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Game Theory-based Human-Assistant Agent Interaction Model: Feasibility Study for a Complex Task

Abstract : As road traffic is becoming increasingly dense, new needs in terms of intelligent human-machine interaction are emerging for their control by human operators. One avenue of research consists in assisting them in their control task by an assistant agent. This paper presents a feasibility study in this field, involving interactions between humans and an assistant agent. For this purpose, a game theory-based model is proposed in order to be able to model a context-sensitive system for the cooperative realization of complex tasks. In this case, the participants of the game are human operators and an assisting agent interacting within the framework of the realization of a control task. Thus, each participants can choose an action between two possible ones (to cooperate or not). Then, the proposed utility functions allow to build the context-sensitive payoff matrix at each observation cycle of the human-machine interaction. To validate our model, we have implemented a simulated control situation; it concerns the regulation of traffic through intersections; this involves two human operators and an assistant agent. Thus, the assistant agent uses the game payoff matrix for its decision-making in using Nash equilibrium. This paper describes a feasibility study, focusing on an analysis of the results obtained during the execution of the simulation. Different research perspectives arise from this study in order to improve and generalize the proposed model
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https://hal-uphf.archives-ouvertes.fr/hal-03331082
Contributor : Frédéric Pruvost Connect in order to contact the contributor
Submitted on : Wednesday, September 1, 2021 - 2:30:30 PM
Last modification on : Friday, June 17, 2022 - 1:33:36 PM

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Martial Razakatiana, Christophe Kolski, René Mandiau, Thomas Mahatody. Game Theory-based Human-Assistant Agent Interaction Model: Feasibility Study for a Complex Task. 8th International Conference on Human-Agent Interaction, Virtual Event (HAI 2020), Nov 2020, Virtual Event, Sydney, Australia. pp.187-195, ⟨10.1145/3406499.3415071⟩. ⟨hal-03331082⟩

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