi = {10.3390/ai2040033},
	url = {https://www.mdpi.com/2673-2688/2/4/33},
	author = {Spieker, Helge and Gotlieb, Arnaud}
}
@misc {27606,
	title = {Summary of: Adaptive Metamorphic Testing with Contextual Bandits},
	howpublished = {IEEE International Conference on Software Testing (ICST)},
	year = {2021},
	type = {Journal-First Track},
	author = {Spieker, Helge and Gotlieb, Arnaud}
}
@inbook {27608,
	title = {Testing Industrial Robotic Systems: A New Battlefield!},
	booktitle = {Software Engineering for Robotics},
	year = {2021},
	month = {06/2021},
	pages = {109-137},
	publisher = {Springer Nature},
	organization = {Springer Nature},
	chapter = {5},
	address = {Cham},
	isbn = {ISBN 978-3-030-66493-0},
	url = {https://www.springer.com/gp/book/9783030664930$\#$aboutBook},
	author = {Gotlieb, Arnaud and Marijan, Dusica and Spieker, Helge},
	editor = {Cavalcanti, Ana and Dongol, Brijesh and Hierons, Robert and Timmis, Jon and Woodcock, Jim}
}
@article {27259,
	title = {Adaptive Metamorphic Testing with Contextual Bandits},
	journal = {Journal of Systems and Software},
	volume = {165},
	year = {2020},
	month = {07/2020},
	publisher = {Elsevier},
	abstract = {Metamorphic Testing is a software testing paradigm which aims at using necessary properties of a system-under-test, called metamorphic relations, to either check its expected outputs, or to generate new test cases. Metamorphic Testing has been successful to test programs for which a full oracle is not available or to test programs for which there are uncertainties on expected outputs such as learning systems. In this article, we propose Adaptive Metamorphic Testing as a generalization of a simple yet powerful reinforcement learning technique, namely contextual bandits, to select one of the multiple metamorphic relations available for a program. By using contextual bandits, Adaptive Metamorphic Testing learns which metamorphic relations are likely to transform a source test case, such that it has higher chance to discover faults. We present experimental results over two major case studies in machine learning, namely image classification and object detection, and identify weaknesses and robustness boundaries. Adaptive Metamorphic Testing efficiently identifies weaknesses of the tested systems in context of the source test case.},
	doi = {10.1016/j.jss.2020.110574},
	author = {Spieker, Helge and Gotlieb, Arnaud}
}
@article {27357,
	title = {ITE: A Lightweight Implementation of Stratified Reasoning for Constructive Logical Operators},
	journal = {International Journal on Artificial Intelligence Tools},
	volume = {29},
	year = {2020},
	month = {06/2020},
	pages = {23},
	publisher = {World Scientific Publishing},
	doi = {10.1142/S0218213020600064.},
	author = {Gotlieb, Arnaud and Marijan, Dusica and Spieker, Helge}
}
@inproceedings {27342,
	title = {Learning Objective Boundaries for Constraint Optimization Problems},
	journal = {International Conference on Machine Learning, Optimization, and Data Science},
	year = {2020},
	month = {01/2021},
	pages = {394-408},
	publisher = { Springer},
	doi = {10.1007/978-3-030-64580-9_33},
	author = {Spieker, Helge and Gotlieb, Arnaud}
}
@inproceedings {27341,
	title = {Opening the Software Engineering Toolbox for the Assessment of Trustworthy AI},
	journal = {1st International Workshop on New Foundations for Human-Centered AI @ ECAI 2020},
	year = {2020},
	month = {08/2020},
	pages = {67-70},
	publisher = {CEUR Workshop Proceedings},
	abstract = {Trustworthiness is a central requirement for the acceptance and success of human-centered artificial intelligence (AI). To deem an AI system as trustworthy, it is crucial to assess its behaviour and characteristics against a gold standard of Trustworthy AI, consisting of guidelines, requirements, or only expectations. While AI systems are highly complex, their implementations are still based on software. The software engineering community has a long-established toolbox for the assessment of software systems, especially in the context of software testing. In this paper, we argue for the application of software engineering and testing practices for the assessment of trustworthy AI. We make the connection between the seven key requirements as defined by the European Commission{\textquoteright}s AI high-level expert group and established procedures from software engineering and raise questions for future work.},
	url = {http://ceur-ws.org/Vol-2659/ahuja.pdf},
	author = {Mohit Kumar {Ahuja} and Mohamed Bachir {Belaid} and Bernab{\'e}, Pierre and Collet, Mathieu and Gotlieb, Arnaud and Lal, Chhagan and Marijan, Dusica and Sen, Sagar and Sharif, Aizaz and Spieker, Helge},
	editor = {Saffiotti, Alessandro and Serafini, Luciano and Lukowicz, Paul}
}
@phdthesis {27452,
	title = {Software Testing in Continuous Integration with Machine Learning and Constraint Optimization},
	volume = {PhD},
	year = {2020},
	month = {09/2020},
