Multi-Objective Controller Synthesis with Uncertain Human Preferences
This addresses the challenge of designing controllers for cyber-physical systems where human decision-makers have uncertain preferences, representing an incremental advance in handling preference uncertainty in synthesis methods.
The paper tackles the problem of multi-objective controller synthesis for Markov decision processes by accounting for uncertain human preferences, using a mixed-integer linear programming approach to synthesize optimally permissive multi-strategies, with experimental results showing feasibility and scalability across varying model sizes and uncertainty levels.
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.