Explaining Cyber-Physical Systems Using Decision Trees

Plambeck, Swantje; Fey, Görschwin; Schyga, Jakob; Hinckeldeyn, Johannes; Kreutzfeldt, Jochen

doi:10.1109/caadcps56132.2022.00006

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…This model has to be developed manually by experts. In contrast, Plambeck et al [24] propose to learn dependencies on external influences for CPS with decision trees. This approach automatically identifies relevant influences and extracts data-related explanations of the system behavior.…”

Section: Related Workmentioning

confidence: 99%

Cases for Explainable Software Systems: Characteristics and Examples

Sadeghi

Klös

Vogelsang

2021

2021 IEEE 29th International Requirements Engineering Conference Workshops (REW)

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Human explanations are often contrastive, meaning that they do not answer the indeterminate "Why?" question, but instead "Why P, rather than Q?". Automatically generating contrastive explanations is challenging because the contrastive event (Q) represents the expectation of a user in contrast to what happened. We present an approach that predicts a potential contrastive event in situations where a user asks for an explanation in the context of rule-based systems. Our approach analyzes a situation that needs to be explained and then selects the most likely rule a user may have expected instead of what the user has observed. This contrastive event is then used to create a contrastive explanation that is presented to the user. We have implemented the approach as a plugin for a home automation system and demonstrate its feasibility in four test scenarios. CCS CONCEPTS• Software and its engineering → Extra-functional properties;• Human-centered computing → Interactive systems and tools; Ubiquitous and mobile computing.

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Section: Related Workmentioning

confidence: 99%

Cases for Explainable Software Systems: Characteristics and Examples

Sadeghi

Klös

Vogelsang

2021

2021 IEEE 29th International Requirements Engineering Conference Workshops (REW)

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“…We call these models explanation models. These models are either manually constructed at design time (e.g., in [8]), or learned from data (e.g., in [20,28]). In [7], we have proposed the MAB-EX framework that uses explanation models to generate explanations on demand, at run-time.…”

Section: Introductionmentioning

confidence: 99%

From Specification Models to Explanation Models: An Extraction and Refinement Process for Timed Automata

Schwammberger

Klös

2022

Electron. Proc. Theor. Comput. Sci.

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Autonomous systems control many tasks in our daily lives. To increase trust in those systems and safety of the interaction between humans and autonomous systems, the system behaviour and reasons for autonomous decision should be explained to users, experts and public authorities. One way to provide such explanations is to use behavioural models to generate context-and user-specific explanations at run-time. However, this comes at the cost of higher modelling effort as additional models need to be constructed. In this paper, we propose a high-level process to extract such explanation models from system models, and to subsequently refine these towards specific users, explanation purposes and situations. By this, we enable the reuse of specification models for integrating selfexplanation capabilities into systems. We showcase our approach using a running example from the autonomous driving domain.

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