Epistemic Logic over Similarity Graphs: Common, Distributed and Mutual Knowledge

TL;DR

This paper investigates epistemic logics over similarity graphs, extending traditional models to include common, distributed, and mutual knowledge, analyzing their expressivity, proof systems, and computational complexity.

Contribution

It introduces new semantics for epistemic modalities based on similarity models and explores their logical properties and computational aspects.

Findings

Enhanced understanding of knowledge modalities over similarity graphs

Analysis of expressivity and semantic correspondence with classical logic

Complexity results for model checking and satisfiability

Abstract

In this paper, we delve into the study of epistemic logics, interpreted through similarity models based on weighted graphs. We explore eight languages that extend the traditional epistemic language by incorporating modalities of common, distributed, and mutual knowledge. The concept of individual knowledge is redefined under these similarity models. It is no longer just a matter of personal knowledge, but is now enriched and understood as knowledge under the individual's epistemic ability. Common knowledge is presented as higher-order knowledge that is universally known to any degree, a definition that aligns with existing literature. We reframe distributed knowledge as a form of knowledge acquired by collectively leveraging the abilities of a group of agents. In contrast, mutual knowledge is defined as the knowledge obtained through the shared abilities of a group. We then focus on the resulting logics, examining their relative expressivity, semantic correspondence to the classical epistemic logic, proof systems and the computational complexity associated with the model checking problem and the satisfiability/validity problem. This paper offers significant insights into the logical analysis and understanding of these enriched forms of knowledge, contributing to the broader discourse on epistemic logic.