	abstract = {Frequent automated software testing is a crucial task for modern software development. It has the goal to evaluate a software{\textquoteright}s functionality and be confident about its quality after recent changes and before the integration of new features or its deployment into the actual production environment.Further challenges are introduced when testing software for cyber-physical systems that integrate both software and dedicated hardware components, e.g. industrial robots or embedded devices.This thesis explores how machine learning and constraint optimization can be leveraged to achieve the desired efficiency and to create an intelligent testing process. Specifically, we contribute new methodology for the test suite optimization process of test case prioritization, test case scheduling, and test case selection and assignment. All of these steps are relevant to decide which test cases are most relevant and when to execute them on which test hardware, e.g. an industrial robot. The results of this thesis have been published in international venues and are in production usage at our industrial partner, ABB Robotics Norway.},
	isbn = {1501-7710},
	url = {https://www.mn.uio.no/ifi/forskning/aktuelt/arrangementer/disputaser/2020/spieker.html},
	author = {Spieker, Helge}
}
@misc {27009,
	title = {Deployment and Evolution of Machine Learning Artifacts: Research Perspectives},
	howpublished = {14th Certus User Partner Workshop (UPW), Larvik, Norway},
	year = {2019},
	author = {Spieker, Helge}
}
@misc {27008,
	title = {How ABB and Certus work together to build better continuous integration testing of cyber-physical systems},
	howpublished = {14th Certus User Partner Workshop (UPW), Larvik, Norway},
	year = {2019},
	author = {Spieker, Helge}
}
@misc {42564,
	title = {Intelligent Software Testing with Reinforcement Learning and Constraint Programming},
	howpublished = {Fraunhofer IAIS, Sankt Augustin, Germany},
	year = {2019},
	abstract = {Complex software systems require frequent testing to ensure their functionality and to discover flaws in their implementation early after they have been introduced. However, executing all tests after each change is often not feasible, especially when physical test agents with limited resources, e.g. industrial robots, are involved. In this talk, we discuss the usage of machine learning and constraint programming for efficient test management. We see how test cases are prioritized with reinforcement learning, and how the test plan is scheduled, such that tests are executed on different physical systems over time.},
	author = {Spieker, Helge}
}
@inproceedings {26586,
	title = {Learning Agents of Bounded Rationality: Rewards Based on Fair Equilibria},
	journal = {31st Swedish AI Society Workshop (SAIS)},
	year = {2019},
	month = {06/2019},
	publisher = {Swedish AI Society},
	address = {Ume{\r a}, Sweden},
	url = {https://sais2019.cs.umu.se/},
	author = {Kampik, Timotheus and Spieker, Helge}
}
@inproceedings {26201,
	title = {Rotational Diversity in Multi-Cycle Assignment Problems},
	journal = {Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-19)},
	volume = {33},
	year = {2019},
	pages = {7724-7731},
	publisher = {AAAI},
	author = {Spieker, Helge and Gotlieb, Arnaud and Mossige, Morten}
}
@techreport {26810,
	title = {SARCoS: Machine Learning Models for the Selection of Security Test Scenarios},
	year = {2019},
	publisher = {Simula Research Laboratory},
	author = {Gotlieb, Arnaud and Bernab{\'e}, Pierre and Marijan, Dusica and Spieker, Helge}
}
@misc {27720,
	title = {Statistics AIS Dataset from Statsat},
	howpublished = {Simula Research Laboratory},
	year = {2019},
	author = {Bernab{\'e}, Pierre and Spieker, Helge and Gotlieb, Arnaud and Marijan, Dusica and Legeard, Bruno}
}
@techreport {26779,
	title = {Survey on Testing of Deep Learning Systems},
	year = {2019},
	publisher = {Simula Research Laboratory},
	abstract = {Recent studies have shown that deep learning algorithms used for image classification or object recognition are not sufficiently reliable. These algorithms can be easily fooled by applying perturbations to images or generating artificial images that result in misclassification. In this paper, we provide an overview of software testing methods present in literature to test deep learning systems. We have explored different methods of testing deep neural networks, namely metamorphic testing, mutation testing, differential testing, and adversarial perturbation testing. We present the main findings available from the literature and compare these methods systematically and comprehensively. The results show that systematic testing of deep learning systems can further help to increase the performance of state-of-the-art systems.},
	author = {Mohit Kumar {Ahuja} and Spieker, Helge and Gotlieb, Arnaud and Marijan, Dusica and Mossige, Morten}
}
@misc {26270,
	title = {Time-aware Test Execution Scheduling for Cyber-Physical Systems},
	howpublished = {Gesellschaft f{\"u}r Informatik Software Engineering Conference 2019 (SE19), Germany},
