AI 中文总结
该研究针对分布式智能体互联网路由挑战，提出SPFR算法，整合执行器发现与重新选择到逐跳转发，经模拟验证其性能优于相关方法且鲁棒性良好。
AI 中文摘要
在无集中式路由控制的分布式智能体互联网（IoA）中，由于目的地未预先确定且智能体的本地服务视图有限，将路由任务分配给能力匹配的执行器极具挑战性。相比之下，“先发现再转发”方法在网络转发前选择执行器，因此当下游出现更多候选者时无法直接支持重新选择。我们提出语义势场路由（SPFR），这是一种将执行器发现与重新选择整合到逐跳转发中的分布式IoA路由算法。SPFR将本地语义转发信息库（FIB）中可见的每个执行器表示为任务条件语义势源，其中效用设定其强度，跳数距离则引发指数衰减。在每一跳，转发智能体重新计算这些势，重新选择主导执行器，并将任务向其转发一跳。在任务一致的冻结FIB条件下，我们证明了无环路和有限跳终止性，并推导了相对于全可见性目标在有限可见性下的显式加性误差界。在真实拓扑上进行的大量模拟显示，SPFR在使用更少转发跳数和显著更少请求触发消息的同时，达到了分布式效用贪婪路由和请求触发全局发现的实现效用，并且在网络和服务动态下保持鲁棒性。
英文摘要
In a distributed Internet of Agents (IoA) without centralized routing control, routing tasks to capability-matched executors is challenging because destinations are not predetermined and agents have bounded local service views. Discover-then-forward approaches, by contrast, select an executor before network forwarding and therefore do not directly support reselection when additional candidates become visible downstream. We introduce Semantic Potential Field Routing (SPFR), a distributed IoA routing algorithm that integrates executor discovery and reselection into hop-by-hop forwarding. SPFR represents each executor visible in a local semantic forwarding information base (FIB) as a task-conditioned semantic potential source, with utility setting its strength and hop distance inducing exponential attenuation. At each hop, the forwarding agent recomputes these potentials, reselects the dominant executor, and forwards the task one hop toward it. Under task-consistent frozen-FIB conditions, we prove loop freedom and finite-hop termination and derive an explicit additive error bound under bounded visibility relative to the full-visibility objective. Extensive simulations on real-world topologies show that SPFR approaches the realized utility of distributed utility-greedy routing and request-triggered global discovery while using fewer forwarding hops and substantially fewer request-triggered messages, and remains robust under network and service dynamics.
