Attention, intentions, and the structure of discourse
Computational Linguistics
Informational redundancy and resource bounds in dialogue
Informational redundancy and resource bounds in dialogue
The effect of resource limits and task complexity on collaborative planning in dialogue
Artificial Intelligence - Special volume on empirical methods
Reinforcement learning with hierarchies of machines
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
Autonomous Agents and Multi-Agent Systems
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Towards developing general models of usability with PARADISE
Natural Language Engineering
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
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
Tractable planning under uncertainty: exploiting structure
Tractable planning under uncertainty: exploiting structure
Partially observable Markov decision processes for spoken dialog systems
Computer Speech and Language
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Probabilistic simulation of human-machine dialogues
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 02
Developing attribute acquisition strategies in spoken dialogue systems via user simulation
Developing attribute acquisition strategies in spoken dialogue systems via user simulation
Automatic learning of dialogue strategy using dialogue simulation and reinforcement learning
HLT '02 Proceedings of the second international conference on Human Language Technology Research
Hybrid reinforcement/supervised learning of dialogue policies from fixed data sets
Computational Linguistics
Journal of Artificial Intelligence Research
Hierarchical reinforcement learning with the MAXQ value function decomposition
Journal of Artificial Intelligence Research
Optimizing dialogue management with reinforcement learning: experiments with the NJFun system
Journal of Artificial Intelligence Research
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Fast reinforcement learning of dialogue policies using stable function approximation
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Hierarchical reinforcement learning for adaptive text generation
INLG '10 Proceedings of the 6th International Natural Language Generation Conference
Generating adaptive route instructions using hierarchical reinforcement learning
SC'10 Proceedings of the 7th international conference on Spatial cognition
Spatially-aware dialogue control using hierarchical reinforcement learning
ACM Transactions on Speech and Language Processing (TSLP)
Optimising natural language generation decision making for situated dialogue
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
Multi-policy dialogue management
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
Learning automata-based approach to learn dialogue policies in large state space
International Journal of Intelligent Information and Database Systems
An adaptive dialogue system with online dialogue policy learning
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
A comparative study of reinforcement learning techniques on dialogue management
EACL '12 Proceedings of the Student Research Workshop at the 13th Conference of the European Chapter of the Association for Computational Linguistics
Comparing HMMs and Bayesian networks for surface realisation
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
An interactive humanoid robot exhibiting flexible sub-dialogues
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Demonstration Session
Optimising incremental dialogue decisions using information density for interactive systems
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Probabilistic dialogue models with prior domain knowledge
SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Modeling spoken dialog systems under the interactive pattern recognition framework
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
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We describe an evaluation of spoken dialogue strategies designed using hierarchical reinforcement learning agents. The dialogue strategies were learnt in a simulated environment and tested in a laboratory setting with 32 users. These dialogues were used to evaluate three types of machine dialogue behaviour: hand-coded, fully-learnt and semi-learnt. These experiments also served to evaluate the realism of simulated dialogues using two proposed metrics contrasted with 'Precision-Recall'. The learnt dialogue behaviours used the Semi-Markov Decision Process (SMDP) model, and we report the first evaluation of this model in a realistic conversational environment. Experimental results in the travel planning domain provide evidence to support the following claims: (a) hierarchical semi-learnt dialogue agents are a better alternative (with higher overall performance) than deterministic or fully-learnt behaviour; (b) spoken dialogue strategies learnt with highly coherent user behaviour and conservative recognition error rates (keyword error rate of 20%) can outperform a reasonable hand-coded strategy; and (c) hierarchical reinforcement learning dialogue agents are feasible and promising for the (semi) automatic design of optimized dialogue behaviours in larger-scale systems.