Multi-Objective Controller Synthesis with Uncertain Human Preferences

TL;DR

This paper introduces a novel method for synthesizing controllers in cyber-physical systems that accounts for uncertain human preferences across multiple objectives, ensuring optimal and scalable solutions.

Contribution

It formalizes uncertain human preferences in multi-objective controller synthesis and develops a mixed-integer linear programming approach for scalable, preference-aware controller synthesis in MDPs.

Findings

The approach is feasible and scalable across various MDP sizes.

Controllers satisfy uncertain human preferences effectively.

User study confirms the quality of synthesized controllers.

Abstract

Complex real-world applications of cyber-physical systems give rise to the need for multi-objective controller synthesis, which concerns 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 demonstrates the quality and benefits of the synthesized controllers.