PRISM: Event-Driven Multi-Agent Resource Collection with Adaptive Preferences

Multi-agent resource collection, exemplified by the Multi-agent Traveling Officer Problem (MTOP), requires mobile agents with limited capacity to capture spatially distributed resources whose availability evolves over time. Existing methods optimize only collected volume and rely on uniform-step formulations, which introduce uninformative no-op transitions and ignore the coupling among agents dispatched at the same time. Real-world dispatch, however, must also balance timeliness and fairness. We propose PRISM, which formulates MTOP as an event-driven asynchronous multi-agent semi-Markov decision process in which decisions are triggered only by agent availability. A state-aware context encoder models interactions among agents, resources, and dispatch history; a semi-autoregressive decoder generates joint assignments efficiently while preserving inter-assignment dependencies; and an adaptive preference head infers agent-specific weights over Resource Volume, Timeliness, and Fairness, letting agents specialize to their local opportunities and team needs. On four public datasets spanning crime, emergency medical services, and traffic, PRISM outperforms the strongest multi-objective baselines by 16.98%, 6.19%, and 4.81% on Volume, Timeliness, and Fairness, respectively. Code: https://anonymous.4open.science/r/PRISM-C5BA.