Designing Artificial Intelligence for Open Worlds

March 21-23, 2022

Sponsored by the Association for the Advancement of Artificial Intelligence

Open-world learning has taken on new importance in recent years as AI systems continue to be applied and transitioned to real-world settings where unexpected events (‘novelties’) can, and do, occur. An open-learning framework has been defined as one that can ‘deal with both normal in-distribution inputs and undesired out-of-distribution (OOD) inputs.’ Designing AI that can operate in open worlds, including detecting, characterizing and adapting to novelty, is a critical goal on the path to building intelligent systems that can work alongside humans to solve complex problems while being reliable enough to handle the unexpected. We invite contributions that describe novel technical approaches for open-world learning and novelty, theoretical frameworks for understanding open-world learning and novelty (including theories of novelty), empirical studies, implementations (including simulators and experimental testbeds), and lessons learned from current implementations.

Key Dates

Keynote Speakers and Panelists

Coming Soon


Both days will comprise a mix of invited talks, paper presentations, a panel on open-world learning and novelty, and discussion groups (on the second day). Hybrid attendance will be supported.

Call for Papers

All submissions should be made via the AAAI SSS-22 EasyChair website. Please be sure to select 'Designing Artificial Intelligence for Open Worlds' as the relevant track when making your submission. We accept the following types of submissions in AAAI format:

•Full papers (up to 8 pages + unlimited pages for references)
•Short and experience papers (up to 4 pages + unlimited pages for references)

Topics (Non-Exhaustive)

• Open-world learning
• Unexpected situations and novelty
• Open-world simulations and gameplaying
• Online reinforcement learning
• Out-of-distribution inputs
• Robustness and anti-fragility in AI
• Architectures for novelty detection, characterization and adaptation
• Experimental methods and frameworks for evaluating OOD
• Theories of open-world learning and novelty
• Datasets and methodologies for open-world learning and novelty
• Experimental results using existing methods, including negative results of interest
• Systems-level issues, including engineering best pracitces
• Applications

Vision, Opinion and Position Papers

We will also accept a small number of vision, opinion and position papers that provide discussions on challenges and roadmaps for designing AI for open worlds.

Papers that do not meet the formatting requirements will be rejected without review. The accepted papers will be published online and will not be considered archival. Select papers may be invited to a special journal issue on open world learning.

Please email any enquiries to

Accepted Papers

Coming Soon

Program and Schedule

Coming Soon

Organizing Committee

  • Mayank Kejriwal, University of Southern California (Co-Chair)
  • Abhinav Shrivastava, University of Maryland (Co-Chair)
  • Eric Kildebeck, University of Texas at Dallas (Co-Chair)
  • Bharat Bhargava, Purdue University
  • Carl Vondrick, Columbia University