arXiv:2602.17315v3 Announce Type: replace Abstract: We introduce Flickering Multi-Armed Bandits (FMAB) to model sequential decision-making in environments with changing action availability, where accessibility of the next action is restricted to a subset dependent on the agent's current choice. We formalize these constraints through stochastically evolving graphs where actions are limited to local neighborhoods. This mobility-constrained structure imposes a dual challenge: the statistical requirement of information acquisition and the physical overhead of navigation. We analyze FMAB under i.i.
The paper introduces a new framework for multi-armed bandits, which reflects ongoing research into more complex and dynamic AI decision-making environments.
This research addresses fundamental challenges in AI decision-making for environments where physical constraints and dynamic action availability are critical, impacting areas from robotics to logistics.
The formalization of Flickering Multi-Armed Bandits (FMAB) provides a new theoretical model for developing AI solutions in constrained, mobility-dependent scenarios.
- · AI researchers
- · Robotics developers
- · Logistics companies
- · Autonomous system developers
- · Traditional MAB approaches
- · Static decision-making models
Improved theoretical understanding and algorithm development for AI navigation and decision-making under dynamic constraints.
More efficient and adaptable autonomous systems capable of operating in complex, real-world environments with changing conditions.
Enhanced resilience and intelligence in AI-driven infrastructures, potentially reducing operational costs and increasing system robustness across various industries.
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Read at arXiv cs.LG