Robotic Process Automation and Artificial Intelligence in Industry 4.0 – A Literature review
Open accessAbstract
Taking into account the technological evolution of the last decades and the proliferation of information systems in society, today we see the vast majority of services provided by companies and institutions as digital services. Industry 4.0 is the fourth industrial revolution where technologies and automation are asserting themselves as major changes. Robotic Process Automation (RPA) has numerous advantages in terms of automating organizational and business processes. Allied to these advantages, the complementary use of Artificial Intelligence (AI) algorithms and techniques allows to improve the accuracy and execution of RPA processes in the extraction of information, in the recognition, classification, forecasting and optimization of processes. In this context, this paper aims to present a study of the RPA tools associated with AI that can contribute to the improvement of the organizational processes associated with Industry 4.0. It appears that the RPA tools enhance their functionality with the objectives of AI being extended with the use of Artificial Neural Network algorithms, Text Mining techniques and Natural Language Processing techniques for the extraction of information and consequent process of optimization and of forecasting scenarios in improving the operational and business processes of organizations.
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A third way to measure trust calibration was to measure over-, under-, and appropriate trust as behavioral deviation (reliance) from ideal behavior.
“A third way to measure the trust calibration was to measure over-, under-, and appropriate trust as the behavioral deviation (reliance) from the ideal behavior [27,50,55,104,110,132,137].”
From Measuring and Understanding Trust Calibrations for Automated Systems: A Survey of the State-Of-The-Art and Future Directions (2023) · cites this paper as [110]
Ribeiro et al. found displaying class probabilities did not help users appropriately assess a system's trustworthiness.
“Ribeiro et al. [110] found that displaying class probabilities did not help users to appropriately assess a system's trustworthiness (see also [6]).”
From Measuring and Understanding Trust Calibrations for Automated Systems: A Survey of the State-Of-The-Art and Future Directions (2023) · cites this paper as [110]
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- Authors
- Ribeiro, J., Lima, R., Eckhardt, T., & Paiva, S.
- Affiliations
- Universidade Politécnica de Viana do Castelo (PT)
- Corresponding author
- Ribeiro, Jorge
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2021
- Volume
- 181
- Pages
- 51-58
- ISSN
- 1877-0509 (ISSN-L)
- OpenAlex
- W3132765992
- Cited by
- 489
- References
- 2
- Citations by year
- 2026: 46 · 2025: 117 · 2024: 122 · 2023: 147 · 2022: 41 · 2021: 16
- FWCI
- 111.97
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Procedia Computer Science (publishedVersion, cc-by-nc-nd)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Robotic Process Automation Applications
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- robotic process automation, artificial intelligence, Industry 4.0, business process automation, artificial neural networks, text mining, natural language processing, information extraction, process optimization, forecasting, digital services
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