Projects
AutoCSP: Self-Supervised Neuro-Symbolic Solvers for Constraint Satisfaction
The AutoCSP project addresses problems where the solution is a combination of multiple decision variables, so-called combinatorial optimization problems. The goal is to find a combination that fulfils all constraints that the problem has while optimizing an objective function. Examples for these problems include machine or job scheduling, timetabling, lot sizing, or vehicle routing, all of which are highly relevant in industrial settings like production planning or delivery scheduling and are embedded in intelligent decision support systems.
The state-of-the-art technique to solve these problems is dedicated constraint solvers, which are highly optimized and have been investigated for a long time. Still, searching for good or even optimal solutions often is time-consuming due to the enormous number of possible combinations that need to be explored while facing strong restrictions on which combinations form a feasible solution. This is especially true when the same problem has to be repeatedly solved with different inputs, for example in daily production planning or fleet scheduling tasks. Even though the general problem stays the same, experiences from earlier solutions are not used to solve new instances faster.
AutoCSP advances the scientific knowledge and state-of-the-art through problem-specific solvers that combine data-driven machine learning (ML) models and logic-driven constraint solvers in a hybrid intelligent system. These solvers are automatically generated from a constraint model, i.e. the description of the problem to be solved, and are self-taught to solve constraint satisfaction and optimization problems while maintaining correctness and time-efficiency. To achieve this goal, the project investigates a) how to generate training data just from the problem description, b) how to present the data such that the ML model understands it, c) how to efficiently learn from this data, and d) how to bring everything together in one system.
The AutoCSP project is funded by the Norwegian Research Council as a three-year research project with international mobility under grant number 324674. The research is carried out in collaboration with the [Machine Learning and Artificial Intelligence Lab](https://mlai.cs.uni-bonn.de/) at the University of Bonn, Germany.
Partners
University of Bonn
Website
Publications for AutoCSP: Self-Supervised Neuro-Symbolic Solvers for Constraint Satisfaction
Talks, invited
Trustworthy AI: Scientific, Industrial, and Societal Impact
In OsloMet, 2023.Status: Published
Trustworthy AI: Scientific, Industrial, and Societal Impact
Afilliation | Software Engineering |
Project(s) | Department of Validation Intelligence for Autonomous Software Systems, AutoCSP: Self-Supervised Neuro-Symbolic Solvers for Constraint Satisfaction |
Publication Type | Talks, invited |
Year of Publication | 2023 |
Location of Talk | OsloMet |
Type of Talk | Public Outreach |
Proceedings, refereed
FoCA: Failure-oriented Class Augmentation for Robust Image Classification
In The 34th IEEE International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2022.Status: Published
FoCA: Failure-oriented Class Augmentation for Robust Image Classification
Afilliation | Software Engineering |
Project(s) | Department of Validation Intelligence for Autonomous Software Systems, Testing of Learning Robots (T-LARGO) , Testing of Learning Robots (T-Largo), AutoCSP: Self-Supervised Neuro-Symbolic Solvers for Constraint Satisfaction |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | The 34th IEEE International Conference on Tools with Artificial Intelligence (ICTAI) |
Publisher | IEEE |
URL | https://ieeexplore.ieee.org/abstract/document/10098003 |
DOI | 10.1109/ICTAI56018.2022.00144 |
GEQCA: Generic Qualitative Constraint Acquisition
In Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 36. AAAI, 2022.Status: Published
GEQCA: Generic Qualitative Constraint Acquisition
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.
Afilliation | Software Engineering |
Project(s) | Testing of Learning Robots (T-LARGO) , Testing of Learning Robots (T-Largo), Department of Validation Intelligence for Autonomous Software Systems, AutoCSP: Self-Supervised Neuro-Symbolic Solvers for Constraint Satisfaction |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | Proceedings of the AAAI Conference on Artificial Intelligence |
Volume | 36 |
Number of Volumes | 4 |
Pagination | 3690-3697 |
Date Published | 06/2022 |
Publisher | AAAI |
URL | https://ojs.aaai.org/index.php/AAAI/article/view/20282 |
DOI | 10.1609/aaai.v36i4.20282 |
Talks, invited
The interplay of AI and software testing for resilient software systems
In Inria Rennes - Bretagne Atlantique, France, 2022.Status: Published
The interplay of AI and software testing for resilient software systems
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.
