Argumentation system for intelligent assistants using fuzzy-based reasoning. T. Koivuaho (), M. Ibrahim (), F. Ummul (), and M. Oussalah () Data Science and Knowledge Engineering for Sensing Decision Support. October 2018, 608-616, https://doi.org/10.1142/9789813273238_0078
Argumentation system for intelligent assistants using fuzzy-based reasoning
|Author:||Koivuaho, T.1; Ibrahim, M.1; Ummul, F.1;|
1Centre for Ubiquitous Computing, Faculty of Information Technology and Electrical Engineering, University of Oulu. OULU, 90014- FINLAND
|Online Access:||PDF Full Text (PDF, 0.2 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020042119541
|Publish Date:|| 2020-04-21
This paper addresses the issue of building intelligent assistant that is able to maintain sustainable conversation with the user while taking into account his emotional, personality aspects, and, at the same time, maintaining high level focus. The approach makes use four meta-features; namely, topic, emotion, personality and dialogue-act. A sequence-to sequence recurrent neural network approach was used to learn answer prototypes from Reddit.com sport corpus, while an ANFIS based approach was developed to extrapolate from the limited configurations used by the neural network to various dialogue utterances.
World Scientific proceedings series on computer engingeering and information science
|Pages:||608 - 616|
Data Science and Knowledge Engineering for Sensing Decision Support. Proceedings of the 13th International FLINS Conference (FLINS 2018)
|Host publication editor:||
Kerre, Etienne E.
International Conference on Data Science and Knowledge Engineering for Sensing Decision Support
|Type of Publication:||
A4 Article in conference proceedings
|Field of Science:||
113 Computer and information sciences
This work is (partially) funded by the Marie Skłodowska-Curie Actions (645706-GRAGE).
|EU Grant Number:||
(645706) GRAGE - Grey and green in Europe: elderly living in urban areas
Electronic version of an article published as Data Science and Knowledge Engineering for Sensing Decision Support. Proceedings of the 13th International FLINS Conference (FLINS 2018) https://doi.org/10.1142/9789813273238_0078 © World Scientific Publishing Co Pte Ltd.