activity
20192025
most citedFrom Verification to Causality-based Explications

4 citations · 14 across the 11 of their papers we have counts for

collaborators

15 papers

cs.LO2025★ 1 cited

Certificates and Witnesses for Multi-objective ω-regular Queries in Markov Decision Processes

Christel Baier, Calvin Chau, Volodymyr Drobitko +2

Multi-objective probabilistic model checking is a powerful technique for verifying stochastic systems against multiple (potentially conflicting) properties. To enhance the trustwor…

cs.GT2023★ 2 cited

A Unifying Formal Approach to Importance Values in Boolean Functions

Hans Harder, Simon Jantsch, Christel Baier +1

Boolean functions and their representation through logics, circuits, machine learning classifiers, or binary decision diagrams (BDDs) play a central role in the design and analysis…

cs.LO2021★ 1 cited

Witnessing Subsystems for Probabilistic Systems with Low Tree Width

Simon Jantsch, Jakob Piribauer, Christel Baier

A standard way of justifying that a certain probabilistic property holds in a system is to provide a witnessing subsystem (also called critical subsystem) for the property. Computi…

cs.LO2021

Witnessing subsystems for probabilistic systems with low tree width

Simon Jantsch, Jakob Piribauer, Christel Baier

A standard way of justifying that a certain probabilistic property holds in a system is to provide a witnessing subsystem (also called critical subsystem) for the property. Computi…

cs.FL2021

Determinization and Limit-determinization of Emerson-Lei automata

Tobias John, Simon Jantsch, Christel Baier +1

We study the problem of determinizing -automata whose acceptance condition is defined on the transitions using Boolean formulas, also known as transition-based Emerson-Lei autom…

cs.LO2021

Causality-Based Game Solving

Christel Baier, Norine Coenen, Bernd Finkbeiner +3

We present a causality-based algorithm for solving two-player reachability games represented by logical constraints. These games are a useful formalism to model a wide array of pro…