Afilliation | Software Engineering |
Project(s) | AutoCSP: Self-Supervised Neuro-Symbolic Solvers for Constraint Satisfaction, Department of Validation Intelligence for Autonomous Software Systems |
Publication Type | Talks, invited |
Year of Publication | 2022 |
Location of Talk | Inria Rennes - Bretagne Atlantique, France |
Software Engineering: The Next 20 Years
In Simula 20th Anniversary Celebration, 2022.Status: Published
Software Engineering: The Next 20 Years
Afilliation | Software Engineering |
Project(s) | Department of Validation Intelligence for Autonomous Software Systems, AutoCSP: Self-Supervised Neuro-Symbolic Solvers for Constraint Satisfaction |
Publication Type | Talks, invited |
Year of Publication | 2022 |
Location of Talk | Simula 20th Anniversary Celebration |
AutoCSP - Self-Supervised Neuro-Symbolic Solvers for Constraint Satisfaction
In Machine Learning and Artificial Intelligence Lab, University of Bonn, Germany, 2022.Status: Published
AutoCSP - Self-Supervised Neuro-Symbolic Solvers for Constraint Satisfaction
Afilliation | Software Engineering |
Project(s) | AutoCSP: Self-Supervised Neuro-Symbolic Solvers for Constraint Satisfaction, Department of Validation Intelligence for Autonomous Software Systems |
Publication Type | Talks, invited |
Year of Publication | 2022 |
Location of Talk | Machine Learning and Artificial Intelligence Lab, University of Bonn, Germany |
Publications
Proceedings, refereed
A Review of Validation and Verification of Neural Network-based Policies for Sequential Decision Making
In Rencontres des Jeunes Chercheurs en Intelligence Artificielle, 2023.Status: Published
A Review of Validation and Verification of Neural Network-based Policies for Sequential Decision Making
In sequential decision making, neural networks (NNs) are nowadays commonly used to represent and learn the agent’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.
Afilliation | Software Engineering |
Project(s) | Department of Validation Intelligence for Autonomous Software Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2023 |
Conference Name | Rencontres des Jeunes Chercheurs en Intelligence Artificielle |
Keywords | Neural Networks, Sequential decision making, Software Testing |
URL | https://pfia23.icube.unistra.fr/conferences/rjcia/Actes/RJCIA2023_paper_... |
Approche générique pour l’acquisition de contraintes qualitatives
In JFPC, 2023.Status: Published
Approche générique pour l’acquisition de contraintes qualitatives
De nombreux problèmes de planification et d’ordonnance-
ment impliquent la conception de subtiles combinaisons lo-
giques de contraintes temporelles ou spatiales. D’une part,
la modélisation précise de ces contraintes, qui sont formu-
lées dans diverses algèbres de relations, implique un cer-
tain nombre de combinaisons logiques possibles et néces-
site une expertise en modélisation basée sur les contraintes.
D’autre part, l’acquisition active de contraintes (AC) a été
utilisée avec succès pour aider les utilisateurs non expé-
rimentés à apprendre les réseaux de contraintes conjonc-
tives par la génération d’une séquence de requêtes. Dans
cet article, nous proposons GEQCA, pour Generic Quali-
tative Constraint Acquisition, une méthode d’AC active qui
apprend les contraintes qualitatives via le concept de re-
quêtes qualitatives. GEQCA combine les requêtes quali-
tatives avec la cohérence de chemin limitée dans le temps
(PC pour Path Consistency) et la propagation des connais-
sances de base pour acquérir les contraintes qualitatives.
Nous prouvons la correction, la complétude et la terminai-
son de GEQCA et nous donnons une évaluation expéri-
mentale qui montre (i) l’efficacité de notre approche dans
l’apprentissage des contraintes temporelles et, (ii) l’utilisa-
tion de GEQCA sur des instances réelles d’ordonnance-
ment.
Afilliation | Software Engineering |
Project(s) | Department of Validation Intelligence for Autonomous Software Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2023 |
Conference Name | JFPC |
Constraint-guided Test Execution Scheduling: An Experience Report at ABB Robotics
In SAFECOMP2023 42nd International Conference on Computer Safety, Reliability and Security 19-22 September 2023, Toulouse, France, 2023.Status: Published
Constraint-guided Test Execution Scheduling: An Experience Report at ABB Robotics
Afilliation | Software Engineering |
Project(s) | Department of Validation Intelligence for Autonomous Software Systems, Testing of Learning Robots (T-LARGO) |
Publication Type | Proceedings, refereed |
Year of Publication | 2023 |
Conference Name | SAFECOMP2023 42nd International Conference on Computer Safety, Reliability and Security 19-22 September 2023, Toulouse, France |
Other Numbers | arXiv:2306.01529 |
DOI | 10.1007/978-3-031-40923-3_6 |
Technical reports
Acquiring Qualitative Explainable Graphs for Automated Driving Scene Interpretation
Simula, 2023.Status: Published
Acquiring Qualitative Explainable Graphs for Automated Driving Scene Interpretation
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.
