Everyone is familiar with the scenario, people demand or assign tasks to robots, and robots execute the tasks to serve people. We call such a model Serve-on-Demand. With the advancement of pervasive computing, machine learning and artificial intelligence, the robot service of the next generation will inevitably turn to actively and exactly meet people’s needs, even without explicit demand. We call it Serve-on-Need. It requires the robots to comprehend the intentions and preferences of people exactly. In this paper, we model the human-computer interaction for Serve-on-Need as a repeated stochastic Bayesian game. We solve the stochastic Bayesian game by an equilibrium analysis and rational learning. We present the service of a coffee robot to illustrate such an approach.
['Yong Lin', 'Fillia Makedon']
From Serve-on-Demand to Serve-on-Need: A Game Theoretic Approach