Paper: Learning Optimal Dialogue Management Rules By Using Reinforcement Learning And Inductive Logic Programming

ACL ID N01-1028
Title Learning Optimal Dialogue Management Rules By Using Reinforcement Learning And Inductive Logic Programming
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2001
Authors

Developing dialogue systems is a complex pro- cess. In particular, designing e cient dialogue management strategies is often di cult as there are no precise guidelines to develop them and no sure test to validate them. Several suggestions have been made recently to use reinforcement learning to search for the optimal management strategy for speci c dialogue situations. These approaches have produced interesting results, including applications involving real world dia- logue systems. However, reinforcement learning su ers from the fact that it is state based. In other words, the optimal strategy is expressed as a decision table specifying which action to take in each speci c state. It is therefore di - cult to see whether there is any generality across states. This limits the analysis of th...