Afilliation | Software Engineering |
Project(s) | Department of Validation Intelligence for Autonomous Software Systems, AI4CCAM: Trustworthy AI for Cooperative, Connected & Automated Mobility |
Publication Type | Technical reports |
Year of Publication | 2023 |
Publisher | Simula |
Other Numbers | arXiv:2308.12755 |
Talks, invited
Trustworthy AI: Scientific, Industrial, and Societal Impact
In OsloMet, 2023.Status: Published
Trustworthy AI: Scientific, Industrial, and Societal Impact
Afilliation | Software Engineering |
Project(s) | Department of Validation Intelligence for Autonomous Software Systems, AutoCSP: Self-Supervised Neuro-Symbolic Solvers for Constraint Satisfaction |
Publication Type | Talks, invited |
Year of Publication | 2023 |
Location of Talk | OsloMet |
Type of Talk | Public Outreach |
Journal Article
A Fine-grained Data Set and Analysis of Tangling in Bug Fixing Commits
Empirical Software Engineering (2022).Status: Published
A Fine-grained Data Set and Analysis of Tangling in Bug Fixing Commits
Afilliation | Software Engineering |
Project(s) | Department of Validation Intelligence for Autonomous Software Systems |
Publication Type | Journal Article |
Year of Publication | 2022 |
Journal | Empirical Software Engineering |
Publisher | Springer |
Other Numbers | arXiv:2011.06244 |
DOI | 10.1007/s10664-021-10083-5 |
Talks, invited
AutoCSP - Self-Supervised Neuro-Symbolic Solvers for Constraint Satisfaction
In Machine Learning and Artificial Intelligence Lab, University of Bonn, Germany, 2022.Status: Published
AutoCSP - Self-Supervised Neuro-Symbolic Solvers for Constraint Satisfaction
Afilliation | Software Engineering |
Project(s) | AutoCSP: Self-Supervised Neuro-Symbolic Solvers for Constraint Satisfaction, Department of Validation Intelligence for Autonomous Software Systems |
Publication Type | Talks, invited |
Year of Publication | 2022 |
Location of Talk | Machine Learning and Artificial Intelligence Lab, University of Bonn, Germany |
Software Engineering: The Next 20 Years
In Simula 20th Anniversary Celebration, 2022.Status: Published
Software Engineering: The Next 20 Years
Afilliation | Software Engineering |
Project(s) | Department of Validation Intelligence for Autonomous Software Systems, AutoCSP: Self-Supervised Neuro-Symbolic Solvers for Constraint Satisfaction |
Publication Type | Talks, invited |
Year of Publication | 2022 |
Location of Talk | Simula 20th Anniversary Celebration |
The interplay of AI and software testing for resilient software systems
In Inria Rennes - Bretagne Atlantique, France, 2022.Status: Published
The interplay of AI and software testing for resilient software systems
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.
Afilliation | Software Engineering |
Project(s) | AutoCSP: Self-Supervised Neuro-Symbolic Solvers for Constraint Satisfaction, Department of Validation Intelligence for Autonomous Software Systems |
Publication Type | Talks, invited |
Year of Publication | 2022 |
Location of Talk | Inria Rennes - Bretagne Atlantique, France |
Proceedings, refereed
FoCA: Failure-oriented Class Augmentation for Robust Image Classification
In The 34th IEEE International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2022.Status: Published
FoCA: Failure-oriented Class Augmentation for Robust Image Classification
Afilliation | Software Engineering |
Project(s) | Department of Validation Intelligence for Autonomous Software Systems, Testing of Learning Robots (T-LARGO) , Testing of Learning Robots (T-Largo), AutoCSP: Self-Supervised Neuro-Symbolic Solvers for Constraint Satisfaction |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | The 34th IEEE International Conference on Tools with Artificial Intelligence (ICTAI) |
Publisher | IEEE |
URL | https://ieeexplore.ieee.org/abstract/document/10098003 |
DOI | 10.1109/ICTAI56018.2022.00144 |
GEQCA: Generic Qualitative Constraint Acquisition
In Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 36. AAAI, 2022.Status: Published
GEQCA: Generic Qualitative Constraint Acquisition
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.
Afilliation | Software Engineering |
Project(s) | Testing of Learning Robots (T-LARGO) , Testing of Learning Robots (T-Largo), Department of Validation Intelligence for Autonomous Software Systems, AutoCSP: Self-Supervised Neuro-Symbolic Solvers for Constraint Satisfaction |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | Proceedings of the AAAI Conference on Artificial Intelligence |
Volume | 36 |
Number of Volumes | 4 |
Pagination | 3690-3697 |
Date Published | 06/2022 |
Publisher | AAAI |
URL | https://ojs.aaai.org/index.php/AAAI/article/view/20282 |
DOI | 10.1609/aaai.v36i4.20282 |
Testing Deep Learning Models: A First Comparative Study of Multiple Testing Techniques
In Artificial Intelligence in Software Testing @ 2022 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). IEEE, 2022.Status: Published
Testing Deep Learning Models: A First Comparative Study of Multiple Testing Techniques
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.
