19 pages, 5 figures, appears (without proofs) in Proceedings of the Seventh International Conference on Principles of Knowledge Representation and Reasoning (KR 2000)
发表信息：
Proceedings of the Seventh International Conference on Principles of Knowledge Representation and Reasoning (KR 2000), April 2000, pp. 153-164
We consider the two-fold problem of representing collective beliefs and aggregating these beliefs. We propose modular, transitive relations for collective beliefs. They allow us to represent conflicting opinions and they have a clear semantics. We compare them with the quasi-transitive relations often used in Social Choice. Then, we describe a way to construct the belief state of an agent informed by a set of sources of varying degrees of reliability. This construction circumvents Arrow's Impossibility Theorem in a satisfactory manner. Finally, we give a simple set-theory-based operator for combining the information of multiple agents. We show that this operator satisfies the desirable invariants of idempotence, commutativity, and associativity, and, thus, is well-behaved when iterated, and we describe a computationally effective way of computing the resulting belief state.
Collaborative Belief Reasoning with LLMs for Efficient Multi-Agent Collaboration
Shenzhen International Graduate School, Tsinghua University（深圳国际研究生院，清华大学）；Huawei Tenchnologies Co., Ltd.（华为技术有限公司）；University of Science and Technology of China（中国科学技术大学）
Visualizing Uncertainty: Spatial Maps of Missing and Conflicting Evidence in Deep Learning
University of the District of Columbia（哥伦比亚特区大学）；University of Texas at Dallas（德克萨斯大学达拉斯分校）；Virginia Tech（弗吉尼亚理工大学）；U.S. Army DEVCOM Army Research Laboratory（美国陆军DEVCOM陆军研究实验室）；University of Oslo（奥斯陆大学）；Bowie State University（鲍伊州立大学）
