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...

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Bibliographic Details
Published inIEEE International Conference on Automation Science and Engineering (CASE) pp. 788 - 793
Main Authors Ahlberg, Sofie, Dimarogonas, Dimos V.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2019
Subjects
Automata
Clocks
Multi-agent systems
Reinforcement learning
Safety
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Summary: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.
ISSN:2161-8089
DOI:10.1109/COASE.2019.8842954