Special issue of IEEE Intelligent Systems – read the full instructions here: https://www.computer.org/digital-library/magazines/is/cfp-metacognitive-prediction-ai-behavior
Submissions Due: 1 November 2025
Important Dates
Title and Abstracts Due: Oct. 1, 2025 (to is3-26@computer.org)
Full Manuscripts Due: Nov. 1, 2025 (via submission site)
Publication: May/Jun 2026
TOPIC SUMMARY:
As artificial intelligence (AI) becomes more prevalent in an increasing number of practical applications and systems, improved characterization of such systems will in turn become important to ensure system resiliency, safety, and reliability in the environments for which they are deployed, which often produces data that differs from data used in training. However, while Intelligent systems, often using supervised machine learning or reinforcement learning, have provided excellent results for a variety of applications, the reasons behind their failure modes – or anomalous behavior they engage in – are generally not well understood. The idea of metacognition, reasoning about an Intelligent system itself, is a key avenue to understanding the behavior and performance of machine learning systems. Recently, a variety of methodologies have been explored in the literature, which include stress testing of robotic systems, model introspection, model certification, and performance prediction. Moreover, researchers across multiple disciplines including Computer Science, Control Theory, Mechanical Engineering, Human Factors, and Business Schools have explored these problems from different angles.
This special issue seeks to collect cutting edge research associated with the 2nd Workshop on Metacognitive Prediction of AI Behavior (METACOG-25); the best papers presented at the workshop will be invited to submit an extended version to the special issue, and other relevant manuscripts will also be accepted. You can submit to this special issue even if you did not have attended/submit to METACOG-25. The main objectives of the special issue are aligned with those of the workshop:
Survey the main approaches to metacognition in intelligent systems.
Understand the requirements that metacognitive approaches have for successful deployment.
Identify novel methods for metacognition that drive improved AI performance in an operational or cross-domain setting.
Identify application areas suitable for the deployment of metacognitive methods.
Understand the relationship between approaches to AI metacognition and the behavior of human operators.
Specific topics to be covered include, but are not limited to:
Explainable performance prediction of black-box AI systems
Stress testing of reinforcement learning systems
Use of metacognition to increase trust in intelligent systems by their operators
Applications of AI metacognition to vision and robotic systems
New methods leveraging neuro-symbolic AI architectures for metacognition
Techniques for AI systems to self-adapt (self-heal, self-repair) in new domains
Hyperdimensional Computing (HDC) and Vector Symbolic Architectures (VSA) and their relationship to metacognition