The AAAI-20 Workshop on Intelligent Process Automation

Hilton New York Midtown, New York, NY, USAFebruary 7th or 8th 2020
TL;DR: A one-day AAAI-20 workshop focusing on learning structured & executable processes (programs) from human demonstrations, natural language specifications, or interactions with an environment. Submissions of previously unpublished or recently published papers welcome. $1000 Best Paper Award. Free lunch. Interdisciplinary discussion. Non-archival proceedings.

How to free people from the mundane and repetitive parts of their daily workload? Robotic Process Automation (RPA) addresses this problem by developing software agents (robots) that can mimic human users to perform a variety of business tasks on their computers (e.g., processing the invoices received from emails). Since the term RPA was coined by Blue Prism in 2012, it has spread through enterprises in many different industrial sections (finance, health, telecommunication, manufacturing, and so on). It is estimated that the RPA software market will reach $3.97 billion by 2025.

Current RPA systems are mostly rule-based. Artificial Intelligence (AI) promises to take RPA to new heights, but so far the AI research efforts related to the different aspects of RPA have been largely isolated. This AAAI-20 workshop aims to bridge the gap between the rapidly growing RPA software industry and the AI research community. It will bring together researchers from different fields to exchange ideas and foster discussions on this important, novel application domain for AI.

Workshop Topics

Technical topics include, but are not limited to:

  • demo2process (learning a task-completion software agent from human demonstrations or behavior logs): interactive task learning, imitation learning, program induction, programming by example, process mining, ...
  • text2process (learning a task-completion software agent from step-by-step natural language text descriptions of the process): learning by instruction, conversational machine learning, natural language programming, natural language grounding, ...
  • task2process (learning a task-completion software agent directly from the task as defined by an environment with its reward function or some input/output examples): reinforcement learning, neural program synthesis, Bayesian program learning, ...

The common theme is that the learning system’s output would not be simply class labels or numerical predictions, but structured & executable processes (in the form of state-action policies, if-this-then-that rules, finite-state automatons, or programs in domain-specific languages), which makes it more challenging than most of today’s machine learning research problems. In addition, such automated processes must be safe, robust and explainable to be ready for enterprise-level applications, which also poses new research challenges.

Furthermore, this workshop strongly encourages interdisciplinary submissions from psychologists, economists, or social scientists on the following topics.

  • human-in-the-loop: the interaction between human users and software robots in attended automation.
  • human-outside-the-loop: the social and organizational impacts of software robots taking over some workloads or responsibilities from human users.

Paper Submission

We welcome contributions of two types.

  • New, previously unpublished papers which could be either long (6-8 pages) or short (2-4 pages). Such submissions will be given priority.
  • Preprints (the accepted version, not the final published version) of recently published or in-press papers (no page limit). Such submissions must indicate clearly when and where the corresponding papers have been or will be published.

All submissions should be formatted in the AAAI-20 style and will be peer-reviewed by multiple reviewers. The authors' identities must be concealed to enable double-blind peer-review, and all the conflicts of interests must be declared in advance. The accepted papers will be published on arXiv.org and they will be included in a non-archival workshop proceedings (which does not prevent the authors from submitting them to other venues in the future). Blue Prism will sponsor a Best Paper Award of $1000. It will be selected from the accepted papers by a committee consisting of both academic and industrial experts.


Important Dates

[AoE time (UTC-12)]

  • November 8th 2019 EXTENDED to November 15th 2019: Abstract submission due
  • November 15th 2019: Full paper submission due (HARD DEADLINE, no extension)
  • December 4th 2019: Notification of acceptance/rejection
  • December 13th 2019: AAAI-20 early registration deadline
  • January 7th 2020: Camera-ready version due
  • January 10th 2020: AAAI-20 late registration deadline
  • February 7th or 8th 2020: Workshop at AAAI-20

Workshop Format (Provisional)

One-Day (February 7th or 8th 2020. 9am - 5pm)

Invited Speakers

Programme Committee

  • Amos Azaria (Ariel University, Israel)
  • Dan Calian (Google Deepmind, UK)
  • Sarah Chasins (UC Berkeley, USA)
  • Xinyun Chen (UC Berkeley, USA)
  • Taolue Chen (Birkbeck, University of London, UK)
  • Chiara Di-Francescomarino (Fondazione Bruno Kessler, Italy)
  • César Ferri (Universitat Politènica de València, Spain)
  • Andre Freitas (University of Manchester, UK)
  • Krishna Dubba (Blue Prism AI Labs, UK)
  • Po-Sen Huang (Google Deepmind, UK)
  • Thanapong Intharah (Khon Kaen University, Thailand)
  • James Kirk (University of Michigan, USA)
  • Toby Jia-Jun Li (Carnegie Mellon University, USA)
  • Xuelong Li (Chinese Academy of Sciences, China)
  • Chen Liang (Google Brain, USA)
  • Xixi Lu (Utrecht University, Netherlands)
  • Fabrizio Maggi (University of Tartu, Estonia)
  • Andrea Marrella (Sapienza Università di Roma, Italy)
  • Marco Montali (Free University of Bozen-Bolzano, Italy)
  • Luigi Pontieri (ICAR, CNR, Italy)
  • John Reid (Blue Prism AI Labs, UK)
  • Julian Richard (Blue Prism AI Labs, UK)
  • Arik Senderovich (University of Toronto, Canada)
  • Murat Sensoy (Blue Prism AI Labs, UK)
  • Cyrus Shaoul (Leela AI, USA)
  • Richard Shin (UC Berkeley, USA)
  • Biplav Srivastava (IBM Research, USA)
  • Heiner Stuckenschmidt (University of Mannheim, Germany)
  • Emilio Sulis (University of Torino, Italy)
  • Sebastiaan van Zelst (Fraunhofer FIT & RWTH Aachen University, Germany)
  • Chenglong Wang (University of Washington, USA)
  • Jun Wang (University College London, UK)
  • Matthias Weidlich (Humboldt-Universität zu Berlin, Germany)
  • Leslie Willcocks (London School of Economics and Political Science, UK)

Best Paper Committee

  • Jacques Cali (Blue Prism AI Labs, UK)
  • Joyce Chai (University of Michigan, USA)
  • Sumit Gulwani (Microsoft Research, USA)
  • Xuelong Li (Chinese Academy of Sciences, China)
  • Dawn Song (UC Berkeley, USA)
  • Dacheng Tao (University of Sydney, Australia)
  • Jun Wang (University College London, UK)
  • Emine Yilmaz (University College London, UK)

Organization Committee