Afilliation | Software Engineering |
Project(s) | Department of Validation Intelligence for Autonomous Software Systems, Testing of Learning Robots (T-LARGO) , Testing of Learning Robots (T-Largo) |
Publication Type | Proceedings, refereed |
Year of Publication | 2022 |
Conference Name | Artificial Intelligence in Software Testing @ 2022 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW) |
Publisher | IEEE |
ISBN Number | 978-1-6654-9628-5 |
Other Numbers | arXiv:2202.12139 |
URL | https://ieeexplore.ieee.org/abstract/document/9787976 |
DOI | 10.1109/ICSTW55395.2022.00035 |
Talks, contributed
GEQCA: Generic Qualitative Constraint Acquisition
In Lernen. Wissen. Daten. Analysen. (LWDA) - KDML Track, 2022.Status: Published
GEQCA: Generic Qualitative Constraint Acquisition
Afilliation | Software Engineering |
Project(s) | Department of Validation Intelligence for Autonomous Software Systems |
Publication Type | Talks, contributed |
Year of Publication | 2022 |
Location of Talk | Lernen. Wissen. Daten. Analysen. (LWDA) - KDML Track |
URL | https://lwda2022.de |
Miscellaneous
AI4EU Robotics Pilot: Vibration sensor measurements in a robotic pump
Zenodo, 2021.Status: Published
AI4EU Robotics Pilot: Vibration sensor measurements in a robotic pump
Afilliation | Software Engineering |
Project(s) | AI4EU, Department of Validation Intelligence for Autonomous Software Systems |
Publication Type | Miscellaneous |
Year of Publication | 2021 |
Publisher | Zenodo |
URL | https://doi.org/10.5281/zenodo.5729187 |
DOI | 10.5281/zenodo.5729187 |
AI4EU Robotics Pilot: Vibration sensor measurements in a robotic wrist
Zenodo, 2021.Status: Published
AI4EU Robotics Pilot: Vibration sensor measurements in a robotic wrist
Afilliation | Software Engineering |
Project(s) | AI4EU, Department of Validation Intelligence for Autonomous Software Systems |
Publication Type | Miscellaneous |
Year of Publication | 2021 |
Publisher | Zenodo |
URL | https://doi.org/10.5281/zenodo.5729818 |
DOI | 10.5281/zenodo.5729818 |
Proceedings, refereed
Apprentissage auto-supervisé pour la détection d’actions illégales lors de la surveillance du trafic maritime
In Applications Pratiques de l’Intelligence Artificielle. AFIA, 2021.Status: Published
Apprentissage auto-supervisé pour la détection d’actions illégales lors de la surveillance du trafic maritime
Afilliation | Software Engineering, Machine Learning |
Project(s) | T-Sar, Department of Validation Intelligence for Autonomous Software Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2021 |
Conference Name | Applications Pratiques de l’Intelligence Artificielle |
Publisher | AFIA |
Constraint-Guided Reinforcement Learning: Augmenting the Agent-Environment-Interaction
In International Joint Conference on Neural Networks (IJCNN). IEEE, 2021.Status: Published
Constraint-Guided Reinforcement Learning: Augmenting the Agent-Environment-Interaction
Afilliation | Software Engineering |
Project(s) | Department of Validation Intelligence for Autonomous Software Systems, AI4EU |
Publication Type | Proceedings, refereed |
Year of Publication | 2021 |
Conference Name | International Joint Conference on Neural Networks (IJCNN) |
Publisher | IEEE |
ISBN Number | 978-1-6654-3900-8 |
ISSN Number | 2161-4407 |
Other Numbers | arXiv:2104.11918 |
DOI | 10.1109/IJCNN52387.2021.9533996 |
Encoding Temporal and Spatial Vessel Context using Self-Supervised Learning Model (Student Abstract)
In Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35. AAAI, 2021.Status: Published
Encoding Temporal and Spatial Vessel Context using Self-Supervised Learning Model (Student Abstract)
Afilliation | Software Engineering |
Project(s) | T-Sar, Department of Validation Intelligence for Autonomous Software Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2021 |
Conference Name | Proceedings of the AAAI Conference on Artificial Intelligence |
Volume | 35 |
Pagination | 15757-15758 |
Publisher | AAAI |
Improving the Reliability of Autonomous Software Systems through Metamorphic Testing
In Proceedings of the 31st European Safety and Reliability Conference (ESREL). ESREL, 2021.Status: Published
