International Center for Computational Logic - Benutzerbeiträge [de]

Inproceedings3444

2025-11-03T13:13:17Z

Robin Ziemek:

{{Publikation Erster Autor
|ErsterAutorVorname=Christel
|ErsterAutorNachname=Baier
|FurtherAuthors=Sascha Klüppelholz; Jakob Piribauer; Robin Ziemek
}}
{{Inproceedings
|Referiert=1
|Title=Formal Quality Measures for Predictors in Markov Decision Processes
|To appear=0
|Year=2025
|Month=April
|Booktitle=Proceedings of the 39th Annual AAAI Conference on Artificial Intelligence
|Publisher=Public Knowledge Project
|Series=Technical Tracks 25
|Volume=39
}}
{{Publikation Details
|Bild=AAAI25Proceedings-Cover.jpg
|Abstract=In adaptive systems, predictors are used to anticipate changes in the system’s state or behavior that may require system adaption, e.g., changing its configuration or adjusting resource allocation. Therefore, the quality of predictors is crucial for the overall reliability and performance of the system under control. This paper studies predictors in systems exhibiting probabilistic and non-deterministic behavior modelled as Markov decision processes (MDPs). Main contributions are the introduction of quantitative notions that measure the effectiveness of predictors in terms of their average capability to predict the occurrence of failures or other undesired system behaviors. The average is taken over all memoryless policies. We study two classes of such notions. One class is inspired by concepts that have been introduced in statistical analysis to explain the impact of features on the decisions of binary classifiers (such as precision, recall, f-score). Second, we study a measure that borrows ideas from recent work on probability-raising causality in MDPs and determines the quality of a predictor by the fraction of memoryless policies under which (the set of states in) the predictor is a probability-raising cause for the considered failure scenario.
|Slides=Formal-Quality-Measures-for-Predictors-Slides.pdf
|Link=https://doi.org/10.1609/aaai.v39i25.34879
|DOI Name=10.1609/aaai.v39i25.34879
|Projekt=CPEC
|Forschungsgruppe=Algebraische und logische Grundlagen der Informatik
|BibTex=@article{Baier_Klüppelholz_Piribauer_Ziemek_2025, title={Formal Quality Measures for Predictors in Markov Decision Processes}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/34879}, DOI={10.1609/aaai.v39i25.34879}, number={25}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Baier, Christel and Klüppelholz, Sascha and Piribauer, Jakob and Ziemek, Robin}, year={2025}, month={Apr.}, pages={26760-26768} }
}}

Inproceedings3444

2025-11-03T13:11:27Z

Robin Ziemek:

Datei:Formal-Quality-Measures-for-Predictors-Slides.pdf

2025-11-03T13:11:18Z

Robin Ziemek:

Inproceedings3444

2025-11-03T12:25:39Z

Robin Ziemek: Die Seite wurde neu angelegt: „{{Publikation Erster Autor |ErsterAutorVorname=Christel |ErsterAutorNachname=Baier |FurtherAuthors=Sascha Klüppelholz; Jakob Piribauer; Robin Ziemek }} {{Inproceedings |Referiert=1 |Title=Formal Quality Measures for Predictors in Markov Decision Processes |To appear=0 |Year=2025 |Month=April |Booktitle=Proceedings of the 39th Annual AAAI Conference on Artificial Intelligence |Publisher=Public Knowledge Project |Series=Technical Tracks 25 |Volume=39 }} {{…“

