Learning to Assign Prediction Tasks to Agents with Capacity Constraints

• URL: http://arxiv.org/abs/2605.27999v1
• Date: Wed, 27 May 2026 05:45:47 GMT
• Title: Learning to Assign Prediction Tasks to Agents with Capacity Constraints
• Abstract summary: We address the problem of learning to assign prediction tasks to one agent from a set of available human or AI agents.<n>We provide a general theoretical characterization of this problem in terms of agent capacities, differences in agent expertise, and task context.<n>We develop a framework of sequential explore-exploit policy-learning algorithms that seek to maximize overall performance.
• Score: 12.929811348173049
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We address the problem of learning to assign prediction tasks to one agent from a set of available human or AI agents. In particular, we focus on the sequential learning of agent expertise and assignment policies where each agent is constrained to handle a fraction of tasks. We provide a general theoretical characterization of this problem in terms of agent capacities, differences in agent expertise, and task context. We then develop a framework of sequential explore-exploit policy-learning algorithms that seek to maximize overall performance. Experimental results over a variety of tabular, image, and text prediction tasks demonstrate systematic gains from our policy-learning algorithms relative to non-contextual baselines across different types of agents, including LLMs and humans.

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