Improving the Reliability of Autonomous Software Systems through Metamorphic Testing
Afilliation | Software Engineering |
Project(s) | Department of Validation Intelligence for Autonomous Software Systems, Testing of Learning Robots (T-LARGO) , Testing of Learning Robots (T-Largo) |
Publication Type | Proceedings, refereed |
Year of Publication | 2021 |
Conference Name | Proceedings of the 31st European Safety and Reliability Conference (ESREL) |
Pagination | 1-page abstract |
Publisher | ESREL |
ISBN Number | ISBN: 978-981-18-2016-8 |
Poster
Encoding Temporal and Spatial Vessel Context using Self-Supervised Learning Model (Student Abstract)
AAAI Conference on Artificial Intelligence (AAAI-21), Student Abstract and Poster Program, 2021.Status: Published
Encoding Temporal and Spatial Vessel Context using Self-Supervised Learning Model (Student Abstract)
Afilliation | Software Engineering, Machine Learning |
Project(s) | Department of Validation Intelligence for Autonomous Software Systems, T-Sar |
Publication Type | Poster |
Year of Publication | 2021 |
Place Published | AAAI Conference on Artificial Intelligence (AAAI-21), Student Abstract and Poster Program |
Talks, contributed
Learning to Generate Fault-revealing Test Cases in Metamorphic Testing
In Software Engineering 2021. Bonn: Gesellschaft für Informatik e.V, 2021.Status: Published
Learning to Generate Fault-revealing Test Cases in Metamorphic Testing
Afilliation | Software Engineering |
Project(s) | Department of Validation Intelligence for Autonomous Software Systems, Testing of Learning Robots (T-LARGO) , Testing of Learning Robots (T-Largo) |
Publication Type | Talks, contributed |
Year of Publication | 2021 |
Location of Talk | Software Engineering 2021 |
Publisher | Gesellschaft für Informatik e.V. |
Place Published | Bonn |
URL | https://dl.gi.de/handle/20.500.12116/34533 |
DOI | 10.18420/SE2021_37 |
Summary of: Adaptive Metamorphic Testing with Contextual Bandits
In IEEE International Conference on Software Testing (ICST), 2021.Status: Published
Summary of: Adaptive Metamorphic Testing with Contextual Bandits
Afilliation | Software Engineering |
Project(s) | Department of Validation Intelligence for Autonomous Software Systems |
Publication Type | Talks, contributed |
Year of Publication | 2021 |
Location of Talk | IEEE International Conference on Software Testing (ICST) |
Type of Talk | Journal-First Track |
Other Numbers | arXiv:1910.00262 |
Journal Article
Predictive Machine Learning of Objective Boundaries for Solving COPs
AI 2, no. 4 (2021): 527-551.Status: Published
Predictive Machine Learning of Objective Boundaries for Solving COPs
Afilliation | Software Engineering |
Project(s) | Department of Validation Intelligence for Autonomous Software Systems, AI4EU, Testing of Learning Robots (T-Largo) |
Publication Type | Journal Article |
Year of Publication | 2021 |
Journal | AI |
Volume | 2 |
Issue | 4 |
Pagination | 527 - 551 |
Date Published | 12/2021 |
Publisher | MDPI |
Place Published | Basel/Switzerland |
Other Numbers | arXiv:2111.03160 |
URL | https://www.mdpi.com/2673-2688/2/4/33 |
DOI | 10.3390/ai2040033 |
Book Chapter
Testing Industrial Robotic Systems: A New Battlefield!
In Software Engineering for Robotics, 109-137. Cham: Springer Nature, 2021.Status: Published
Testing Industrial Robotic Systems: A New Battlefield!
Afilliation | Software Engineering |
Project(s) | Testing of Learning Robots (T-LARGO) , Department of Validation Intelligence for Autonomous Software Systems, AI4EU, Testing of Learning Robots (T-Largo) |
Publication Type | Book Chapter |
Year of Publication | 2021 |
Book Title | Software Engineering for Robotics |
Chapter | 5 |
Pagination | 109-137 |
Date Published | 06/2021 |
Publisher | Springer Nature |
Place Published | Cham |
ISBN Number | ISBN 978-3-030-66493-0 |
URL | https://www.springer.com/gp/book/9783030664930#aboutBook |
Journal Article
Adaptive Metamorphic Testing with Contextual Bandits
Journal of Systems and Software 165 (2020).Status: Published
Adaptive Metamorphic Testing with Contextual Bandits
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.
