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20122026
most citedCounterfactual Explanations for MITL Violations

3 citations · 8 across the 22 of their papers we have counts for

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7 papers · 1 filter

cs.LO2024★ 3 cited

Counterfactual Explanations for MITL Violations

Bernd Finkbeiner, Felix Jahn, Julian Siber

MITL is a temporal logic that facilitates the verification of real-time systems by expressing the critical timing constraints placed on these systems. MITL specifications can be ch…

cs.LO2024

Syntax-Guided Automated Program Repair for Hyperproperties

Raven Beutner, Tzu-Han Hsu, Borzoo Bonakdarpour +1

We study the problem of automatically repairing infinite-state software programs w.r.t. temporal hyperproperties. As a first step, we present a repair approach for the temporal log…

cs.LO2024

Non-Deterministic Planning for Hyperproperty Verification

Raven Beutner, Bernd Finkbeiner

Non-deterministic planning aims to find a policy that achieves a given objective in an environment where actions have uncertain effects, and the agent - potentially - only observes…

cs.LO2024★ 2 cited

Synthesis of Temporal Causality

Bernd Finkbeiner, Hadar Frenkel, Niklas Metzger +1

We present an automata-based algorithm to synthesize omega-regular causes for omega-regular effects on executions of a reactive system, such as counterexamples uncovered by a model…

cs.LO2024

Monitoring Second-Order Hyperproperties

Raven Beutner, Bernd Finkbeiner, Hadar Frenkel +1

Hyperproperties express the relationship between multiple executions of a system. This is needed in many AI-related fields, such as knowledge representation and planning, to captur…

cs.MA2024

Hyper Strategy Logic

Raven Beutner, Bernd Finkbeiner

Strategy logic (SL) is a powerful temporal logic that enables strategic reasoning in multi-agent systems. SL supports explicit (first-order) quantification over strategies and prov…

cs.LG2024

Learning Better Representations From Less Data For Propositional Satisfiability

Mohamed Ghanem, Frederik Schmitt, Julian Siber +1

Training neural networks on NP-complete problems typically demands very large amounts of training data and often needs to be coupled with computationally expensive symbolic verifie…