{{Publikation Erster Autor
|ErsterAutorVorname=Christel
|ErsterAutorNachname=Baier
|FurtherAuthors=Sascha Klüppelholz; Jakob Piribauer; Robin Ziemek
}}
{{Inproceedings
|Referiert=1
|Title=Formal Quality Measures for Predictors in Markov Decision Processes
|To appear=0
|Year=2025
|Month=April
|Booktitle=Proceedings of the 39th Annual AAAI Conference on Artificial Intelligence
|Publisher=Public Knowledge Project
|Series=Technical Tracks 25
|Volume=39
}}
{{Publikation Details
|Bild=AAAI25Proceedings-Cover.jpg
|Abstract=In adaptive systems, predictors are used to anticipate changes in the system’s state or behavior that may require system adaption, e.g., changing its configuration or adjusting resource allocation. Therefore, the quality of predictors is crucial for the overall reliability and performance of the system under control. This paper studies predictors in systems exhibiting probabilistic and non-deterministic behavior modelled as Markov decision processes (MDPs). Main contributions are the introduction of quantitative notions that measure the effectiveness of predictors in terms of their average capability to predict the occurrence of failures or other undesired system behaviors. The average is taken over all memoryless policies. We study two classes of such notions. One class is inspired by concepts that have been introduced in statistical analysis to explain the impact of features on the decisions of binary classifiers (such as precision, recall, f-score). Second, we study a measure that borrows ideas from recent work on probability-raising causality in MDPs and determines the quality of a predictor by the fraction of memoryless policies under which (the set of states in) the predictor is a probability-raising cause for the considered failure scenario.
|Link=https://doi.org/10.1609/aaai.v39i25.34879
|DOI Name=10.1609/aaai.v39i25.34879
|Projekt=CPEC
|Forschungsgruppe=Algebraische und logische Grundlagen der Informatik
}}

Datei:Formal-Quality-Measures-for-Predictors-in-Markov-Decision-Processes.pdf

2025-11-03T12:14:00Z

Robin Ziemek:

Datei:AAAI25Proceedings-Cover.jpg

2025-11-03T12:10:08Z

Robin Ziemek:

Article3926188434

2025-03-12T15:04:05Z

Robin Ziemek:

{{Publikation Erster Autor
|ErsterAutorVorname=Christel
|ErsterAutorNachname=Baier
|FurtherAuthors=Jakob Piribauer; Robin Ziemek
}}
{{Article
|Referiert=0
|Title=Foundations of probability-raising causality in Markov decision processes
|To appear=0
|Year=2024
|Journal=Logical Methods in Computer Science
|Volume=20
|Number=1
}}
{{Publikation Details
|Abstract=This work introduces a novel cause-effect relation in Markov decision processes using the probability-raising principle. Initially, sets of states as causes and effects are considered, which is subsequently extended to regular path properties as effects and then as causes. The paper lays the mathematical foundations and analyzes the algorithmic properties of these cause-effect relations. This includes algorithms for checking cause conditions given an effect and deciding the existence of probability-raising causes. As the definition allows for sub-optimal coverage properties, quality measures for causes inspired by concepts of statistical analysis are studied. These include recall, coverage ratio and f-score. The computational complexity for finding optimal causes with respect to these measures is analyzed.
|ISSN=1860-5974
|DOI Name=10.46298/LMCS-20(1:4)2024
|Projekt=CPEC, CeTI
|Forschungsgruppe=Algebraische und logische Grundlagen der Informatik
|BibTex=@article{BPZ24,
title = {Foundations of probability-raising causality in Markov decision processes},
author = {Christel Baier and Jakob Piribauer and Robin Ziemek},
url = {https://lmcs.episciences.org/10015},
doi = {10.46298/lmcs-20(1:4)2024},
journal = {Logical Methods in Computer Science},
issn = {1860-5974},
volume = {Volume 20, Issue 1},
eid = 4,
year = {2024},
month = {Jan},
keywords = {Computer Science - Logic in Computer Science},
}
}}

Article3926188434

2025-03-06T09:18:03Z

Robin Ziemek:

