Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System
Optimizing Dialogue Management with Reinforcement Learning: Experiments with the NJFun System
Designing the dialogue policy of a spoken dialogue system involves many nontrivial choices. This paper presents a reinforcement learning approach for automatically optimizing a dialogue policy, which addresses the technical challenges in applying reinforcement learning to a working dialogue system with human users. We report on the design, construction and …