Human-in-the-Loop Control Synthesis for Multi-Agent Systems under Hard and Soft Metric Interval Temporal Logic Specifications

In this paper we present a control synthesis framework for a multi-agent system under hard and soft constraints, which performs online re-planning to achieve collision avoidance and execution of the optimal path with respect to some human preference considering the type of the violation of the soft constraints. The human preference is indicated by a mixed initiative controller and the resulting change of trajectory is used by an inverse reinforcement learning based algorithm to improve the path which the affected agent tries to follow. A case study is presented to validate the result.