Understanding Dialogue Acts by Bayesian Inference and Reinforcement Learning

evel (Austin 1962). DAs constitute the most fundamental part of communication, and the comprehension of DAs is essential to human-agent interaction. The purpose of this study is to enable an agent to behave properly in response to DAs without their explicit representation on one hand and to estimate the DAs explicitly on the other hand. The former is realized by reinforcement learning and the latter by Bayesian inference. The simulation results demonstrated that the agent not only responded to DAs successfully but also inferred the DAs correctly.