Game Theory and Human-Robot Interactions
https://www.nature.com/articles/s42256-018-0010-3
In January, a research team led by the University of Sussex for the first time managed to use game theory and the concept of Nash equilibrium – the state that a system will reach given each “player” in the game attempting to use the best response strategies to each other – in programming robots that can interact better with humans. Previously, most such attempts focused either on complete control by the user, or a fixed pattern that cannot easily adapt to potential changing requirements and needs of the human. Game theory had been suggested prior as a framework to model interactions between the two entities, but the problem was that conventionally each partner requires complete knowledge of the dynamics and strategies used by the other. The University of Sussex team, however, managed to get around that limitation by using adaptive techniques in order to learn and model the human’s behavior as a type of controller, and then applying game theory and the robot’s knowledge of that control to compute a Nash equilibrium for the system. This allows for a stable interaction between the two “players,” enabling them to optimally work together on a task.
The published paper focused primarily on physical interaction between the human and the robot, such as in physical rehabilitation, where doing so could be most helpful. In the example mentioned in the paper, the researchers conducted an experiment to validate the procedure of arm reaching movements of patients. However, it seems very conceivable that the underlying principles behind the method could also be applied to social interaction between robots and humans. After all, human interactions often can be modeled using similar game theory techniques, since in many situations, much of what we do can be boiled down to finding the optimal response to the needs, questions, or statements of our counterpart. Extending this to a social context needs to connect further to the concept of networks, as the strategies of the human user that the robot would have to consider would depend on the state of the entire network, as well as how its behavior changes over time.
In short, this paper shows that the ideas of game theory have far broader-reaching applications than just to fields of economics and politics, the most commonly known examples. This application of game theory to robotics seems to have the potential to become an important player in the near future as technology and AI advance.