Parra-Ullauri, Juan and Garcia-Dominguez, Antonio and Bencomo, Nelly and Garcia-Paucar, Luis (2022) 'History-aware explanations: towards enabling human-in-the-loop in self-adaptive systems.', MODELS '22: 25th International Conference on Model Driven Engineering Languages and Systems Montreal, Canada, 23-28 Oct 2022.
The complexity of real-world problems requires modern software systems to autonomously adapt and modify their behaviour at run time to deal with internal and external challenges and contexts. Consequently, these self-adaptive systems (SAS) can show unexpected and surprising behaviours to users, who may not understand or agree with them. This is exacerbated due to the ubiquity and complexity of AI-based systems which are often considered as "black-boxes". Users may feel that the decision-making process of SAS is oblivious to the user's own decision-making criteria and priorities. Inevitably, users may mistrust or even avoid using the system. Furthermore, SAS could benefit from the human involvement in satisfying stakeholders' requirements. Accordingly, it is argued that a system should be able to explain its behaviour and how it has reached its current state. A history-aware, human-in-the-loop approach to address these issues is presented in this paper. For this approach, the system should i) offer access and retrieval of historic data about the past behaviour of the system, ii) track over time the reasons for its decisions to show and explain them to the users, and iii) provide capabilities, called effectors, to empower users by allowing them to steer the decision-making based on the information provided by i) and ii). This paper looks into enabling a human-in-the-loop approach into the decision-making of SAS based on the MAPE-K architecture. We present a feedback layer based on temporal graph databases (TGDB) that has been added to the MAPE-K architecture to provide a two-way communication between the human and the SAS. Collaboration, communication and trustworthiness between the human and SAS is promoted by the provision of history-based explanations extracted from the TGDB, and a set of effectors allow human users to influence the system based on the received information. The encouraging results of an application of the approach to a network management case study and a validation from a SAS expert are shown.
|Item Type:||Conference item (Paper)|
|Full text:||Publisher-imposed embargo |
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|Publisher Web site:||https://doi.org/10.1145/3550356.3561538|
|Publisher statement:||© 2022 Copyright held by the owner/author(s). Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).|
|Date accepted:||25 August 2022|
|Date deposited:||23 September 2022|
|Date of first online publication:||09 November 2022|
|Date first made open access:||08 March 2023|
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