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A Mental Model Based Theory of Trust
Handling trust is one of the core requirements for facilitating effective interaction between the human and the AI agent.
A Mental-Model Centric Landscape of Human-AI Symbiosis
Through this paper, we will see how this new framework allows us to capture the various works done in the space of human-AI interaction and identify the fundamental behavioral patterns supported by these works.
Trust-Aware Planning: Modeling Trust Evolution in Iterated Human-Robot Interaction
The problem of trust management is particularly challenging in mixed human-robot teams where the human and the robot may have different models about the task at hand and thus may have different expectations regarding the current course of action, thereby forcing the robot to focus on the costly explicable behavior.
Human-AI Symbiosis: A Survey of Current Approaches
In this paper, we aim at providing a comprehensive outline of the different threads of work in human-AI collaboration.
`Why didn't you allocate this task to them?' Negotiation-Aware Explicable Task Allocation and Contrastive Explanation Generation
Task allocation is an important problem in multi-agent systems.
Inference of Human's Observation Strategy for Monitoring Robot's Behavior based on a Game-Theoretic Model of Trust
Thus, we define the concept of a trust boundary over the mixed strategy space of the human and show that it helps to discover optimal monitoring strategies.
A review of neuro-fuzzy systems based on intelligent control
The system's ability to adapt and self-organize are two key factors when it comes to how well the system can survive the changes to the environment and the plant they work within.
