arXiv:2607.00680v1 Announce Type: new Abstract: We study distributed online submodular maximization under partition matroid constraints, in which multiple agents select a limited number of actions from their own subsets sequentially to maximize the cumulative value of a sequence of objective functions. We develop a unified algorithmic framework that accommodates full-information and bandit feedback models. For both feedback models, we prove that the proposed algorithms achieve sublinear $(1-1/e)$-regret guarantees, which are comparable to those achieved by existing centralized counterparts. Fu
This paper leverages advanced algorithmic research in distributed online submodular maximization, reflecting ongoing efforts to scale AI and machine learning capabilities for complex, real-world distributed systems.
It provides a foundational advancement in enabling robust and efficient distributed AI agents, allowing them to make optimal decisions with limited information across various applications.
This research offers a unified algorithmic framework for distributed online submodular maximization, enhancing the potential for more effective and scalable AI agent deployments.
- · AI/ML researchers
- · Developers of distributed systems
- · Industries using autonomous agents
- · Systems reliant on centralized decision-making
Improved efficiency and performance of distributed AI systems and online decision-making processes.
Accelerated development and adoption of sophisticated AI agents in various sectors, from logistics to resource management.
Potentially, a shift towards more autonomous and self-optimizing distributed infrastructure, reducing human oversight requirements.
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