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On the Evaluation of Intelligent Process Automation

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arxiv 2001.02639 v2 pith:BWTVY5MQ submitted 2020-01-08 cs.HC cs.SE

Deborah Ferreira , Julia Rozanova , Krishna Dubba , Dell Zhang , Andre Freitas This is my paper · ORCID
classification cs.HCcs.SE
keywords automationsystemsevaluationintelligentprocesssupporttasksacross
verification ladder T0 review T1 audit T2 compute T3 formal
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Intelligent Process Automation (IPA) is emerging as a sub-field of AI to support the automation of long-tail processes which requires the coordination of tasks across different systems. So far, the field of IPA has been largely driven by systems and use cases, lacking a more formal definition of the task and its assessment. This paper aims to address this gap by providing a formalisation of IPA and by proposing specific metrics to support the empirical evaluation of IPA systems. This work also compares and contrasts IPA against related tasks such as end-user programming and program synthesis.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. LMV-RPA: Large Model Voting-based Robotic Process Automation

    cs.RO 2024-12 reject novelty 3.0 of 10

    LMV-RPA combines four OCR engines, two LLMs, and majority voting to structure invoices as JSON, claiming 99 percent accuracy and 80 percent faster processing that the paper's own tables do not support.

  2. LMRPA: Large Language Model-Driven Efficient Robotic Process Automation for OCR

    cs.RO 2024-12 reject novelty 2.0 of 10

    An OCR pipeline enhanced with an LLM is benchmarked as faster than UiPath and Automation Anywhere, but the benchmark's methodology and reported numbers contain inconsistencies.

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