Abstract
Trust is a critical issue in human–robot interactions: as robotic systems gain complexity, it becomes crucial for them to be able to blend into our society by maximizing their acceptability and reliability. Various studies have examined how trust is attributed by people to robots, but fewer have investigated the opposite scenario, where a robot is the trustor and a human is the trustee. The ability for an agent to evaluate the trustworthiness of its sources of information is particularly useful in joint task situations where people and robots must collaborate to reach shared goals. We propose an artificial cognitive architecture based on the developmental robotics paradigm that can estimate the trustworthiness of its human interactors for the purpose of decision making. This is accomplished using Theory of Mind (ToM), the psychological ability to assign to others beliefs and intentions that can differ from one’s owns. Our work is focused on a humanoid robot cognitive architecture that integrates a probabilistic ToM and trust model supported by an episodic memory system. We tested our architecture on an established developmental psychological experiment, achieving the same results obtained by children, thus demonstrating a new method to enhance the quality of human and robot collaborations.This article is part of the theme issue ‘From social brains to social robots: applying neurocognitive insights to human–robot interaction’.
Highlights
The technological revolution taking place in the fields of robotics and artificial intelligence seems to indicate a future shift in our human-centred social paradigm towards a greater inclusion of artificial cognitive agents in our everyday environments
Research regarding trust in human–robot interactions (HRI) assumes a major importance in order to ensure the highest quality of the interaction itself, as trust directly affects the willingness of people to accept information produced by a robot and to cooperate with it
Many studies have already explored trust that humans give to robots and how this can be enhanced by tuning both the design and the behaviour of the machine, but not so much research has focused on the opposite scenario, that is the trust that artificial agents can assign to people
Summary
The technological revolution taking place in the fields of robotics and artificial intelligence seems to indicate a future shift in our human-centred social paradigm towards a greater inclusion of artificial cognitive agents in our everyday environments. This means that collaborative scenarios between humans and robots will become more frequent and will have a deeper impact on everyday life. Many studies have already explored trust that humans give to robots and how this can be enhanced by tuning both the design and the behaviour of the machine, but not so much research has focused on the opposite scenario, that is the trust that artificial agents can assign to people. We have tested our architecture on an established developmental psychology experiment [1] and the results we obtained confirm that our approach successfully models trust mechanisms and dynamics in cognitive robots
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