Requirements for Recognition and Rapid Response to Unfamiliar Events Outside of Agent Design Scope
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Abstract (arXiv)
Regardless of past learning, an agent in an open world will face unfamiliar events outside of prior experience, existing models, or policies. Further, the agent will sometimes lack relevant knowledge and/or sufficient time to assess the situation and evaluate response options. How can an agent respond reasonably to situations that are outside of its original design scope? How can it recognize such situations sufficiently quickly and reliably to determine reasonable, adaptive courses of action? We identify key characteristics needed for solutions, review the state-of-the-art, and outline a proposed, novel approach that combines domain-general meta-knowledge (inspired by human cognition) and metareasoning. This approach offers potential for fast, adaptive responses to unfamiliar situations, more fully meeting the performance characteristics required for open-world, general agents.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 44 references that are in the map. First posted on arXiv 16 Apr 2025.
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7 of 44 references.
References · 7
- 2020Towards Continual Reinforcement Learning: A Review and Perspectivescited by 274
- 2020Self-Supervised Policy Adaptation during Deploymentcited by 123
- 2022You Only Live Once: Single-Life Reinforcement Learningcited by 24
- 2023Prediction and Control in Continual Reinforcement Learningcited by 19
- 2023Adapt On-the-Go: Behavior Modulation for Single-Life Robot Deploymentcited by 8
- 2024NovelGym: A Flexible Ecosystem for Hybrid Planning and Learning Agents Designed for Open Worldscited by 4
- 2023A Domain-Independent Agent Architecture for Adaptive Operation in Evolving Open Worldscited by 3
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