Learning mixed initiative dialog strategies by using reinforcement learning on both conversants Export

@inproceedings{citeulike:5183886, abstract = {This paper describes an application of reinforcement learning to determine a dialog 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 useful dialog policy is learned without the drawbacks of other approaches that require significant human interaction. The specific task that the agents were trained on was chosen for its complexity and requirement that both conversants bring task knowledge to the interaction, thus ensuring its collaborative nature. The results of our experiment show that you can use reinforcement learning to create an effective dialog policy, which employs a mixed initiative strategy, without the drawbacks of large amounts of data or significant human input.}, address = {Morristown, NJ, USA}, author = {English, Michael S. and Heeman, Peter A.}, booktitle = {HLT '05: Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing}, citeulike-article-id = {5183886}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1220575.1220702}, citeulike-linkout-1 = {http://dx.doi.org/10.3115/1220575.1220702}, doi = {10.3115/1220575.1220702}, keywords = {dialogue, reinforcement-learning}, location = {Vancouver, British Columbia, Canada}, pages = {1011--1018}, posted-at = {2009-07-16 19:23:48}, priority = {0}, publisher = {Association for Computational Linguistics}, title = {Learning mixed initiative dialog strategies by using reinforcement learning on both conversants}, url = {http://dx.doi.org/10.3115/1220575.1220702}, year = {2005} }

TY - CONF ID - citeulike:5183886 L3 - citeulike-article-id:5183886 N2 - This paper describes an application of reinforcement learning to determine a dialog 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 useful dialog policy is learned without the drawbacks of other approaches that require significant human interaction. The specific task that the agents were trained on was chosen for its complexity and requirement that both conversants bring task knowledge to the interaction, thus ensuring its collaborative nature. The results of our experiment show that you can use reinforcement learning to create an effective dialog policy, which employs a mixed initiative strategy, without the drawbacks of large amounts of data or significant human input. CY - Vancouver, British Columbia, Canada AD - Morristown, NJ, USA EP - 1018 TI - Learning mixed initiative dialog strategies by using reinforcement learning on both conversants PB - Association for Computational Linguistics T2 - HLT '05: Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing SP - 1011 KW - dialogue KW - reinforcement-learning AU - English, Michael AU - Heeman, Peter PY - 2005/// UR - http://dx.doi.org/10.3115/1220575.1220702 ER -