An Overview on the Explainability of Cyber-Physical Systems
Journal
Vol. 35 (2022): Proceedings of FLAIRS-35
Type
conference paper
Date Issued
2022-05-04
Author(s)
Abstract (De)
The increase in automating complicated physical processes
using Cyber-Physical Systems (CPS) raises the complexity of
CPS and their behavior. It creates the necessity to make them
explainable. The popular Explainable Artificial Intelligence
(XAI) methodologies employed to explain the behavior of
CPS usually overlook the impact of physical and virtual context when explaining the outputs of decision-making software
models, which are essential factors in explaining CPS’ behavior to stakeholders. Hence in this article, we survey the most
relevant XAI methods to identify their shortcomings and applicability in explaining the behavior of CPS. Our main findings are (i) Several papers emphasize the relevance of context
in describing CPS. However, the approaches for explaining
CPS fall short of being context-aware; (ii) the explanation
delivery mechanisms use low-level visualization tools that
make the explanations unintelligible. Finally (iii), these unintelligible explanations lack actionability. Therefore, we propose to enrich the explanations further with contextual information using Semantic Technologies, user feedback, and enhanced explanation visualization techniques to improve their
understandability. To that end, context-aware explanation and
better explanation presentation based on knowledge graphs
might be a promising research direction for explainable CPS
using Cyber-Physical Systems (CPS) raises the complexity of
CPS and their behavior. It creates the necessity to make them
explainable. The popular Explainable Artificial Intelligence
(XAI) methodologies employed to explain the behavior of
CPS usually overlook the impact of physical and virtual context when explaining the outputs of decision-making software
models, which are essential factors in explaining CPS’ behavior to stakeholders. Hence in this article, we survey the most
relevant XAI methods to identify their shortcomings and applicability in explaining the behavior of CPS. Our main findings are (i) Several papers emphasize the relevance of context
in describing CPS. However, the approaches for explaining
CPS fall short of being context-aware; (ii) the explanation
delivery mechanisms use low-level visualization tools that
make the explanations unintelligible. Finally (iii), these unintelligible explanations lack actionability. Therefore, we propose to enrich the explanations further with contextual information using Semantic Technologies, user feedback, and enhanced explanation visualization techniques to improve their
understandability. To that end, context-aware explanation and
better explanation presentation based on knowledge graphs
might be a promising research direction for explainable CPS
Language
English
HSG Classification
contribution to scientific community
Refereed
Yes
Publisher
The Florida Artificial Intelligence Society
Publisher place
Florida, USA
Subject(s)
Division(s)
Eprints ID
266123
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An Overview on the Explainability of Cyber-Physical Systems.pdf
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Format
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