Paper: Learning Mixed Initiative Dialog Strategies By Using Reinforcement Learning On Both Conversants

ACL ID H05-1127
Title Learning Mixed Initiative Dialog Strategies By Using Reinforcement Learning On Both Conversants
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2005
Authors

This paper describes an application of re- inforcement learning to determine a dia- log policy for a complex collaborative task where policies for both the system and a proxy for a user of the system are learned simultaneously. With this approach a use- ful dialog policy is learned without the drawbacks of other approaches that re- quire significant human interaction. The specific task that the agents were trained on was chosen for its complexity and re- quirement that both conversants bring task knowledge to the interaction, thus ensur- ing its collaborative nature. The results of our experiment show that you can use re- inforcement learning to create an effective dialog policy, which employs a mixed ini- tiative strategy, without the drawbacks of large amounts of data or significant huma...