Learning automata: an introduction
Learning automata: an introduction
Technical Note: \cal Q-Learning
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Autonomous Agents and Multi-Agent Systems
PARADISE: a framework for evaluating spoken dialogue agents
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Spoken Dialogue Technology
DATE: a dialogue act tagging scheme for evaluation of spoken dialogue systems
HLT '01 Proceedings of the first international conference on Human language technology research
Spoken dialogue management using probabilistic reasoning
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
The Knowledge Engineering Review
Networks of Learning Automata: Techniques for Online Stochastic Optimization
Networks of Learning Automata: Techniques for Online Stochastic Optimization
Partially observable Markov decision processes for spoken dialog systems
Computer Speech and Language
Exploring selfish reinforcement learning in repeated games with stochastic rewards
Autonomous Agents and Multi-Agent Systems
Automatic learning of dialogue strategy using dialogue simulation and reinforcement learning
HLT '02 Proceedings of the second international conference on Human Language Technology Research
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
The Hidden Information State model: A practical framework for POMDP-based spoken dialogue management
Computer Speech and Language
Evaluation of a hierarchical reinforcement learning spoken dialogue system
Computer Speech and Language
Optimizing dialogue management with reinforcement learning: experiments with the NJFun system
Journal of Artificial Intelligence Research
The Knowledge Engineering Review
Bayesian update of dialogue state: A POMDP framework for spoken dialogue systems
Computer Speech and Language
A probabilistic framework for dialog simulation and optimal strategy learning
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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This paper addresses the problem of scalable optimisation of dialogue policies in speech-based conversational systems using reinforcement learning. More specifically, for large state spaces several difficulties like large tables, an account of prior knowledge and data sparsity are faced. Hence, we present an online policy learning algorithm based on hierarchical structure learning automata using eligibility trace method to find optimal dialogue strategies that cover large state spaces. The proposed algorithm is capable of deriving an optimal policy that prescribes what action should be taken in various states of conversation so as to maximise the expected total reward to attain the goal and incorporates good exploration and exploitation in its updates to improve the naturalness of human-computer interaction. The proposed model is tested using the most sophisticated evaluation framework PARADISE for accessing the travel information system.