2022
DOI: 10.1007/s00766-021-00368-y
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A negotiation support system for defining utility functions for multi-stakeholder self-adaptive systems

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Cited by 8 publications
(5 citation statements)
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“…Designing the singular observer (3) for system (2), the problem of finding observation gain is turned into the problem of finding a feasible solution for system (4) that satisfies Theorem 1. So, we will get the next conclusion…”
Section:  =−mentioning
confidence: 99%
“…Designing the singular observer (3) for system (2), the problem of finding observation gain is turned into the problem of finding a feasible solution for system (4) that satisfies Theorem 1. So, we will get the next conclusion…”
Section:  =−mentioning
confidence: 99%
“…Self-adaptation issues in embedded software have been investigated across different areas of software engineering, such as requirements engineering [1][2][3][4], software architecture [5][6][7][8][9][10], middleware architectures [11][12][13][14], component-based development [15][16][17], model-driven development [18][19][20][21] and goal-driven models [22][23][24][25]. The majority of these works have been isolated initiatives.…”
Section: Self-adaptation In Embedded Softwarementioning
confidence: 99%
“…It is also challenging for humans to understand how input variables (e.g., utility function weights) need to be selected to generate desirable plans. While approaches for utility function definition have been proposed in the past (Wohlrab and Garlan, 2021b), it is often unclear to human stakeholders how a defined utility function impacts the generated planning policies. Our approach for quality tradeoff explanations aims to address this issue.…”
Section: Requirementsmentioning
confidence: 99%
“…Among those Pareto-optimal solutions, one solution has to be selected that optimizes the objectives in the most appropriate manner for a given situation/context. To define a utility function that can be used to select a solution, human preferences are needed (Wohlrab and Garlan, 2021b). For realistic systems, however, the state and action spaces are typically large (and may contain hundreds of thousands of states Chen et al, 2020), which makes it difficult for human users to verify that a utility function correctly captures their needs and leads to desirable policies being generated.…”
Section: Introductionmentioning
confidence: 99%