From Task Allocation to Risk Clearing: A Unifying Interface for Mixed Human-Agent Societies
This work addresses the challenge of scalable, transparent coordination in safety-critical environments with heterogeneous agents, but remains at the conceptual/agenda stage without empirical validation.
The paper proposes Risk-Aware Option Clearing (ROC), a coordination mechanism for mixed human-agent societies that uses risk summaries of agent options to optimize task allocation under safety constraints, aiming to overcome limitations of static task allocation and opaque joint policies.
As humans, robots, and software agents increasingly share safety-critical environments, coordination must move from static task allocation to managing uncertain commitments. Existing frameworks fall short: they either assume rigid, static teams or learn opaque joint policies that are hard to adapt and difficult to integrate with human decision-makers. To overcome these limitations, we propose Risk-Aware Option Clearing (ROC), a unifying coordination mechanism in which agents expose options (temporally extended skills) paired with risk summaries that predict outcome distributions. A central clearinghouse then assigns tasks by optimizing risk-adjusted mission utility under deadlines and safety constraints. ROC is a family of mechanisms, ranging from deployments where the clearinghouse learns outcome models from data to ones that consume full distributional predictions from agents. By treating risk-aware options as the basic coordination unit, ROC sketches a scalable, transparent infrastructure for integrating heterogeneous agents into future mixed human--agent societies and outlines a research agenda for such risk-aware clearing layers.