In Autonomous and Intelligent systems (AIS), the decision-making process can be divided into two parts: (i) the priorities of the requirements are determined at design-time; (ii) design selection follows where alternatives are compared, and the preferred alternatives are chosen autonomously by the AIS. Runtime design selection is a trade-off analysis between non-functional requirements (NFRs) that uses optimisation methods, including decision-analysis and utility theory. The aim is to select the design option yielding the highest expected utility. A problem with these techniques is that they use a uni-scalar cumulative utility value to represent a combined priority for all the NFRs. However, this uni-scalar value doesn't give information about the varying impacts of actions under uncertain environmental contexts on the satisfaction priorities of individual NFRs. In this paper, we present a novel use of Multi-Reward Partially Observable Markov Decision Process (MR-POMDP) to support reasoning of separate NFR priorities. We discuss the use of rewards in MR-POMDPs as a way to support AIS with (a) priority-aware decision-making; and (b) maintain service-level agreement, by autonomously tuning NFRs' priorities to new contexts and based on data gathered at runtime. We evaluate our approach by applying it to a substantial Network case.
|Title of host publication||Proceedings of the 36th Annual ACM Symposium on Applied Computing, SAC 2021|
|Number of pages||10|
|Publication status||Published - 22 Mar 2021|
|Name||Proceedings of the 36th Annual ACM Symposium on Applied Computing|
Bibliographical note© 2021 Copyright held by the owner/author(s). CC BY
Funding: This work has been partially supported by The Lerverhulme Trust Fellowship "QuantUn: quantification of uncertainty using Bayesian surprises" (Grant No. RF-2019-548/9) and the EPSRC Research Project Twenty20Insight (Grant No. EP/T017627/1).
- autonomous and intelligent software systems
- non-functional requirements
- runtime models