Advantages of decision lists and implicit negatives in inductive logic programming
New Generation Computing
Machine Learning
Predicting and Adapting to Poor Speech Recognition in a Spoken Dialogue System
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Empirical Evaluation of a Reinforcement Learning Spoken Dialogue System
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Learning optimal dialogue strategies: a case study of a spoken dialogue agent for email
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Automatic optimization of dialogue management
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Spoken dialogue management using probabilistic reasoning
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
Journal of Artificial Intelligence Research
Induction of first-order decision lists: results on learning the past tense of English verbs
Journal of Artificial Intelligence Research
ICML '04 Proceedings of the twenty-first international conference on Machine learning
An Inductive Logic Programming Approach to Statistical Relational Learning
Proceedings of the 2005 conference on An Inductive Logic Programming Approach to Statistical Relational Learning
Evolving optimal inspectable strategies for spoken dialogue systems
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
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Developing dialogue systems is a complex process. In particular, designing efficient dialogue management strategies is often difficult 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 specific dialogue situations. These approaches have produced interesting results, including applications involving real world dialogue systems. However, reinforcement learning suffers 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 specific state. It is therefore difficult to see whether there is any generality across states. This limits the analysis of the optimal strategy and its potential for re-use in other dialogue situations. In this paper we tackle this problem by learning rules that generalize the state-based strategy. These rules are more readable than the underlying strategy and therefore easier to explain and re-use. We also investigate the capability of these rules in directing the search for the optimal strategy by looking for generalization whilst the search proceeds.