Search Results for author: Bernd Finkbeiner

Found 10 papers, 4 papers with code

Neural Circuit Synthesis from Specification Patterns

1 code implementation NeurIPS 2021 Frederik Schmitt, Christopher Hahn, Markus N. Rabe, Bernd Finkbeiner

We train hierarchical Transformers on the task of synthesizing hardware circuits directly out of high-level logical specifications in linear-time temporal logic (LTL).

Efficient Monitoring of Hyperproperties using Prefix Trees

no code implementations18 Jan 2021 Bernd Finkbeiner, Christopher Hahn, Marvin Stenger, Leander Tentrup

Hyperproperties, such as non-interference and observational determinism, relate multiple computation traces with each other and are thus not monitorable by tools that consider computations in isolation.

Logic in Computer Science

Realizing Omega-regular Hyperproperties

no code implementations18 Jan 2021 Bernd Finkbeiner, Christopher Hahn, Jana Hofmann, Leander Tentrup

We furthermore studied the realizability problem of HyperQPTL.

Logic in Computer Science

Syntroids: Synthesizing a Game for FPGAs using Temporal Logic Specifications

no code implementations18 Jan 2021 Gideon Geier, Philippe Heim, Felix Klein, Bernd Finkbeiner

Syntroids is a space shooter arcade game realized on an FPGA, where the control flow architecture has been completely specified in Temporal Stream Logic (TSL) and implemented using reactive synthesis.

Hardware Architecture

Linear-time Temporal Logic with Team Semantics: Expressivity and Complexity

no code implementations7 Oct 2020 Jonni Virtema, Jana Hofmann, Bernd Finkbeiner, Juha Kontinen, Fan Yang

We study the expressivity and complexity of model checking linear temporal logic with team semantics (TeamLTL).

Logic in Computer Science Computational Complexity F.4.1; D.2.4

Teaching Temporal Logics to Neural Networks

1 code implementation ICLR 2021 Christopher Hahn, Frederik Schmitt, Jens U. Kreber, Markus N. Rabe, Bernd Finkbeiner

We study two fundamental questions in neuro-symbolic computing: can deep learning tackle challenging problems in logics end-to-end, and can neural networks learn the semantics of logics.

Proceedings 3rd Workshop on formal reasoning about Causation, Responsibility, and Explanations in Science and Technology

no code implementations1 Jan 2019 Bernd Finkbeiner, Samantha Kleinberg

A further objective is to link to the foundations of causal reasoning in the philosophy of sciences and to causal reasoning performed in other areas of computer science, engineering, and beyond.

Synthesizing Skeletons for Reactive Systems

no code implementations25 Mar 2018 Bernd Finkbeiner, Hazem Torfah

This information is represented in the form of a labeled transition system, which we call skeleton.

Temporal Stream Logic: Synthesis beyond the Bools

2 code implementations1 Dec 2017 Bernd Finkbeiner, Felix Klein, Ruzica Piskac, Mark Santolucito

On the other hand, however, synthesis from TSL is scalable, because it is independent of the complexity of the handled data.

Logic in Computer Science

Temporal Logics for Hyperproperties

1 code implementation17 Jan 2014 Michael R. Clarkson, Bernd Finkbeiner, Masoud Koleini, Kristopher K. Micinski, Markus N. Rabe, César Sánchez

Standard temporal logics such as LTL, CTL, and CTL* can refer only to a single path at a time, hence cannot express many hyperproperties of interest.

Logic in Computer Science

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