FRB-Dialog: A Toolkit for Automatic Learning of Fuzzy-Rule Based (FRB) Dialog Managers

This paper describes a toolkit designed to automatically develop dialog managers for spoken dialog system based on evolving Fuzzy-rule-based (FRB) classifiers. The FRB-dialog toolkit allows to develop dialog managers selecting the next system action by considering a set of dynamic rules that are automatically obtained by means of the application of the FRB classification process. Our approach bridges the gap between the academic and industrial perspectives for developing dialog systems, taking into account the data supplied by the user throughout the complete dialog history without causing scalability problems, and also considering confidence measures provided by the recognition and understanding modules.

[1]  H. Cuayahuitl,et al.  Human-computer dialogue simulation using hidden Markov models , 2005, IEEE Workshop on Automatic Speech Recognition and Understanding, 2005..

[2]  Milica Gasic,et al.  POMDP-Based Statistical Spoken Dialog Systems: A Review , 2013, Proceedings of the IEEE.

[3]  Roberto Pieraccini,et al.  A stochastic model of human-machine interaction for learning dialog strategies , 2000, IEEE Trans. Speech Audio Process..

[4]  Ramón López-Cózar,et al.  A domain-independent statistical methodology for dialog management in spoken dialog systems , 2014, Comput. Speech Lang..

[5]  Plamen P. Angelov,et al.  Evolving Fuzzy Classifier for Novelty Detection and Landmark Recognition by Mobile Robots , 2007, Mobile Robots.

[6]  Roberto Pieraccini,et al.  The use of belief networks for mixed-initiative dialog modeling , 2000, IEEE Trans. Speech Audio Process..

[7]  Encarna Segarra,et al.  An Online Generated Transducer to Increase Dialog Manager Coverage , 2012, INTERSPEECH.

[8]  David Suendermann-Oeft,et al.  One Year of Contender: What Have We Learned about Assessing and Tuning Industrial Spoken Dialog Systems? , 2012, SDCTD@NAACL-HLT.

[9]  David Suendermann,et al.  Data-Driven Methods in Industrial Spoken Dialog Systems , 2012 .

[10]  Plamen P. Angelov,et al.  Robust classification of low-grade cervical cytology following analysis with ATR-FTIR spectroscopy and subsequent application of self-learning classifier eClass , 2010, Analytical and bioanalytical chemistry.

[11]  Yorick Wilks,et al.  Some background on dialogue management and conversational speech for dialogue systems , 2011, Comput. Speech Lang..

[12]  Marilyn A. Walker,et al.  Reinforcement Learning for Spoken Dialogue Systems , 1999, NIPS.

[13]  David Griol,et al.  AN AUTOMATIC DIALOG SIMULATION TECHNIQUE TO DEVELOP AND EVALUATE INTERACTIVE CONVERSATIONAL AGENTS , 2013, Appl. Artif. Intell..

[14]  Plamen P. Angelov,et al.  Evolving social network analysis: A case study on mobile phone data , 2012, 2012 IEEE Conference on Evolving and Adaptive Intelligent Systems.

[15]  Eric Horvitz,et al.  Conversation as Action Under Uncertainty , 2000, UAI.

[16]  David Griol,et al.  The Conversational Interface: Talking to Smart Devices , 2016 .

[17]  Joelle Pineau,et al.  Spoken Dialogue Management Using Probabilistic Reasoning , 2000, ACL.

[18]  Gary Geunbae Lee,et al.  Hybrid approach to robust dialog management using agenda and dialog examples , 2010, Comput. Speech Lang..

[19]  Steve J. Young,et al.  A survey of statistical user simulation techniques for reinforcement-learning of dialogue management strategies , 2006, The Knowledge Engineering Review.

[20]  Plamen P. Angelov,et al.  Evolving Fuzzy-Rule-Based Classifiers From Data Streams , 2008, IEEE Transactions on Fuzzy Systems.