	year = {2019},
	author = {Mossige, Morten and Gotlieb, Arnaud and Spieker, Helge and Meling, Hein and Carlsson, Mats}
}
@misc {26206,
	title = {Towards Sequence-to-Sequence Reinforcement Learning for Constraint Solving with Constraint-Based Local Search},
	year = {2019},
	type = {Student Abstract},
	address = {Conference on Artificial Intelligence (AAAI-19), Hawaii, USA},
	author = {Spieker, Helge}
}
@misc {26229,
	title = {Boundary Estimation: Learning Boundaries for Constraint Optimization Problems},
	howpublished = {International Symposium on Mathematical Optimization (ISMP{\textquoteright}18), Bordeaux, France},
	year = {2018},
	type = {Invited Talk},
	keywords = {learning theory, Optimization},
	author = {Gotlieb, Arnaud and Spieker, Helge}
}
@misc {25656,
	title = {Different Cycle, Different Assignment: Diversity in Assignment Problems with Multiple Cycles},
	year = {2018},
	type = {Student Abstract},
	address = {AAAI-18, New Orleans, Louisiana, USA},
	abstract = {We present approaches to handle diverse assignments in\&nbsp;multi-cycle assignment problems. The goal is to assign a task to different agents in each cycle, such that all possible combinations are made over time. Our method combines the original profit value, that is to be optimized by the assignment problem with an additional assignment preference. By merging both, we steer the optimization towards diverse assignments without large trade-offs in the original profits.},
	author = {Spieker, Helge and Gotlieb, Arnaud and Mossige, Morten}
}
@misc {25988,
	title = {Estimating Objective Boundaries for Constraint Optimization Problems},
	howpublished = {NordConsNet Workshop, Gothenburg, Sweden},
	year = {2018},
	abstract = {Solving Constraint Optimization Problems (COP) requires exploring a large search space. By providing objective boundaries, this space can be pruned. Finding close boundaries, that correctly under- or overestimate the optimum, is difficult without having a heuristic function for the COP. We present a method for learning to estimate boundaries from problem instances using machine learning. The trained model can then estimate boundaries for unseen instances and thereby support the constraint solver through an additional boundary constraint.},
	author = {Spieker, Helge and Gotlieb, Arnaud}
}
@misc {25657,
	title = {Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration},
	howpublished = {Gesellschaft f{\"u}r Informatik Software Engineering Conference 2018 (SE18), Ulm, Germany},
	year = {2018},
	author = {Spieker, Helge and Gotlieb, Arnaud and Marijan, Dusica and Mossige, Morten}
}
@inproceedings {26109,
	title = {Stratified Constructive Disjunction and Negation in Constraint Programming},
	journal = {Proc. of IEEE Int. Conf. on Tools with Artificial Intelligence (ICTAI-18). Volos, Greece. Nov. 2018},
	year = {2018},
	pages = {106-113},
	publisher = {IEEE},
	abstract = {Constraint Programming (CP) is a powerful declarative programming paradigm combining inference and search in order to find solutions to various type of constraint systems. Dealing with highly disjunctive constraint systems is notoriously difficult in CP. Apart from trying to solve each disjunct independently from each other, there is little hope and effort to succeed in constructing intermediate results combining the knowledge originating from several disjuncts. In this paper, we propose If Then Else (ITE), a lightweight approach for implementing stratified constructive disjunction and negation on top of an existing CP solver, namely SICStus Prolog clp(FD). Although constructive disjunction is known for more than three decades, it does not have straightforward implementations in most CP solvers. ITE is a freely available library proposing stratified and constructive reasoning for various operators, including disjunction and negation, implication and conditional. Our preliminary experimental results show that ITE is competitive with existing approaches that handle disjunctive constraint systems.},
	doi = {10.1109/ICTAI.2018.00026},
	author = {Gotlieb, Arnaud and Marijan, Dusica and Spieker, Helge}
}
@inproceedings {25989,
	title = {Towards Hybrid Constraint Solving with Reinforcement Learning and Constraint-Based Local Search},
	journal = {Data Science meets Optimization Workshop at Federated Artificial Intelligence Meeting},
	year = {2018},
	author = {Spieker, Helge and Gotlieb, Arnaud}
}
@inproceedings {25382,
	title = {Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration},
	journal = {Proceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis},
	year = {2017},
	pages = {12-22},
	publisher = {ACM},
	address = {New York, NY, USA},
	isbn = {978-1-4503-5076-1},
	doi = {10.1145/3092703.3092709},
	author = {Spieker, Helge and Gotlieb, Arnaud and Marijan, Dusica and Mossige, Morten}
}
@inproceedings {25440,
	title = {Time-aware Test Case Execution Scheduling for Cyber-Physical Systems},
	journal = {Proceedings of Principles of Constraint Programming (CP{\textquoteright}17)},
