Author name cluster

Christopher Hahn

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

2 author rows

ICLR Conference 2023 Conference Paper

Iterative Circuit Repair Against Formal Specifications

Matthias Cosler
Frederik Schmitt
Christopher Hahn
Bernd Finkbeiner

We present a deep learning approach for repairing sequential circuits against formal specifications given in linear-time temporal logic (LTL). Given a defective circuit and its formal specification, we train Transformer models to output circuits that satisfy the corresponding specification. We propose a separated hierarchical Transformer for multimodal representation learning of the formal specification and the circuit. We introduce a data generation algorithm that enables generalization to more complex specifications and out-of-distribution datasets. In addition, our proposed repair mechanism significantly improves the automated synthesis of circuits from LTL specifications with Transformers. It improves the state-of-the-art by $6.8$ percentage points on held-out instances and $11.8$ percentage points on an out-of-distribution dataset from the annual reactive synthesis competition.

Details

NeurIPS Conference 2021 Conference Paper

Neural Circuit Synthesis from Specification Patterns

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). The LTL synthesis problem is a well-known algorithmic challenge with a long history and an annual competition is organized to track the improvement of algorithms and tooling over time. New approaches using machine learning might open a lot of possibilities in this area, but suffer from the lack of sufﬁcient amounts of training data. In this paper, we consider a method to generate large amounts of additional training data, i. e. , pairs of speciﬁcations and circuits implementing them. We ensure that this synthetic data is sufﬁciently close to human-written speciﬁcations by mining common patterns from the speciﬁcations used in the synthesis competitions. We show that hierarchical Transformers trained on this synthetic data solve a signiﬁcant portion of problems from the synthesis competitions, and even out-of-distribution examples from a recent case study.

PDF Details

ICLR Conference 2021 Conference Paper

Teaching Temporal Logics to Neural Networks

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. In this work we focus on linear-time temporal logic (LTL), as it is widely used in verification. We train a Transformer on the problem to directly predict a solution, i.e. a trace, to a given LTL formula. The training data is generated with classical solvers, which, however, only provide one of many possible solutions to each formula. We demonstrate that it is sufficient to train on those particular solutions to formulas, and that Transformers can predict solutions even to formulas from benchmarks from the literature on which the classical solver timed out. Transformers also generalize to the semantics of the logics: while they often deviate from the solutions found by the classical solvers, they still predict correct solutions to most formulas.

Details

Highlights Conference 2016 Conference Abstract

Deciding Hyperproperties

Bernd Finkbeiner
Christopher Hahn

This talk is based on the paper “Deciding Hyperproperties” by Bernd Finkbeiner and Christopher Hahn, which appeared on the 27th International Conference on Concurrency Theory (CONCUR 2016). Hyperproperties, like observational determinism or symmetry, cannot be expressed as properties of individual computation traces, because they describe a relation between multiple computation traces. HyperLTL is a temporal logic that captures such relations through trace variables, which are introduced through existential and universal trace quantifiers and can be used to refer to multiple computations at the same time. We study the satisfiability problem of HyperLTL. We show that the problem is PSPACE-complete for alternation-free formulas (and, hence, no more expensive than LTL satisfiability), EXPSPACE-complete for exists-forall formulas, and undecidable for forall-exists formulas. Many practical hyperproperties can be expressed as alternation-free formulas. Our results show that both satisfiability and implication are decidable for such properties.

Details