arXiv:2605.27999v1 Announce Type: cross 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.
The proliferation of AI systems and the increasing complexity of tasks necessitate advanced methods for allocating work effectively between human and AI agents, particularly with capacity constraints.
This research provides a theoretical and algorithmic framework for optimizing human-AI collaboration under real-world constraints, which is crucial for maximizing efficiency and performance in hybrid workforces.
The ability to dynamically learn and assign tasks based on agent expertise and capacity will significantly improve the operational efficiency and scalability of systems integrating human and AI agents.
- · AI-powered service industries
- · Organizations deploying AI agents
- · Task management platform providers
- · Inefficient human-AI task allocation models
- · Systems lacking adaptive learning capabilities
Improved performance and resource utilization in systems where tasks are assigned to a mix of human and AI agents.
Accelerated adoption of AI agents in complex workflows as allocation becomes more intelligent and efficient.
New competitive advantages for organizations that master hybrid human-AI workforce orchestration, leading to increased productivity and cost savings.
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Read at arXiv cs.AI