activity
20172022
most citedVerifiably Safe Off-Model Reinforcement Learning

40 citations · 86 across the 4 of their papers we have counts for

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Showing 2019Show all

6 papers · 1 filter

cs.LO2019

Overview of Logical Foundations of Cyber-Physical Systems

André Platzer

Cyber-physical systems (CPSs) are important whenever computer technology interfaces with the physical world as it does in self-driving cars or aircraft control support systems. Due…

cs.LO2019

Differential Equation Invariance Axiomatization

André Platzer, Yong Kiam Tan

This article proves the completeness of an axiomatization for differential equation invariants described by Noetherian functions. First, the differential equation axioms of differe…

cs.LO2019

An Axiomatic Approach to Liveness for Differential Equations

Yong Kiam Tan, André Platzer

This paper presents an approach for deductive liveness verification for ordinary differential equations (ODEs) with differential dynamic logic. Numerous subtleties complicate the g…

cs.AI201940 cited

Verifiably Safe Off-Model Reinforcement Learning

Nathan Fulton, Andre Platzer

The desire to use reinforcement learning in safety-critical settings has inspired a recent interest in formal methods for learning algorithms. Existing formal methods for learning…

cs.PL201917 cited

HyPLC: Hybrid Programmable Logic Controller Program Translation for Verification

Luis Garcia, Stefan Mitsch, Andre Platzer

Programmable Logic Controllers (PLCs) provide a prominent choice of implementation platform for safety-critical industrial control systems. Formal verification provides ways of est…

cs.LO2019

Uniform Substitution At One Fell Swoop

André Platzer

Uniform substitution of function, predicate, program or game symbols is the core operation in parsimonious provers for hybrid systems and hybrid games. By postponing soundness-crit…