Parra-Ullauri, Juan Marcelo, Garcia-Dominguez, Antonio orcid.org/0000-0002-4744-9150 and Bencomo, Nelly (2021) From a Series of (Un)fortunate Events to Global Explainability of Runtime Model-Based Self-Adaptive Systems. In: Companion Proceedings - 24th International Conference on Model-Driven Engineering Languages and Systems, MODELS-C 2021. 24th International Conference on Model-Driven Engineering Languages and Systems, MODELS-C 2021, 10-15 Oct 2021 Companion Proceedings - 24th International Conference on Model-Driven Engineering Languages and Systems, MODELS-C 2021 . Institute of Electrical and Electronics Engineers Inc. , JPN , pp. 807-816.
Abstract
Self-adaptive systems (SAS) increasingly use AI-based approaches for their flexible decision-making, which often appear to users as 'black boxes'. These systems can exhibit unexpected and surprising behaviours that may violate imposed constraints. Runtime models (RTMs) have been used for SAS management in order to provide capabilities needed to explain reasons why the system present the current emergent behaviour. Existing work on explanations derived from RTMs have focused on justifying why the system has presented a specific behaviour at a given time. Nevertheless, we argue that a more general scope is required for understanding the entire evolution of the system, rather than understanding the behaviour for a given instance or situation. From the point of view of Explainable AI (XAI), the latter type of explanations are called global explanations, whereas understanding a single decision refers to local explanations. Global explanations tend to promote trust on the system in question, while local explanations tend to promote trust on a specific decision. In this paper, we propose the use of event graph models to construct global explanations from evolving RTMs. Event graphs allow the representation of the system behaviour as a state-time diagram, by indicating the occurrence of events and their relationships. RTMs are incrementally queried to look for situations of interest (i.e. events), using Complex Event Processing (CEP) in order to analyze and correlate real-time events and therefore, derive conclusions. The approach is applied to a AI-enhanced SAS in the domain of mobile communications. The encouraging results show that event graphs allow the system to present a summarised overview of the system's behaviour, promoting understandability and trust worthiness.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | Funding Information: This work has been partially sponsored by The Lerverhulme Trust Grant No. RF-2019-548/9 and the EPSRC Research Project Grant No. EP/T017627/1. Publisher Copyright: © 2021 IEEE. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details. |
Keywords: | CEP,Event Graph Models,Global Explainability,Runtime Models,Self-Adaptive Systems,XAI |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 01 May 2024 09:20 |
Last Modified: | 21 Jan 2025 18:27 |
Published Version: | https://doi.org/10.1109/MODELS-C53483.2021.00127 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers Inc. |
Series Name: | Companion Proceedings - 24th International Conference on Model-Driven Engineering Languages and Systems, MODELS-C 2021 |
Identification Number: | 10.1109/MODELS-C53483.2021.00127 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:212150 |
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Description: From a Series of (Un)fortunate Events to Global Explainability of Runtime Model-Based Self-Adaptive Systems