Sascha Klüppelholz

TACAS Conference 2026 Conference Paper

Concurrent Permissive Strategy Templates

• Ashwani Anand
• Christel Baier
• Calvin Chau
• Sascha Klüppelholz
• Ali Mirzaei
• Satya Prakash Nayak
• Anne-Kathrin Schmuck

AAAI Conference 2025 Conference Paper

Formal Quality Measures for Predictors in Markov Decision Processes

• Christel Baier
• Sascha Klüppelholz
• Jakob Piribauer
• Robin Ziemek

In adaptive systems, predictors are used to anticipate changes in the system’s state or behavior that may require system adaption, e.g., changing its configuration or adjusting resource allocation. Therefore, the quality of predictors is crucial for the overall reliability and performance of the system under control. This paper studies predictors in systems exhibiting probabilistic and non-deterministic behavior modelled as Markov decision processes (MDPs). Main contributions are the introduction of quantitative notions that measure the effectiveness of predictors in terms of their average capability to predict the occurrence of failures or other undesired system behaviors. The average is taken over all memoryless policies. We study two classes of such notions. One class is inspired by concepts that have been introduced in statistical analysis to explain the impact of features on the decisions of binary classifiers (such as precision, recall, f-score). Second, we study a measure that borrows ideas from recent work on probability-raising causality in MDPs and determines the quality of a predictor by the fraction of memoryless policies under which (the set of states in) the predictor is a probability-raising cause for the considered failure scenario.

AAAI Conference 2024 Conference Paper

Backward Responsibility in Transition Systems Using General Power Indices

• Christel Baier
• Roxane van den Bossche
• Sascha Klüppelholz
• Johannes Lehmann
• Jakob Piribauer

To improve reliability and the understanding of AI systems, there is increasing interest in the use of formal methods, e.g. model checking. Model checking tools produce a counterexample when a model does not satisfy a property. Understanding these counterexamples is critical for efficient debugging, as it allows the developer to focus on the parts of the program that caused the issue. To this end, we present a new technique that ascribes a responsibility value to each state in a transition system that does not satisfy a given safety property. The value is higher if the non-deterministic choices in a state have more power to change the outcome, given the behaviour observed in the counterexample. For this, we employ a concept from cooperative game theory – namely general power indices, such as the Shapley value – to compute the responsibility of the states. We present an optimistic and pessimistic version of responsibility that differ in how they treat the states that do not lie on the counterexample. We give a characterisation of optimistic responsibility that leads to an efficient algorithm for it and show computational hardness of the pessimistic version. We also present a tool to compute responsibility and show how a stochastic algorithm can be used to approximate responsibility in larger models. These methods can be deployed in the design phase, at runtime and at inspection time to gain insights on causal relations within the behavior of AI systems.