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. © 2019 IEEE. ; QC 20200113
Human-in-the-loop control synthesis for multi-agent systems under hard and soft metric interval temporal logic specifications∗
2019-01-01
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Control system synthesis , Reinforcement learning , Temporal logic , Control synthesis , Hard and soft constraints , Human-in-the-loop control , Interval temporal logic , Inverse reinforcement learning , Mixed initiative , Optimal paths , Soft constraint , Multi agent systems , Control Engineering , Reglerteknik
BASE | 2018
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