{{Publikation Erster Autor
|ErsterAutorVorname=Christel
|ErsterAutorNachname=Baier
|FurtherAuthors=Jakob Piribauer; Robin Ziemek
}}
{{Article
|Referiert=0
|Title=Foundations of probability-raising causality in Markov decision processes
|To appear=0
|Year=2024
|Journal=Logical Methods in Computer Science
|Volume=20
|Number=1
}}
{{Publikation Details
|Abstract=This work introduces a novel cause-effect relation in Markov decision processes using the probability-raising principle. Initially, sets of states as causes and effects are considered, which is subsequently extended to regular path properties as effects and then as causes. The paper lays the mathematical foundations and analyzes the algorithmic properties of these cause-effect relations. This includes algorithms for checking cause conditions given an effect and deciding the existence of probability-raising causes. As the definition allows for sub-optimal coverage properties, quality measures for causes inspired by concepts of statistical analysis are studied. These include recall, coverage ratio and f-score. The computational complexity for finding optimal causes with respect to these measures is analyzed.
|ISSN=1860-5974
|DOI Name=10.46298/LMCS-20(1:4)2024
|Forschungsgruppe=Algebraische und logische Grundlagen der Informatik
|BibTex=@article{lmcs:10015,
title = {Foundations of probability-raising causality in Markov decision processes},
author = {Christel Baier and Jakob Piribauer and Robin Ziemek},
url = {https://lmcs.episciences.org/10015},
doi = {10.46298/lmcs-20(1:4)2024},
journal = {Logical Methods in Computer Science},
issn = {1860-5974},
volume = {Volume 20, Issue 1},
eid = 4,
year = {2024},
month = {Jan},
keywords = {Computer Science - Logic in Computer Science},
}
}}

Article3926188434

2025-03-06T09:16:59Z

Robin Ziemek:

{{Publikation Erster Autor
|ErsterAutorVorname=Christel
|ErsterAutorNachname=Baier
|FurtherAuthors=Jakob Piribauer; Robin Ziemek
}}
{{Article
|Referiert=0
|Title=Foundations of probability-raising causality in Markov decision processes
|To appear=0
|Year=2024
|Journal=Logical Methods in Computer Science
|Volume=20
|Number=1
|Pages=66
}}
{{Publikation Details
|Abstract=This work introduces a novel cause-effect relation in Markov decision processes using the probability-raising principle. Initially, sets of states as causes and effects are considered, which is subsequently extended to regular path properties as effects and then as causes. The paper lays the mathematical foundations and analyzes the algorithmic properties of these cause-effect relations. This includes algorithms for checking cause conditions given an effect and deciding the existence of probability-raising causes. As the definition allows for sub-optimal coverage properties, quality measures for causes inspired by concepts of statistical analysis are studied. These include recall, coverage ratio and f-score. The computational complexity for finding optimal causes with respect to these measures is analyzed.
|ISSN=1860-5974
|DOI Name=10.46298/LMCS-20(1:4)2024
|Forschungsgruppe=Algebraische und logische Grundlagen der Informatik
|BibTex=@article{lmcs:10015,
title = {Foundations of probability-raising causality in Markov decision processes},
author = {Christel Baier and Jakob Piribauer and Robin Ziemek},
url = {https://lmcs.episciences.org/10015},
doi = {10.46298/lmcs-20(1:4)2024},
journal = {Logical Methods in Computer Science},
issn = {1860-5974},
volume = {Volume 20, Issue 1},
eid = 4,
year = {2024},
month = {Jan},
keywords = {Computer Science - Logic in Computer Science},
}
}}

Phdthesis3022

2025-03-06T09:08:26Z

Robin Ziemek:

{{Publikation Erster Autor
|ErsterAutorVorname=Robin
|ErsterAutorNachname=Ziemek
}}
{{Phdthesis
|Title=Probabilistic Causality in Markovian Models
|Instructor=Prof. Dr. Christel Baier
|Date=2024/09/23
|School=TU Dresden
}}
{{Publikation Details
|Abstract=The complexity of modern computer and software systems still seems to grow exponentially, while the human user is widely left without explanations on how to understand these systems. One of the central tasks of current computer science therefore lies in the development of methods and tools to build such an understanding. A similar task is addressed by formal verification which gives various verifiable justifications for the functionality of a system. As these still only give knowledge that a system functions properly they only address a portion of the task to make systems easier to comprehend. It is widely believed that cause-effect reasoning plays an important role in forming human understanding of complex relations. Thus, there are already many accounts on causality in modern computer science. However, most of them are focusing on a form of backward looking actual causality. This variant of causality is concerned with actual events after their occurrence and tries to reason about causes mostly in a counterfactual manner.

