Skip to main content

AIRPA: An Architecture to Support the Execution and Maintenance of AI-Powered RPA Robots

  • Conference paper
  • First Online:
Business Process Management: Blockchain and Robotic Process Automation Forum (BPM 2021)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 428))

Included in the following conference series:

Abstract

Robotic Process Automation (RPA) has quickly evolved from automating simple rule-based tasks. Nowadays, RPA is required to mimic more sophisticated human tasks, thus implying its combination with Artificial Intelligence (AI) technology, i.e., the so-called intelligent RPA. Putting together RPA with AI leads to a challenging scenario since (1) it involves professionals from both fields who typically have different skills and backgrounds, and (2) AI models tend to degrade over time which affects the performance of the overall solution. This paper describes the AIRPA project, which addresses these challenges by proposing a software architecture that enables (1) the abstraction of the robot development from the AI development and (2) the monitor, control, and maintain intelligent RPA developments to ensure its quality and performance over time. The project has been conducted in the Servinform context, a Spanish consultancy firm, and the proposed prototype has been validated with reality settings. The initial experiences yield promising results in reducing AHT (Average Handle Time) in processes where AIRPA deployed cognitive robots, which encourages exploring the support of intelligent RPA development.

This research has been supported by the NICO project (PID2019-105455GB-C31) of the Spanish Ministry of Science, Innovation and Universities and the AIRPA project (EXP00118029/IDI-20190524, P011-19/E09) of the Center for the Development of Industrial Technology (CDTI).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://www.edureka.co/blog/rpa-developer-roles-and-responsibilities/.

  2. 2.

    https://www.servinform.es/.

  3. 3.

    https://www.iwt2.org/.

  4. 4.

    As the project is under development, the realization of their goals are in progress.

  5. 5.

    https://dlabs.ai/blog/rpa-2-0-how-to-achieve-the-highest-level-of-automation/.

  6. 6.

    https://cloud.google.com/vertex-ai.

  7. 7.

    https://aws.amazon.com/sagemaker/.

  8. 8.

    https://azure.microsoft.com/en-gb/services/machine-learning/.

  9. 9.

    https://www.blueprism.com/.

  10. 10.

    https://www.uipath.com/.

References

  1. Chakraborti, T., Isahagian, V., Khalaf, R., Khazaeni, Y., Muthusamy, V., Rizk, Y., Unuvar, M.: From robotic process automation to intelligent process automation: emerging trends. In: Internation Conference on Business Process Management 2020 RPA Forum. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58779-6

  2. Jha, N., Prashar, D., Nagpal, A.: Combining artificial intelligence with robotic process automation—an intelligent automation approach. In: Ahmed, K.R., Hassanien, A.E. (eds.) Deep Learning and Big Data for Intelligent Transportation. SCI, vol. 945, pp. 245–264. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-65661-4_12

    Chapter  Google Scholar 

  3. Jimenez-Ramirez, A., Reijers, H.A., Barba, I., Del Valle, C.: A method to improve the early stages of the robotic process automation lifecycle. In: Giorgini, P., Weber, B. (eds.) CAiSE 2019. LNCS, vol. 11483, pp. 446–461. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-21290-2_28

    Chapter  Google Scholar 

  4. Hollebeek, L.D., Sprott, D.E., Brady,M.K.: Rise of the machines? Customer engagement in automated service interactions. J. Serv. Res. 24(1), 3–8 (2021). https://doi.org/10.1177/1094670520975110, https://www.sciencedirect.com/science/article/pii/0166361596000139

  5. Le Clair, C., O’Donnell, G., Lipson, A., Lynch, D.: The Forrester Wave™: Robotic Process Automation, Q4 2019. The Forrester Wave (2019)

    Google Scholar 

  6. Leevy, J.L., Khoshgoftaar, T.M., Bauder, R.A., Seliya, N.: The effect of time on the maintenance of a predictive model. In: 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), pp. 1891–1896. IEEE (2019)

    Google Scholar 

  7. Leno, V., Polyvyanyy, A., Dumas, M., La Rosa, M., Maggi, F.M.: Robotic process mining vision and challenges. Bus. Inf. Syst. Eng. 63,301–314 (2020)

    Google Scholar 

  8. Mahala, G., Sindhgatta, R., Dam, H.K., Ghose, A.: Designing optimal robotic process automation architectures. In: Kafeza, E., Benatallah, B., Martinelli, F., Hacid, H., Bouguettaya, A., Motahari, H. (eds.) ICSOC 2020. LNCS, vol. 12571, pp. 448–456. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-65310-1_32

    Chapter  Google Scholar 

  9. Martínez-Rojas, A., Barba, I., Enríquez, J.G.: Towards a taxonomy of cognitive RPA components. In: Asatiani, A., García, J.M., Helander, N., Jiménez-Ramírez, A., Koschmider, A., Mendling, J., Meroni, G., Reijers, H.A. (eds.) BPM 2020. LNBIP, vol. 393, pp. 161–175. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58779-6_11

    Chapter  Google Scholar 

  10. Martins, P., Sá, F., Morgado, F., Cunha, C.: Using machine learning for cognitive Robotic Process Automation (RPA). In: Iberian Conference on Information Systems and Technologies (CISTI) (2020)

    Google Scholar 

  11. Singh, M.K., Raghavendra, D., Pandian, D., Sadana, A.: Surface automation - interacting with applications using Black box approach. In: International Conference for Convergence in Technology (I2CT) (2021)

    Google Scholar 

  12. Sonnenburg, S., et al.: The need for open source software in machine learning. J. Mach. Learn. Res. 8,2443–2466 (2007)

    Google Scholar 

  13. Willcocks, L., Lacity, M., Craig, A.: Robotic process automation: strategic transformation lever for global business services? J. Inf. Technol. Teach. Cases 7(1), 17–28 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to A. Martínez-Rojas .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Martínez-Rojas, A., Sánchez-Oliva, J., López-Carnicer, J.M., Jiménez-Ramírez, A. (2021). AIRPA: An Architecture to Support the Execution and Maintenance of AI-Powered RPA Robots. In: González Enríquez, J., Debois, S., Fettke, P., Plebani, P., van de Weerd, I., Weber, I. (eds) Business Process Management: Blockchain and Robotic Process Automation Forum. BPM 2021. Lecture Notes in Business Information Processing, vol 428. Springer, Cham. https://doi.org/10.1007/978-3-030-85867-4_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-85867-4_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85866-7

  • Online ISBN: 978-3-030-85867-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics