Recent research has shown that surprisingly rich models of human activity can be learned from GPS (positional) data. However, most effort to date has concentrated on modeling single individuals or statistical properties of groups of people. Moreover, prior work focused solely on modeling actual successful executions (and not failed or attempted executions) of the activities of interest. We, in contrast, take on the task of understanding human interactions, attempted interactions, and intentions from noisy sensor data in a fully relational multi-agent setting. We use a real-world game of capture the flag to illustrate our approach in a well-defined domain that involves many distinct cooperative and competitive joint activities. We model the domain using Markov logic, a statistical-relational language, and learn a theory that jointly denoises the data and infers occurrences of high-level activities, such as a player capturing an enemy. Our unified model combines constraints imposed by the geometry of the game area, the motion model of the players, and by the rules and dynamics of the game in a probabilistically and logically sound fashion. We show that while it may be impossible to directly detect a multi-agent activity due to sensor noise or malfunction, the occurrence of the activity can still be inferred by considering both its impact on the future behaviors of the people involved as well as the events that could have preceded it. Further, we show that given a model of successfully performed multi-agent activities, along with a set of examples of failed attempts at the same activities, our system automatically learns an augmented model that is capable of recognizing success and failure, as well as goals of peoples actions with high accuracy. We compare our approach with other alternatives and show that our unified model, which takes into account not only relationships among individual players, but also relationships among activities over the entire length of a game, although more computationally costly, is significantly more accurate. Finally, we demonstrate that explicitly modeling unsuccessful attempts boosts performance on other important recognition tasks.
SocialGrid: A Benchmark for Planning and Social Reasoning in Embodied Multi-Agent Systems
Technical University of Darmstadt（达姆施塔特技术大学）；German Research Center for Artificial Intelligence（德国人工智能研究中心）；Lab1141（Lab1141实验室）；Centre for Cognitive Science, Darmstadt（达姆施塔特认知科学中心）
Neural Amortized Inference for Nested Multi-agent Reasoning
Dartmouth College（达特茅斯学院）；Google Research（谷歌研究院）；New York University（纽约大学）；University of Virginia（弗吉尼亚大学）；Massachusetts Institute of Technology（麻省理工学院）；Johns Hopkins University（约翰斯·霍普金斯大学）
Tree-of-Reasoning: Towards Complex Medical Diagnosis via Multi-Agent Reasoning with Evidence Tree
South China University of Technology（华南理工大学）；Key Laboratory of Big Data and Intelligent Robot (SCUT), Ministry of Education（大数据与智能机器人重点实验室（SCUT））；Hong Kong Polytechnic University（香港理工大学）
Adaptive Reasoning Executor: A Collaborative Agent System for Efficient Reasoning
Artificial Intelligence Innovation and Incubation Institute, Fudan University（复旦大学人工智能创新与孵化院）；School of Data Science, Fudan University（复旦大学数据科学学院）；Shanghai Academy of Artificial Intelligence for Science（上海人工智能科学研究院）
AgenticRag-R1: Agentic Reinforcement Learning with Stack Memory for Multi-Step Reasoning, Retrieval and Memorizing
National Engineering Research Center of Software Engineering, Peking University（北京大学国家软件工程研究中心）；School of Computer Science, Peking University（北京大学计算机学院）
Augmenting Agent Platforms to Facilitate Conversation Reasoning
在多Agent系统中，通过智能体通信语言（Agent Communication Languages，简称CCN）进行的通信在Agent之间的合作、协调和知识共享中起着关键作用。尽管如此，关于代理消息传递的复杂推理，特别是关于代理之间的对话，往往没有得到通用代理程序设计语言的广泛支持。ACRE（智能体通信Reasoning Engine）旨在补充现有Agent程序设计语言的逻辑推理能力，使其具有对复杂交互协议进行推理的能力，从而促进Agent之间的对话。本文概述了ACRE项目的目标，并详细介绍了代理工厂多代理框架内的原型实现的功能。
