Operational Causality – Necessarily Sufficient and Sufficiently Necessary

Baier, Christel; Dubslaff, Clemens; Funke, Florian; Jantsch, Simon; Piribauer, Jakob; Ziemek, Robin

doi:10.1007/978-3-031-15629-8_2

Cited by 4 publications

(4 citation statements)

References 60 publications

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“…Notions of necessary causality are typically forward-looking: A necessary cause C for an effect E is an event that occurs on every execution that exhibits the effect E (see, e.g., [3], and for a philosophical analysis of necessity in causes [28]). The backward view naturally arises when the task is to explain what went wrong after an undesired effect has been observed.…”

Section: Forward-and Backward-looking Causalitymentioning

confidence: 99%

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Counterfactual Causality for Reachability and Safety based on Distance Functions

Parreaux,

Piribauer,

Baier

2023

Electron. Proc. Theor. Comput. Sci.

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Investigations of causality in operational systems aim at providing human-understandable explanations of why a system behaves as it does. There is, in particular, a demand to explain what went wrong on a given counterexample execution that shows that a system does not satisfy a given specification. To this end, this paper investigates a notion of counterfactual causality in transition systems based on Stalnaker's and Lewis' semantics of counterfactuals in terms of most similar possible worlds and introduces a novel corresponding notion of counterfactual causality in two-player games. Using distance functions between paths in transition systems to capture the similarity of executions, this notion defines whether reaching a certain set of states is a cause for the fact that a given execution of a system satisfies an undesirable reachability or safety property. Similarly, using distance functions between memoryless strategies in reachability and safety games, it is defined whether reaching a set of states is a cause for the fact that a given strategy for the player under investigation is losing.The contribution of the paper is two-fold: In transition systems, it is shown that counterfactual causality can be checked in polynomial time for three prominent distance functions between paths. In two-player games, the introduced notion of counterfactual causality is shown to be checkable in polynomial time for two natural distance functions between memoryless strategies. Further, a notion of explanation that can be extracted from a counterfactual cause and that pinpoints changes to be made to the given strategy in order to transform it into a winning strategy is defined. For the two distance functions under consideration, the problem to decide whether such an explanation imposes only minimal necessary changes to the given strategy with respect to the used distance function turns out to be coNP-complete and not to be solvable in polynomial time if P is not equal to NP, respectively.

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Section: Forward-and Backward-looking Causalitymentioning

confidence: 99%

“…Checking causality is shown to be decidable by a formulation as a hyperlogic model-checking problem. Furthermore, notions of necessary and sufficient causes as sets of states in transition systems have been considered [3]. These do not rely on the counterfactuality principle and are of forward-looking nature.…”

Section: Contributionsmentioning

confidence: 99%

Counterfactual Causality for Reachability and Safety based on Distance Functions

Parreaux,

Piribauer,

Baier

2023

Electron. Proc. Theor. Comput. Sci.

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“…To our knowledge, no theory of causality has been applied to interactions with robots. Baier et al [1] have conducted a survey on published approaches that utilise Halpern's notion of causality. We summarise some of the most prominent related work below.…”

Section: Related Workmentioning

confidence: 99%

“…Causality has intrigued philosophers since ancient times. Modern theories of causality were put forward by philosophers such as Hume and Lewis, and they found applications in engineering, particularly in explaining the results of testing and verification [1].…”

Section: Introductionmentioning

confidence: 99%

Kaspar Causally Explains

Araujo¹,

Holthaus²,

Gou³

et al. 2022

Lecture Notes in Computer Science

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The Kaspar robot has been used with great success to work as an education and social mediator with children with autism spectrum disorder. Enabling the robot to automatically generate causal explanations is key to enrich the interaction scenarios for children and promote trust in the robot. We present a theory of causal explanation to be embedded in Kaspar. Based on this theory, we build a causal model and an analysis method to calculate causal explanations. We implement our method in Java with inputs provided by a human operator. This model automatically generates the causal explanation that are then spoken by Kaspar. We validate our explanations for user satisfaction in an empirical evaluation.

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