In this thesis we address a probabilistic form of causality which is forward looking by nature. As such, we define and discuss novel notions of probabilistic causes in discrete time Markov chains and Markov decision processes. For this we rely on two central principles of probabilistic causality. On one hand, the probability-raising principle states that a cause should raise the probability of its effect. On the other hand, temporal priority requires that a cause must occur before its effect. We build the mathematical and algorithmic foundations of our so called probability-raising causes. For this we work in a state-based setting where causes and effects are reachability properties of sets of states. In order to measure the predictive power of states we define quality-measures for which we interpret causes as binary classifiers. With these tools we address the algorithmic questions of checking cause-effect relations if both a cause candidate and an effect are given and finding quality optimal causes if only the effect is given. We discuss possible extensions of this basic state-based framework to more general formulations of causes and effects as ω-regular properties.
|Slides=Verteidigung - Slides.pdf
|Link=https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-934134
|Forschungsgruppe=Algebraische und logische Grundlagen der Informatik
}}

Phdthesis3022

2025-03-06T09:07:33Z

Robin Ziemek:

{{Publikation Erster Autor
|ErsterAutorVorname=Robin
|ErsterAutorNachname=Ziemek
}}
{{Phdthesis
|Title=Probabilistic Causality in Markovian Models
|Instructor=Prof. Dr. Christel Baier
|Date=2024/08/09
|School=TU Dresden
}}
{{Publikation Details
|Abstract=The complexity of modern computer and software systems still seems to grow exponentially, while the human user is widely left without explanations on how to understand these systems. One of the central tasks of current computer science therefore lies in the development of methods and tools to build such an understanding. A similar task is addressed by formal verification which gives various verifiable justifications for the functionality of a system. As these still only give knowledge that a system functions properly they only address a portion of the task to make systems easier to comprehend. It is widely believed that cause-effect reasoning plays an important role in forming human understanding of complex relations. Thus, there are already many accounts on causality in modern computer science. However, most of them are focusing on a form of backward looking actual causality. This variant of causality is concerned with actual events after their occurrence and tries to reason about causes mostly in a counterfactual manner.

In this thesis we address a probabilistic form of causality which is forward looking by nature. As such, we define and discuss novel notions of probabilistic causes in discrete time Markov chains and Markov decision processes. For this we rely on two central principles of probabilistic causality. On one hand, the probability-raising principle states that a cause should raise the probability of its effect. On the other hand, temporal priority requires that a cause must occur before its effect. We build the mathematical and algorithmic foundations of our so called probability-raising causes. For this we work in a state-based setting where causes and effects are reachability properties of sets of states. In order to measure the predictive power of states we define quality-measures for which we interpret causes as binary classifiers. With these tools we address the algorithmic questions of checking cause-effect relations if both a cause candidate and an effect are given and finding quality optimal causes if only the effect is given. We discuss possible extensions of this basic state-based framework to more general formulations of causes and effects as ω-regular properties.
|Slides=Verteidigung - Slides.pdf
|Link=https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-934134
|Forschungsgruppe=Algebraische und logische Grundlagen der Informatik
}}

Phdthesis3022

2025-03-06T09:06:41Z

Robin Ziemek:

{{Publikation Erster Autor
|ErsterAutorVorname=Robin
|ErsterAutorNachname=Ziemek
}}
{{Phdthesis
|Title=Probabilistic Causality in Markovian Models
|Instructor=Prof. Dr. Christel Baier
|Date=2024/08/09
|School=TU Dresden
}}
{{Publikation Details
|Abstract=The complexity of modern computer and software systems still seems to grow exponentially, while the human user is widely left without explanations on how to understand these systems. One of the central tasks of current computer science therefore lies in the development of methods and tools to build such an understanding. A similar task is addressed by formal verification which gives various verifiable justifications for the functionality of a system. As these still only give knowledge that a system functions properly they only address a portion of the task to make systems easier to comprehend. It is widely believed that cause-effect reasoning plays an important role in forming human understanding of complex relations. Thus, there are already many accounts on causality in modern computer science. However, most of them are focusing on a form of backward looking actual causality. This variant of causality is concerned with actual events after their occurrence and tries to reason about causes mostly in a counterfactual manner.
In this thesis we address a probabilistic form of causality which is forward looking by nature. As such, we define and discuss novel notions of probabilistic causes in discrete time Markov chains and Markov decision processes. For this we rely on two central principles of probabilistic causality. On one hand, the probability-raising principle states that a cause should raise the probability of its effect. On the other hand, temporal priority requires that a cause must occur before its effect. We build the mathematical and algorithmic foundations of our so called probability-raising causes. For this we work in a state-based setting where causes and effects are reachability properties of sets of states. In order to measure the predictive power of states we define quality-measures for which we interpret causes as binary classifiers. With these tools we address the algorithmic questions of checking cause-effect relations if both a cause candidate and an effect are given and finding quality optimal causes if only the effect is given. We discuss possible extensions of this basic state-based framework to more general formulations of causes and effects as ω-regular properties.
|Slides=Verteidigung - Slides.pdf
|Link=https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-934134
|Forschungsgruppe=Algebraische und logische Grundlagen der Informatik
}}

Phdthesis3022/en

2025-03-06T09:03:17Z

Robin Ziemek: Page created automatically by parser function on page Phdthesis3022

#REDIRECT [[Phdthesis3022]]

Phdthesis3022

2025-03-06T09:03:16Z

Robin Ziemek: Die Seite wurde neu angelegt: „{{Publikation Erster Autor |ErsterAutorVorname=Robin |ErsterAutorNachname=Ziemek }} {{Phdthesis |Title=Probabilistic Causality in Markovian Models |Instructor=Prof. Dr. Christel Baier |Date=2024/03/09 |School=TU Dresden }} {{Publikation Details |Abstract=The complexity of modern computer and software systems still seems to grow exponentially, while the human user is widely left without explanations on how to understand these systems. One of the central…“

{{Publikation Erster Autor
|ErsterAutorVorname=Robin
|ErsterAutorNachname=Ziemek
}}
{{Phdthesis
|Title=Probabilistic Causality in Markovian Models
|Instructor=Prof. Dr. Christel Baier
|Date=2024/03/09
|School=TU Dresden
}}
{{Publikation Details
|Abstract=The complexity of modern computer and software systems still seems to grow exponentially,
while the human user is widely left without explanations on how to understand these systems.
One of the central tasks of current computer science therefore lies in the development
of methods and tools to build such an understanding. A similar task is addressed by formal
verification which gives various verifiable justifications for the functionality of a system. As
these still only give knowledge that a system functions properly they only address a portion
of the task to make systems easier to comprehend. It is widely believed that cause-effect
reasoning plays an important role in forming human understanding of complex relations.
Thus, there are already many accounts on causality in modern computer science. However,
most of them are focusing on a form of backward looking actual causality. This variant of
causality is concerned with actual events after their occurrence and tries to reason about
causes mostly in a counterfactual manner.
In this thesis we address a probabilistic form of causality which is forward looking by nature.
As such, we define and discuss novel notions of probabilistic causes in discrete time Markov
chains and Markov decision processes. For this we rely on two central principles of probabilistic
causality. On one hand, the probability-raising principle states that a cause should raise
the probability of its effect. On the other hand, temporal priority requires that a cause must
occur before its effect. We build the mathematical and algorithmic foundations of our so
called probability-raising causes. For this we work in a state-based setting where causes and
effects are reachability properties of sets of states. In order to measure the predictive power
of states we define quality-measures for which we interpret causes as binary classifiers. With
these tools we address the algorithmic questions of checking cause-effect relations if both a
cause candidate and an effect are given and finding quality-optimal causes if only the effect is
given. We discuss possible extensions of this basic state-based framework to more general
formulations of causes and effects as ω-regular properties.
|Slides=Verteidigung - Slides.pdf
|Link=https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-934134
|Forschungsgruppe=Algebraische und logische Grundlagen der Informatik
}}

Datei:Verteidigung - Slides.pdf

2025-03-06T09:01:05Z

Robin Ziemek: Slides von dem Vortrag der Verteidigung

Slides von dem Vortrag der Verteidigung