	year = {2017},
	publisher = { Springer},
	abstract = {Testing cyber-physical systems involves the execution of test cases on target-machines equipped with the latest release of a software control system. When testing industrial robots, it is common that the target machines need to share some common resources, e.g., costly hardware devices, and so there is a need to schedule test case execution on the target machines, accounting for these shared resources. With a large number of such tests executed on a regular basis, this scheduling becomes difficult to manage manually. In fact, with manual test execution planning and scheduling, some robots may remain unoccupied for long periods of time and some test cases may not be executed. This paper introduces TC-Sched, a time-aware method for automated test case execution scheduling. TC-Sched uses Constraint Programming to schedule tests to run on multiple machines constrained by the tests{\textquoteright} access to shared resources, such as measurement or networking devices. The CP model is written in SICStus Prolog and uses the Cumulatives global constraint. Given a set of test cases, a set of machines, and a set of shared resources, TC-Sched produces an execution schedule where each test is executed once with minimal time between when a source code change is committed and the test results are reported to the developer. Experiments reveal that TC-Sched can schedule 500 test cases over 100 machines in less than 4 minutes for 99.5\% of the instances. In addition, TC-Sched largely outperforms simpler methods based on a greedy algorithm and is suitable for deployment on industrial robot testing.},
	doi = {10.1007/978-3-319-66158-2_25},
	author = {Mossige, Morten and Gotlieb, Arnaud and Spieker, Helge and Meling, Hein and Carlsson, Mats}
}
@article {24979,
	title = {Multi-stage evolution of single- and multi-objective MCLP},
	journal = {Soft Computing},
	year = {2016},
	month = {10/2016},
	pages = {1{\textendash}14},
	publisher = { Springer},
	abstract = {Maximal covering location problems have efficiently been solved using evolutionary computation. The multi-stage placement of charging stations for electric cars is an instance of this problem which is addressed in this study. It is particularly challenging, because a final solution is constructed in multiple steps, stations cannot be relocated easily and intermediate solutions should be optimal with respect to certain objectives. This paper is an extended version of work published in Spieker et al. (Innovations in intelligent systems and applications (INISTA), 2015 international symposium on. IEEE, pp 1\&ndash;7, 2015). In this work, it was shown that through problem decomposition, an incremental genetic algorithm benefits from having multiple intermediate stages. On the other hand, a decremental strategy does not profit from reduced computational complexity. We extend our previous work by including multi-objective optimization of multi-stage charging station placement, allowing us to not only optimize toward (weighted) demand location coverage, but also to include a second objective, taking into account traffic density. It is shown that the reachable part of the full Pareto front at each stage is bound by the solution that was chosen from the respective previous front. By careful choice of the selection strategy, a particular focus can be set. This can be exploited to comply with concrete implementation goals and to adjust the evolved strategy to both static and dynamic changes in requirements.},
	issn = {1433-7479},
	doi = {10.1007/s00500-016-2374-9},
	url = {http://dx.doi.org/10.1007/s00500-016-2374-9},
	author = {Spieker, Helge and Gaier, Adam and Meilinger, Stefanie and Asteroth, Alexander}
}
@inproceedings {7276733,
	title = {Successive evolution of charging station placement},
	journal = {2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA)},
	year = {2015},
	month = {09/2015},
	pages = {1-7},
	publisher = { IEEE},
	abstract = {An evolving strategy for a multi-stage placement of charging stations for electrical cars is developed. Both an incremental as well as a decremental placement decomposition are evaluated on this Maximum Covering Location Problem. We show that an incremental Genetic Algorithm benefits from problem decomposition effects of having multiple stages and shows greedy behaviour.},
	keywords = {automobiles, charging station placement, Charging stations, decremental placement decomposition, electric vehicles, electrical cars, facility location, genetic algorithms, greedy behaviour, Heuristic algorithms, incremental genetic algorithm, incremental placement decomposition, maximum covering location problem, multistage placement, Optimization, Planning, problem decomposition effects, successive evolution},
	doi = {10.1109/INISTA.2015.7276733},
	author = {Spieker, Helge and Hagg, Alexander and Asteroth, Alexander and Meilinger, Stefanie and Jacobs, Volker and Oslislo, Alexander}
}
