@misc{10481/64763,
year = {2020},
month = {9},
url = {http://hdl.handle.net/10481/64763},
abstract = {Conversational systems have become an element of everyday life for billions of users who use
speech-based interfaces to services, engage with personal digital assistants on smartphones, social
media chatbots, or smart speakers. One of the most complex tasks in the development of these
systems is to design the dialogue model, the logic that provided a user input selects the next answer.
The dialogue model must also consider mechanisms to adapt the response of the system and the
interaction style according to different groups and user profiles. Rule-based systems are difficult
to adapt to phenomena that were not taken into consideration at design-time. However, many of
the systems that are commercially available are based on rules, and so are the most widespread
tools for the development of chatbots and speech interfaces. In this paper, we present a proposal
to: i) automatically generate the dialogue rules from a dialogue corpus through the use of evolving
algorithms, ii) adapt the rules according to the detected user intention. We have evaluated our
proposal with several conversational systems of different application domains, from which our
approach provided an efficient way for adapting a set of dialogue rules considering user utterance
clusters.},
organization = {The research leading to these results has received funding from the European Union’s Horizon 2020 research and
innovation programme under grant agreement No 823907 (MENHIR project: https://menhir-project.eu), and the Spanish
project TEC2017-88048-C2-2-R.},
publisher = {Wiley},
title = {Adaptive dialogue management using intent clustering and fuzzy rules},
doi = {10.1111/exsy.12630},
author = {Griol Barres, David and Callejas Carrión, Zoraida and Molina, José Manuel and Sanchis, Araceli},
}