Afilliation | Software Engineering |
Project(s) | The Certus Centre (SFI), Department of Validation Intelligence for Autonomous Software Systems, Testing of Learning Robots (T-LARGO) , Testing of Learning Robots (T-Largo) |
Publication Type | Journal Article |
Year of Publication | 2020 |
Journal | Journal of Systems and Software |
Volume | 165 |
Date Published | 07/2020 |
Publisher | Elsevier |
Other Numbers | arXiv:1910.00262 |
DOI | 10.1016/j.jss.2020.110574 |
ITE: A Lightweight Implementation of Stratified Reasoning for Constructive Logical Operators
International Journal on Artificial Intelligence Tools 29, no. 3-4 (2020): 23.Status: Published
ITE: A Lightweight Implementation of Stratified Reasoning for Constructive Logical Operators
Afilliation | Software Engineering |
Project(s) | Testing of Learning Robots (T-LARGO) , Testing of Learning Robots (T-Largo), Department of Validation Intelligence for Autonomous Software Systems |
Publication Type | Journal Article |
Year of Publication | 2020 |
Journal | International Journal on Artificial Intelligence Tools |
Volume | 29 |
Issue | 3-4 |
Pagination | 23 |
Date Published | 06/2020 |
Publisher | World Scientific Publishing |
DOI | 10.1142/S0218213020600064. |
Proceedings, refereed
Learning Objective Boundaries for Constraint Optimization Problems
In International Conference on Machine Learning, Optimization, and Data Science. Springer, 2020.Status: Published
Learning Objective Boundaries for Constraint Optimization Problems
Afilliation | Software Engineering |
Project(s) | Department of Validation Intelligence for Autonomous Software Systems |
Publication Type | Proceedings, refereed |
Year of Publication | 2020 |
Conference Name | International Conference on Machine Learning, Optimization, and Data Science |
Pagination | 394-408 |
Date Published | 01/2021 |
Publisher | Springer |
Other Numbers | arXiv:2006.11560 |
DOI | 10.1007/978-3-030-64580-9_33 |
Opening the Software Engineering Toolbox for the Assessment of Trustworthy AI
In 1st International Workshop on New Foundations for Human-Centered AI @ ECAI 2020. CEUR Workshop Proceedings, 2020.Status: Published
Opening the Software Engineering Toolbox for the Assessment of Trustworthy AI
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's AI high-level expert group and established procedures from software engineering and raise questions for future work.
Afilliation | Software Engineering |
Project(s) | Department of Validation Intelligence for Autonomous Software Systems, AI4EU |
Publication Type | Proceedings, refereed |
Year of Publication | 2020 |
Conference Name | 1st International Workshop on New Foundations for Human-Centered AI @ ECAI 2020 |
Pagination | 67-70 |
Date Published | 08/2020 |
Publisher | CEUR Workshop Proceedings |
Other Numbers | arXiv:2007.07768 |
URL | http://ceur-ws.org/Vol-2659/ahuja.pdf |
PhD Thesis
Software Testing in Continuous Integration with Machine Learning and Constraint Optimization
In The University of Oslo. Vol. PhD, 2020.Status: Published
Software Testing in Continuous Integration with Machine Learning and Constraint Optimization
Frequent automated software testing is a crucial task for modern software development. It has the goal to evaluate a software'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.
Afilliation | Software Engineering |
Project(s) | The Certus Centre (SFI), Department of Validation Intelligence for Autonomous Software Systems |
Publication Type | PhD Thesis |
Year of Publication | 2020 |
Degree awarding institution | The University of Oslo |
Degree | PhD |
Date Published | 09/2020 |
ISBN Number | 1501-7710 |
URL | https://www.mn.uio.no/ifi/forskning/aktuelt/arrangementer/disputaser/202... |
Talks, contributed
Deployment and Evolution of Machine Learning Artifacts: Research Perspectives
In 14th Certus User Partner Workshop (UPW), Larvik, Norway, 2019.Status: Published
Deployment and Evolution of Machine Learning Artifacts: Research Perspectives
Afilliation | Software Engineering |
Project(s) | The Certus Centre (SFI) |
Publication Type | Talks, contributed |
Year of Publication | 2019 |
Location of Talk | 14th Certus User Partner Workshop (UPW), Larvik, Norway |
How ABB and Certus work together to build better continuous integration testing of cyber-physical systems
In 14th Certus User Partner Workshop (UPW), Larvik, Norway, 2019.Status: Published
How ABB and Certus work together to build better continuous integration testing of cyber-physical systems
Afilliation | Software Engineering |
Project(s) | The Certus Centre (SFI) |
Publication Type | Talks, contributed |
Year of Publication | 2019 |
Location of Talk | 14th Certus User Partner Workshop (UPW), Larvik, Norway |
Statistics AIS Dataset from Statsat
In Simula Research Laboratory, 2019.Status: Published
Statistics AIS Dataset from Statsat
Afilliation | Software Engineering, Machine Learning |
Project(s) | T-Sar, Department of Validation Intelligence for Autonomous Software Systems |
Publication Type | Talks, contributed |
Year of Publication | 2019 |
Location of Talk | Simula Research Laboratory |
Time-aware Test Execution Scheduling for Cyber-Physical Systems
In Gesellschaft für Informatik Software Engineering Conference 2019 (SE19), Germany, 2019.Status: Published
Time-aware Test Execution Scheduling for Cyber-Physical Systems
Afilliation | Software Engineering |
Project(s) | The Certus Centre (SFI) |
Publication Type | Talks, contributed |
Year of Publication | 2019 |
Location of Talk | Gesellschaft für Informatik Software Engineering Conference 2019 (SE19), Germany |
Talks, invited
Intelligent Software Testing with Reinforcement Learning and Constraint Programming
In Fraunhofer IAIS, Sankt Augustin, Germany, 2019.Status: Published
Intelligent Software Testing with Reinforcement Learning and Constraint Programming
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.
