Robots That Learn Language: A Developmental Approach to Situated Human-Robot Conversations

Abstract

8.1 Sharing the risk of being misunderstood The experiments in learning a pragmatic capability illustrate the importance of sharing the risk of not being understood correctly between the user and the robot. In the learning period for utterance understanding by the robot, the values of the local confidence parameters changed significantly when the robot acted incorrectly in the first trial and correctly in the second trial. To facilitate learning, the user had to gradually increase the ambiguity of utterances according to the robot's developing ability to understand them and had to take the risk of not being understood correctly. In the its learning period for utterance generation, the robot adjusted its utterances to the user while learning the global confidence function. When the target understanding rate was set to 0.95, the global confidence function became very unstable in cases where the robot's expectations of being understood correctly at a high probability were not met. This instability could be prevented by using a lower value of , which means that the robot would have to take a greater risk of not being understood correctly. Accordingly, in human-machine interaction, both users and robots must face the risk of not being understood correctly and thus adjust their actions to accommodate such risk in order to effectively couple their belief systems. Although the importance of controlling the risk of error in learning has generally been seen as an exploration-exploitation trade-off in the field of reinforcement learning by machines (e.g., (Dayan & Sejnowski, 1996)), we argue here that the mutual accommodation of the risk of error by those communicating is an important basis for the formation of mutual understanding. 8.2 Incomplete observed information and fast adaptation In general, an utterance does not contain complete information about what a speaker wants to convey to a listener. The proposed learning method interpreted such utterances according to the situation by providing necessary but missing information by making use of the assumption of shared beliefs. The method also enabled the robot and the user to adapt such an assumption of shared beliefs to each other with little interaction. We can say that the method successfully