Xiaolian Guo

Jun Liu, Ke Yu, Keliang Chen et al.

With the rapid advancement of artificial intelligence, the proliferation of autonomous agents has introduced new challenges in interoperability, scalability, and coordination. The Internet of Agents (IoA) aims to interconnect heterogeneous agents through standardized communication protocols, enabling seamless collaboration and intelligent task execution. However, existing agent communication protocols such as MCP, A2A, and ANP remain fragmented and scenario-specific. To address this gap, we propose Agent Collaboration Protocols (ACPs), a comprehensive protocol suite for the IoA. ACPs include registration, discovery, interaction, and tooling protocols to support trustable access, capability orchestration, and workflow construction. We present the architecture, key technologies, and application workflows of ACPs, and demonstrate its effectiveness in a collaborative restaurant booking scenario. ACPs lay the foundation for building a secure, open, and scalable agent internet infrastructure.

Zhiwei Lin, Yi Li, Xiaolian Guo

A ranking is an ordered sequence of items, in which an item with higher ranking score is more preferred than the items with lower ranking scores. In many information systems, rankings are widely used to represent the preferences over a set of items or candidates. The consensus measure of rankings is the problem of how to evaluate the degree to which the rankings agree. The consensus measure can be used to evaluate rankings in many information systems, as quite often there is not ground truth available for evaluation. This paper introduces a novel approach for consensus measure of rankings by using graph representation, in which the vertices or nodes are the items and the edges are the relationship of items in the rankings. Such representation leads to various algorithms for consensus measure in terms of different aspects of rankings, including the number of common patterns, the number of common patterns with fixed length and the length of the longest common patterns. The proposed measure can be adopted for various types of rankings, such as full rankings, partial rankings and rankings with ties. This paper demonstrates how the proposed approaches can be used to evaluate the quality of rank aggregation and the quality of top-$k$ rankings from Google and Bing search engines.