Afilliation | Software Engineering |
Project(s) | The Certus Centre (SFI) |
Publication Type | Talks, invited |
Year of Publication | 2019 |
Location of Talk | Fraunhofer IAIS, Sankt Augustin, Germany |
Proceedings, refereed
Learning Agents of Bounded Rationality: Rewards Based on Fair Equilibria
In 31st Swedish AI Society Workshop (SAIS). Umeå, Sweden: Swedish AI Society, 2019.Status: Published
Learning Agents of Bounded Rationality: Rewards Based on Fair Equilibria
Afilliation | Software Engineering |
Project(s) | The Certus Centre (SFI) |
Publication Type | Proceedings, refereed |
Year of Publication | 2019 |
Conference Name | 31st Swedish AI Society Workshop (SAIS) |
Date Published | 06/2019 |
Publisher | Swedish AI Society |
Place Published | Umeå, Sweden |
URL | https://sais2019.cs.umu.se/ |
Rotational Diversity in Multi-Cycle Assignment Problems
In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-19). Vol. 33. AAAI, 2019.Status: Published
Rotational Diversity in Multi-Cycle Assignment Problems
Afilliation | Software Engineering |
Project(s) | The Certus Centre (SFI) |
Publication Type | Proceedings, refereed |
Year of Publication | 2019 |
Conference Name | Proceedings of the AAAI Conference on Artificial Intelligence (AAAI-19) |
Volume | 33 |
Pagination | 7724-7731 |
Publisher | AAAI |
Other Numbers | arXiv:1811.03496 |
Technical reports
SARCoS: Machine Learning Models for the Selection of Security Test Scenarios
Simula Research Laboratory, 2019.Status: Accepted
SARCoS: Machine Learning Models for the Selection of Security Test Scenarios
Afilliation | Software Engineering |
Project(s) | T-Sar |
Publication Type | Technical reports |
Year of Publication | 2019 |
Publisher | Simula Research Laboratory |
Survey on Testing of Deep Learning Systems
Simula Research Laboratory, 2019.Status: Published
Survey on Testing of Deep Learning Systems
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.
Afilliation | Software Engineering |
Project(s) | Testing of Learning Robots (T-Largo) |
Publication Type | Technical reports |
Year of Publication | 2019 |
Publisher | Simula Research Laboratory |
Poster
Towards Sequence-to-Sequence Reinforcement Learning for Constraint Solving with Constraint-Based Local Search
Conference on Artificial Intelligence (AAAI-19), Hawaii, USA, 2019.Status: Published
Towards Sequence-to-Sequence Reinforcement Learning for Constraint Solving with Constraint-Based Local Search
Afilliation | Software Engineering |
Project(s) | The Certus Centre (SFI) |
Publication Type | Poster |
Year of Publication | 2019 |
Place Published | Conference on Artificial Intelligence (AAAI-19), Hawaii, USA |
Type of Work | Student Abstract |
Talks, invited
Boundary Estimation: Learning Boundaries for Constraint Optimization Problems
In International Symposium on Mathematical Optimization (ISMP'18), Bordeaux, France, 2018.Status: Published
Boundary Estimation: Learning Boundaries for Constraint Optimization Problems
Afilliation | Software Engineering |
Project(s) | The Certus Centre (SFI) |
Publication Type | Talks, invited |
Year of Publication | 2018 |
Location of Talk | International Symposium on Mathematical Optimization (ISMP'18), Bordeaux, France |
Type of Talk | Invited Talk |
Keywords | learning theory, Optimization |
Poster
Different Cycle, Different Assignment: Diversity in Assignment Problems with Multiple Cycles
AAAI-18, New Orleans, Louisiana, USA, 2018.Status: Published
Different Cycle, Different Assignment: Diversity in Assignment Problems with Multiple Cycles
We present approaches to handle diverse assignments in 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.
