  This paper investigates the use of game theoretic representations to
represent and learn how to play interactive games such as Connect Four. We
combine aspects of learning by demonstration, active learning, and game theory
allowing a robot to leverage its developing representation of the game to
conduct question/answer sessions with a person, thus filling in gaps in its
knowledge. The paper demonstrates a method for teaching a robot the win
conditions of the game Connect Four and its variants using a single
demonstration and a few trial examples with a question and answer session led
by the robot. Our results show that the robot can learn arbitrary win
conditions for the game with little prior knowledge of the win conditions and
then play the game with a human utilizing the learned win conditions. Our
experiments also show that some questions are more important for learning the
game's win conditions. We believe that this method could be broadly applied to
a variety of interactive learning scenarios.
