Joining the Conversation: Towards Language Acquisition for Ad Hoc Team Play
This addresses language acquisition for AI agents in collaborative settings, but it appears incremental as it builds on existing ad hoc team play concepts with a new modeling approach.
The paper tackles the problem of cooperative language acquisition in ad hoc team play by proposing a probabilistic model that infers speaker intentions and listener semantics from observed communications, assuming positive signaling and listening behaviors while accounting for potential speaker sub-optimality.
In this paper, we propose and consider the problem of cooperative language acquisition as a particular form of the ad hoc team play problem. We then present a probabilistic model for inferring a speaker's intentions and a listener's semantics from observing communications between a team of language-users. This model builds on the assumptions that speakers are engaged in positive signalling and listeners are exhibiting positive listening, which is to say the messages convey hidden information from the listener, that then causes them to change their behaviour. Further, it accounts for potential sub-optimality in the speaker's ability to convey the right information (according to the given task). Finally, we discuss further work for testing and developing this framework.