Learning dialogue strategies using the reinforcement learning framework is problematic due to its expensive computational cost. In this paper we propose an algorithm that reduces a state-action space to one which includes only valid state-actions. We performed experiments on full and reduced spaces using three systems (with 5, 9 and 20 slots) in the travel domain using a simulated environment. The task was to learn multi-goal dialogue strategies optimizing single and multiple confirmations. Average results using strategies learnt on reduced spaces reveal the following benefits against full spaces: 1) less computer memory (94% reduction), 2) faster learning (93% faster convergence) and better performance (8.4% less time steps and 7.7% higher reward).
|Title of host publication||Interspeech 2006 - ICSLP|
|Subtitle of host publication||Ninth International Conference on Spoken Language Processing, Proceedings of the|
|Publication status||Published - 2006|
|Event||Ninth International Conference on Spoken Language Processing (INTERSPEECH 2006 - ICSLP) - Pittsburgh, PA, United States|
Duration: 17 Sep 2006 → 21 Sep 2006
|Conference||Ninth International Conference on Spoken Language Processing (INTERSPEECH 2006 - ICSLP)|
|Period||17/09/06 → 21/09/06|