INTERSPEECH 2006 - ICSLP
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).
Bibliographic reference. Cuayįhuitl, Heriberto / Renals, Steve / Lemon, Oliver / Shimodaira, Hiroshi (2006): "Learning multi-goal dialogue strategies using reinforcement learning with reduced state-action spaces", In INTERSPEECH-2006, paper 1282-Mon2FoP.6.