20,810 research outputs found
Process Algebra for Modal Transition Systemses
Full text linkThe formalism of modal transition systems (MTS) is a well established framework for systems specification as well as abstract interpretation. Nevertheless, due to incapability to capture some useful features, various extensions have been studied, such as e.g. mixed transition systems or disjunctive MTS. Thus a need to compare them has emerged. Therefore, we introduce transition system with obligations as a general model encompassing all the aforementioned models, and equip it with a process algebra description. Using these instruments, we then compare the previously studied subclasses and characterize their relationships
Verification of Open Interactive Markov Chains
Full text linkInteractive Markov chains (IMC) are compositional behavioral models extending both labeled transition systems and continuous-time Markov chains. IMC pair modeling convenience - owed to compositionality properties - with effective verification algorithms and tools - owed to Markov properties. Thus far however, IMC verification did not consider compositionality properties, but considered closed systems. This paper discusses the evaluation of IMC in an open and thus compositional interpretation. For this we embed the IMC into a game that is played with the environment. We devise algorithms that enable us to derive bounds on reachability probabilities that are assured to hold in any composition context
Artifact for CMSB22 paper "Abstraction-Based Segmental Simulation of Chemical Reaction Networks"
No full textThis is the artifact for the CMSB22 paper "Abstraction-Based Segmental Simulation of Chemical Reaction Networks" by Martin Helfrich, Milan Ceska, Jan Kretinsky and Štefan Martiček.
It makes the presented results reproducible.
The artifact is a modified version of SeQuaiA: a Java-based tool for semi-quantitative analysis of CRNs. In particular, there is no GUI just a benchmark script. Please refer to the SeQuaiA repository for all active development and additional features.This work has been supported by the Czech Science Foundation grant GJ20-02328Y, the German Research Foundation (DFG) projects 378803395 (ConVeY) and 427755713 (GOPro) as well as the ERC Advanced Grant 787367 (PaVeS)
Machine Learning and Model Checking Join Forces (Dagstuhl Seminar 18121)
Full text linkThis report documents the program and the outcomes of Dagstuhl Seminar 18121 "Machine Learning and Model Checking Join Forces". This Dagstuhl Seminar brought together researchers working in the fields of machine learning and model checking. It helped to identify new research topics on the one hand and to help with current problems on the other hand
Stochastic Games (Dagstuhl Seminar 24231)
No full textThe Dagstuhl Seminar on Stochastic Games brought together leading researchers and practitioners in the field to discuss recent advances, challenges, and future directions. The seminar featured a series of tutorials, invited talks, and contributed talks, which provided a comprehensive overview of the latest developments in Markov decision processes, reinforcement learning, and stochastic game theory. The seminar fostered lively discussions during open problem sessions and working groups, culminating in a collaborative exploration of open questions and potential research directions
Artificial Intelligence and Formal Methods Join Forces for Reliable Autonomy (Dagstuhl Seminar 24361)
No full textThis report documents the program and outcomes of Dagstuhl Seminar 24361, "Artificial Intelligence and Formal Methods Join Forces for Reliable Autonomy." AI is a disruptive force with growing applications in everyday life. Therefore, AI systems require serious safety, correctness, and reliability considerations. Recently, the field of safety in AI has triggered a vast amount of research. This seminar brought together experts from the fields of artificial intelligence, formal methods, and robotics. Via a diverse program with ample space for open yet guided discussion, a common understanding of problems was developed. Consequently, the seminar provided a means to identify key challenges and open problems in the research areas that underpin reliable autonomy
Artifact for Paper: Monitizer - Automating Design and Evaluation of Neural Network Monitors
No full text<div>
<pre>This is the artifact for our tool paper "Monitizer: Automating Design and Evaluation of Neural Network Monitors" which <br>was submitted to CAV'24 and (conditionally) accepted pending the artifact evaluation.<br><br>It contains a README, explaining the structure and how to use the artifact. <br>The core element is a docker image (docker-image.tar) that can be run within docker and is already setup with the <br>necessary software dependencies. Within this image, we provide our source code and </pre>
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Artifact for Paper: Monitizer - Automating Design and Evaluation of Neural Network Monitors
No full text<div>
<pre>This is the artifact for our tool paper "Monitizer: Automating Design and Evaluation of Neural Network Monitors" which <br>was submitted to CAV'24 and (conditionally) accepted pending the artifact evaluation.<br><br>It contains a README, explaining the structure and how to use the artifact. <br>The core element is a docker image (docker-image.tar) that can be run within docker and is already setup with the <br>necessary software dependencies. Within this image, we provide our source code and scripts to run the experiments from the paper.</pre>
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Artifact for Paper: Monitizer - Automating Design and Evaluation of Neural Network Monitors
No full text<div>
<pre>This is the artifact for our tool paper "Monitizer: Automating Design and Evaluation of Neural Network Monitors" which <br>was submitted to CAV'24 and (conditionally) accepted pending the artifact evaluation.<br><br>It contains a README, explaining the structure and how to use the artifact. <br>The core element is a docker image (docker-image.tar) that can be run within docker and is already setup with the <br>necessary software dependencies. Within this image, we provide our source code and scripts to run the experiments from the paper.</pre>
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Artifact for Paper: Monitizer - Automating Design and Evaluation of Neural Network Monitors
No full text<div>
<pre>This is the artifact for our tool paper "Monitizer: Automating Design and Evaluation of Neural Network Monitors" which <br>was submitted to CAV'24 and (conditionally) accepted pending the artifact evaluation.<br><br>It contains a README, explaining the structure and how to use the artifact. <br>The core element is a docker image (docker-image.tar) that can be run within docker and is already setup with the <br>necessary software dependencies. Within this image, we provide our source code and </pre>
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