Item Type |
Conference or Workshop Item
(Paper)
|
Abstract |
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 |
Authors |
Jha, Sanjiv |
Journal or Publication Title |
Vol. 35 (2022): Proceedings of FLAIRS-35 |
Language |
English |
Subjects |
computer science |
HSG Classification |
contribution to scientific community |
Refereed |
Yes |
Date |
4 May 2022 |
Publisher |
The Florida Artificial Intelligence Society |
Place of Publication |
Florida, USA |
Publisher DOI |
https://doi.org/10.32473/flairs.v35i.130646 |
Depositing User |
Sanjiv Jha
|
Date Deposited |
13 Apr 2022 12:30 |
Last Modified |
25 May 2022 11:30 |
URI: |
https://www.alexandria.unisg.ch/publications/266123 |