@inproceedings{DBLP:conf/cav/BadingsJJSV22,

title = {Sampling-Based Verification of CTMCs with Uncertain Rates},

author = {Thom S. Badings and Nils Jansen and Sebastian Junges and Mariëlle Stoelinga and Matthias Volk},

url = {https://arxiv.org/abs/2205.08300},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {CAV (2)},

volume = {13372},

pages = {26–47},

publisher = {Springer},

series = {Lecture Notes in Computer Science},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Kerkkamp2021,

title = {Grouping of Maintenance Actions with Deep Reinforcement Learning and Graph Convolutional Networks},

author = {David Kerkkamp and Zaharah A. Bukhsh and Yingqian Zhang and Nils Jansen},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {International Conference on Agents and Artificial Intelligence (ICAART)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{DBLP:conf/aaai/BadingsA00PS22,

title = {Sampling-Based Robust Control of Autonomous Systems with Non-Gaussian  Noise},

author = {Thom S. Badings and Alessandro Abate and Nils Jansen and David Parker and Hasan A. Poonawala and Mariëlle Stoelinga},

url = {https://ojs.aaai.org/index.php/AAAI/article/view/21201/20950},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

booktitle = {AAAI},

pages = {9669–9678},

publisher = {AAAI Press},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{JimenezRoa2021_ESREL,

title = {Fault Trees, Decision Trees, and Binary Decision Diagrams: A systematic comparison},

author = {Lisandro A. Jimenez-Roa and Tom Heskes and Mariëlle Stoelinga},

editor = {Bruno Castanier and Marko Cepin and David Bigaud and Christophe Bérenguer},

url = {http://esrel2021.org/en/index.html},

doi = {10.3850/978-981-18-2016-8_241-cd},

year  = {2021},

date = {2021-09-21},

urldate = {2021-09-21},

booktitle = {Proceedings of the 31st European Safety and Reliability Conference (ESREL 2021)},

pages = {673–680},

publisher = {Research Publishing},

abstract = {In reliability engineering, we need to understand system dependencies, cause-effect relations, identify critical components, and analyze how they trigger failures. Three prominent graph models commonly used for these purposes are fault trees (FTs), decision trees (DTs), and binary decision diagrams (BDDs). These models are popular because they are easy to interpret, serve as a communication tool between stakeholders of various backgrounds, and support decision-making processes. Moreover, these models help to understand real-world problems by computing reliability metrics, minimum cut sets, logic rules, and displaying dependencies. Nevertheless, it is unclear how these graph models compare. Thus, the goal of this paper is to understand the similarities and differences through a systematic comparison based on their (i) purpose and application, (ii) structural representation, (iii) analysis methods, (iv) construction, and (v) benefits & limitations. Furthermore, we use a running example based on a Container Seal Design to showcase the models in practice. Our results show that, given that FTs, DTs, and BDDs have different purposes and application domains, they adopt different structural representations and analysis methodologies that entail a variety of benefits and limitations, the latter can be addressed via conversion methods or extensions. Specific remarks are that BDDs can be considered as a compact representation of binary DTs, since the former allows sub-node sharing, which makes BDDs more efficient at representing logical rules than binary DTs. It is possible to obtain cut sets from BDDs and DTs and construct a FT using the (con/dis)junctive normal form, although this may result in a sub-optimal FT structure.},

note = {European Safety and Reliability Conference 2021, ESREL 2021 ; Conference date: 19-09-2021 Through 23-09-2021},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}