Abstract
In Industry 5.0, human workers and their wellbeing are placed at the centre of the production process. In this context, task-oriented dialogue systems allow workers to delegate simple tasks to industrial assets while working on other, more complex ones. The possibility of naturally interacting with these systems reduces the cognitive demand to use them and triggers acceptation. Most modern solutions, however, do not allow a natural communication, and modern techniques to obtain such systems require large amounts of data to be trained, which is scarce in these scenarios. To overcome these challenges, this paper presents KIDE4I (Knowledge-drIven Dialogue framEwork for Industry), a semantic-based task-oriented dialogue system framework for industry that allows workers to naturally interact with industrial systems, is easy to adapt to new scenarios and does not require great amounts of data to be constructed. This work also reports the process to adapt KIDE4I to new scenarios. To validate and evaluate KIDE4I, it has been adapted to four use cases that are relevant to industrial scenarios following the described methodology, and two of them have been evaluated through two user studies. The system has been considered as accurate, useful, efficient, not demanding cognitively, flexible and fast. Furthermore, subjects view the system as a tool to improve their productivity and security while carrying out their tasks.
Highlights
Recent technological advances in last decades have caused a great revolution in industrial settings. That terms such as Industry 4.0—and even more recently, Industry 5.0—that define sustainable, advanced and human-centered industrial environments are essential in modern industry
The questionnaire chosen to evaluate the dialogue system was the System Interfaces Questionnaire (SASSI) questionnaire [41], as it provides a comprehensive evaluation on speech-based dialogue systems and it is considered as an standard resource to evaluate such systems
The main characteristics of this system are that (i) it allows a natural communication between workers and industrial assets, reducing the cognitive demand to do so, (ii) it does not need large amounts of training data to be constructed, and (iii) its architecture is generic enough to adapt it to new use cases with a reduced amount of effort
Summary
Recent technological advances in last decades have caused a great revolution in industrial settings. The implementation of more complex and innovative technologies in industrial scenarios, which have reduced the physical workload of workers, have, as a counterpart, an increase of the cognitive load so as to control and manage such technologies [1]. In this sense, workers interact with a wide range of systems at a daily basis, such as intelligent information systems or advanced collaborative robots, and it is key to facilitate this interaction so as to guarantee optimal work conditions. The use of these technologies are conceived so as to not affect the quality of workers’ tasks, as they only require a simple interaction for the target system to function, with minimal impact in their cognitive demand
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