Abstract
In recent years, Robotic Process Automation (RPA) has been widely adopted across the industry as an important enabler for business process automation and digital transformation. Recent advancements suggest that next generation RPA will require advanced human-robot collaboration capabilities for providing a more natural conversational interface and supporting more complex automation orchestration needs. Our work focuses on the nascent field of conversational RPA bots that are able to dynamically orchestrate automation tasks through natural language. In this context, recommending possible utterances and next steps to the user is an important capability to enhance human-bot collaboration. We take an exploratory approach to the problem of next-best-skill recommendation in human-robot collaboration. We highlight key characteristics of this problem, examine existing approaches, and call out specific challenges in implementing a solution. We suggest that this problem calls for an integrated strategy for recommendation, and illustrate a possible implementation architecture that can integrate multiple recommendation strategies.
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Notes
- 1.
An utterance that serves a function in the dialog (e.g., questions and request).
- 2.
Sidekick - common RPA slang for a close companion or personal assistant.
- 3.
A job requisition is document describing the required skills for a job.
- 4.
The amount of space available on a display for an application to provide output.
- 5.
In some domains, this problem is also known as concept drift.
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Acknowledgements
We would like to thank Sebastian Carbajalo, Yara Rizk, Vatche Isahagian, Vinod Muthusamy, Mahmoud Mahmoud, Scott Boag, and Ben Herta for invaluable inputs and feedback.
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Yaeli, A., Shlomov, S., Oved, A., Zeltyn, S., Mashkif, N. (2022). Recommending Next Best Skill in Conversational Robotic Process Automation. In: Marrella, A., et al. Business Process Management: Blockchain, Robotic Process Automation, and Central and Eastern Europe Forum. BPM 2022. Lecture Notes in Business Information Processing, vol 459. Springer, Cham. https://doi.org/10.1007/978-3-031-16168-1_14
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