Dialog Systems based on Markov decision processes over two real tasks

Abstract

In this work the state of the art of the automatic dialogue strategy management using Markov decision processes (MDP) with reinforcement learning (RL) is described. Partially observable Markov decision processes (POMDP) are also described. To test the validity of these methods, two spoken dialogue systems have been developed. The first one is a spoken dialogue system for weather forecast providing, and the second one is a more complex system for train information. With the first system, comparisons between a rule-based system and an automatically trained system have been done, using a real corpus to train the automatic strategy. In the second system, the scalability of these methods when used in larger systems has been tested.