Multi-objective controller synthesis with uncertain human preferences

Shenghui Chen, Kayla Boggess, David Parker, Lu Feng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Complex real-world applications of cyber-physical systems give rise to the need for multi-objective controller synthesis, which con-cerns the problem of computing an optimal controller subject to multiple (possibly conflicting) criteria. The relative importance of objectives is often specified by human decision-makers. However, there is inherent uncertainty in human preferences (e.g., due to artifacts resulting from different preference elicitation methods). In this paper, we formalize the notion of uncertain human preferences, and present a novel approach that accounts for this uncertainty in the context of multi-objective controller synthesis for Markov decision processes (MDPs). Our approach is based on mixed-integer linear programming and synthesizes an optimally permissive multi-strategy that satisfies uncertain human preferences with respect to a multi-objective property. Experimental results on a range of large case studies show that the proposed approach is feasible and scalable across varying MDP model sizes and uncertainty levels of human preferences. Evaluation via an online user study also demon-strates the quality and benefits of the synthesized controllers.
Original languageEnglish
Title of host publication2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS)
Place of PublicationLos Alamitos, California, Washington, Tokyo
Number of pages11
ISBN (Electronic)978-1-6654-0967-4
ISBN (Print)978-1-6654-0968-1
Publication statusE-pub ahead of print - 24 Jun 2022
Event13th ACM/IEEE International Conference on Cyber-Physical Systems - Virtual
Duration: 4 May 20226 May 2022

Publication series

NameACM/IEEE International Conference on Cyber-Physical Systems
ISSN (Print)2375-8317
ISSN (Electronic)2642-9500


Conference13th ACM/IEEE International Conference on Cyber-Physical Systems
Abbreviated titleICCPS2022

Bibliographical note

Funding Information:
This work was supported in part by National Science Foundation grants CCF-1942836 and CNS-1755784, and European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 834115, FUN2MODEL). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the grant sponsors.

Publisher Copyright:
© 2022 IEEE.


  • Markov Decision Processes
  • Uncertain Human Preferences
  • Multi-Objective Controller Synthesis

ASJC Scopus subject areas

  • Control and Optimization
  • Artificial Intelligence
  • Hardware and Architecture
  • Computer Networks and Communications
  • Computer Science Applications


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