Learning to Assign Prediction Tasks to Agents with Capacity Constraints

2026-05-27

AI Summary

This study addresses the problem of dynamically allocating prediction tasks among capacity-constrained agents—whether human or artificial—to maximize collective performance. It introduces, for the first time, a theoretical formulation of task assignment under explicit capacity constraints and proposes a context-aware sequential exploration–exploitation learning framework. This framework integrates multi-agent capability modeling with optimized task–agent matching strategies. Empirical evaluations demonstrate that the proposed approach significantly outperforms non-contextual baselines across tabular, image, and text prediction tasks, and is effective in collaborative settings involving both large language models and human agents.

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.

Problem

• task assignment
• capacity constraints
• prediction tasks
• agent expertise
• sequential learning

Innovation

• task assignment
• capacity constraints
• sequential policy learning
• explore-exploit
• agent expertise