Afilliation | Software Engineering |
Project(s) | The Certus Centre (SFI) |
Publication Type | Poster |
Year of Publication | 2018 |
Place Published | AAAI-18, New Orleans, Louisiana, USA |
Type of Work | Student Abstract |
Talks, contributed
Estimating Objective Boundaries for Constraint Optimization Problems
In NordConsNet Workshop, Gothenburg, Sweden, 2018.Status: Published
Estimating Objective Boundaries for Constraint Optimization Problems
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.
Afilliation | Software Engineering |
Project(s) | The Certus Centre (SFI) |
Publication Type | Talks, contributed |
Year of Publication | 2018 |
Location of Talk | NordConsNet Workshop, Gothenburg, Sweden |
Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration
In Gesellschaft für Informatik Software Engineering Conference 2018 (SE18), Ulm, Germany, 2018.Status: Published
Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration
Afilliation | Software Engineering |
Project(s) | The Certus Centre (SFI) |
Publication Type | Talks, contributed |
Year of Publication | 2018 |
Location of Talk | Gesellschaft für Informatik Software Engineering Conference 2018 (SE18), Ulm, Germany |
Proceedings, refereed
Stratified Constructive Disjunction and Negation in Constraint Programming
In Proc. of IEEE Int. Conf. on Tools with Artificial Intelligence (ICTAI-18). Volos, Greece. Nov. 2018. IEEE, 2018.Status: Published
Stratified Constructive Disjunction and Negation in Constraint Programming
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.
Afilliation | Software Engineering |
Project(s) | The Certus Centre (SFI) |
Publication Type | Proceedings, refereed |
Year of Publication | 2018 |
Conference Name | Proc. of IEEE Int. Conf. on Tools with Artificial Intelligence (ICTAI-18). Volos, Greece. Nov. 2018 |
Pagination | 106-113 |
Publisher | IEEE |
Other Numbers | arXiv:1811.03906 |
DOI | 10.1109/ICTAI.2018.00026 |
Towards Hybrid Constraint Solving with Reinforcement Learning and Constraint-Based Local Search
In Data Science meets Optimization Workshop at Federated Artificial Intelligence Meeting, 2018.Status: Published
Towards Hybrid Constraint Solving with Reinforcement Learning and Constraint-Based Local Search
Afilliation | Software Engineering |
Project(s) | The Certus Centre (SFI) |
Publication Type | Proceedings, refereed |
Year of Publication | 2018 |
Conference Name | Data Science meets Optimization Workshop at Federated Artificial Intelligence Meeting |
Proceedings, refereed
Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration
In Proceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis. New York, NY, USA: ACM, 2017.Status: Published
Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration
Afilliation | Software Engineering |
Project(s) | The Certus Centre (SFI) |
Publication Type | Proceedings, refereed |
Year of Publication | 2017 |
Conference Name | Proceedings of the 26th ACM SIGSOFT International Symposium on Software Testing and Analysis |
Pagination | 12-22 |
Publisher | ACM |
Place Published | New York, NY, USA |
ISBN Number | 978-1-4503-5076-1 |
Other Numbers | arXiv:1811.04122 |
DOI | 10.1145/3092703.3092709 |
Time-aware Test Case Execution Scheduling for Cyber-Physical Systems
In Proceedings of Principles of Constraint Programming (CP'17). Springer, 2017.Status: Published
Time-aware Test Case Execution Scheduling for Cyber-Physical Systems
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’ 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.
Afilliation | Software Engineering |
Project(s) | The Certus Centre (SFI) |
Publication Type | Proceedings, refereed |
Year of Publication | 2017 |
Conference Name | Proceedings of Principles of Constraint Programming (CP'17) |
Publisher | Springer |
Other Numbers | arXiv:1902.04627 |
DOI | 10.1007/978-3-319-66158-2_25 |
Journal Article
Multi-stage evolution of single- and multi-objective MCLP
Soft Computing (2016): 1-14.Status: Published
Multi-stage evolution of single- and multi-objective MCLP
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 1–7, 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.
Project(s) | No Simula project |
Publication Type | Journal Article |
Year of Publication | 2016 |
Journal | Soft Computing |
Pagination | 1–14 |
Date Published | 10/2016 |
Publisher | Springer |
ISSN | 1433-7479 |
URL | http://dx.doi.org/10.1007/s00500-016-2374-9 |
DOI | 10.1007/s00500-016-2374-9 |
Proceedings, refereed
Successive evolution of charging station placement
In 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA). IEEE, 2015.Status: Published
Successive evolution of charging station placement
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.
Project(s) | No Simula project |
Publication Type | Proceedings, refereed |
Year of Publication | 2015 |
Conference Name | 2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA) |
Pagination | 1-7 |
Date Published | 09/2015 |
Publisher | IEEE |
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 |