paper-with-me

Papers

Resolving Distributed Knowledge

2016-06-24 · Thomas Ågotnes, Yì N. Wáng

Distributed knowledge is the sum of the knowledge in a group; what someone who is able to discern between two possible worlds whenever any member of the group can discern between them, would know. Sometimes distributed knowledge is referred to as the potential knowledge of a group, or the joint knowledge they could obtain if they had unlimited means of communication. In epistemic logic, the formula D_G{\phi} is intended to express the fact that group G has distributed knowledge of {\phi}, that there is enough information in the group to infer {\phi}. But this is not the same as reasoning about what happens if the members of the group share their information. In this paper we introduce an operator R_G, such that R_G{\phi} means that {\phi} is true after G have shared all their information with each other - after G's distributed knowledge has been resolved. The R_G operators are called resolution operators. Semantically, we say that an expression R_G{\phi} is true iff {\phi} is true in what van Benthem [11, p. 249] calls (G's) communication core; the model update obtained by removing links to states for members of G that are not linked by all members of G. We study logics with different combinations of resolution operators and operators for common and distributed knowledge. Of particular interest is the relationship between distributed and common knowledge. The main results are sound and complete axiomatizations.

📄 PDF Abstract BibTeX arXiv:1606.07515

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Distributed Answer Set Coloring: Stable Models Computation via Graph Coloring

2019-09-18 · Marco De Bortoli

Answer Set Programming (ASP) is a famous logic language for knowledge representation, which has been really successful in the last years, as witnessed by the great interest into the development of efficient solvers for A…

CodeV: Issue Resolving with Visual Data

2024-12-23 · Linhao Zhang, Daoguang Zan, Quanshun Yang, Zhirong Huang 외

Large Language Models (LLMs) have advanced rapidly in recent years, with their applications in software engineering expanding to more complex repository-level tasks. GitHub issue resolving is a key challenge among these …

DynamicER: Resolving Emerging Mentions to Dynamic Entities for RAG

2024-10-15 · Jinyoung Kim, Dayoon Ko, Gunhee Kim

In the rapidly evolving landscape of language, resolving new linguistic expressions in continuously updating knowledge bases remains a formidable challenge. This challenge becomes critical in retrieval-augmented generati…

Entity LinkingRAGRetrievalRetrieval-augmented Generation

Discerning and Resolving Knowledge Conflicts through Adaptive Decoding with Contextual Information-Entropy Constraint

2024-02-19 · Xiaowei Yuan, Zhao Yang, Yequan Wang, Shengping Liu 외

Large language models internalize enormous parametric knowledge during pre-training. Concurrently, realistic applications necessitate external contextual knowledge to aid models on the underlying tasks. This raises a cru…

RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representations

2024-02-27 · Jing Huang, Zhengxuan Wu, Christopher Potts, Mor Geva 외

Individual neurons participate in the representation of multiple high-level concepts. To what extent can different interpretability methods successfully disentangle these roles? To help address this question, we introduc…

AttributeLanguage ModelingLanguage Modelling