SEAMS 2023
Mon 15 - Tue 16 May 2023 Melbourne, Australia
co-located with ICSE 2023

Decision making in self-adaptive systems often involves trade-offs between multiple quality attributes, with user preferences that indicate the relative importance and priorities among the attributes. However, eliciting such preferences accurately from users is a difficult task, as they may find it challenging to specify their preference in a precise, mathematical form. Instead, they may have an easier time expressing their displeasure when the system does not exhibit behaviors that satisfy their internal preferences. Furthermore, the user’s preference may change over time depending on the environmental context; thus, the system may be required to continuously adapt its behavior to satisfy this change in preference. However, existing self-adaptive frameworks do not explicitly consider dynamic human preference as one of the sources of uncertainty. In this paper, we propose a new adaptation framework that is specifically designed to support self-adaptation to user preference. Our framework takes a human-on-the-loop approach where the user is given an ability to intervene and indicate dissatisfaction and corrections with the current behavior of the system; in such a scenario, the system automatically updates the existing preference values so that the new, resulting behavior of the system is consistent with the user’s notion of satisfactory behavior. To perform this adaptation, we propose a novel similarity analysis to produce changes in the preference that are optimal with respect to the system utility. We illustrate our approach in a case study involving a delivery robot system. Our preliminary results indicate that our approach can effectively adapt its behavior to changing human preference.

Tue 16 May

Displayed time zone: Hobart change

11:00 - 12:30
Session 5: Runtime decision-making and human in the loopResearch Track / Artifact Track at Meeting Room 105
Chair(s): Amel Bennaceur The Open University, UK
11:00
25m
Paper
Runtime Verification of Self-Adaptive Systems with Changing Requirements
Research Track
Marc Carwehl Humboldt-Universität zu Berlin, Thomas Vogel Humboldt-Universtität zu Berlin, Genaína Nunes Rodrigues University of Brasília, Lars Grunske Humboldt-Universität zu Berlin
Pre-print
11:25
25m
Paper
Runtime Resolution of Feature Interactions through Adaptive Requirement Weakening
Research Track
Simon Chu , Emma Shedden , Changjian Zhang Carnegie Mellon University, Rômulo Meira-Góes Carnegie Mellon University, Gabriel A. Moreno Carnegie Mellon University Software Engineering Institute, David Garlan Carnegie Mellon University, Eunsuk Kang Carnegie Mellon University
Pre-print
11:50
15m
Short-paper
Architecture-based Uncertainty Impact Analysis to ensure Confidentiality
Research Track
Sebastian Hahner Karlsruhe Institute of Technology (KIT), Robert Heinrich Karlsruhe Institute of Technology (KIT), Ralf Reussner Karlsruhe Institute of Technology (KIT) and FZI - Research Center for Information Technology (FZI)
12:05
25m
Paper
Preference Adaptation: user satisfaction is all you need!
Research Track
NIANYU LI Peking University, China, Mingyue Zhang Peking University, China, Jialong Li Waseda University, Japan, Eunsuk Kang Carnegie Mellon University, Kenji Tei Waseda University
Pre-print