๐ฎ
๐ฎ
The Ethereal
A Simplicial Complex Model for Dynamic Epistemic Logic to study Distributed Task Computability
September 10, 2018 ยท The Ethereal ยท ๐ International Symposium on Games, Automata, Logics and Formal Verification
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
รric Goubault, Jรฉrรฉmy Ledent, Sergio Rajsbaum
arXiv ID
1809.03095
Category
cs.LO: Logic in CS
Cross-listed
cs.DC
Citations
45
Venue
International Symposium on Games, Automata, Logics and Formal Verification
Last Checked
2 months ago
Abstract
The usual epistemic model S5n for a multi-agent system is based on a Kripke frame, which is a graph whose edges are labeled with agents that do not distinguish between two states. We propose to uncover the higher dimensional information implicit in this structure, by considering a dual, simplicial complex model. We use dynamic epistemic logic (DEL) to study how an epistemic simplicial complex model changes after a set of agents communicate with each other. We concentrate on an action model that represents the so called immediate snapshot communication patterns of asynchronous agents, because it is central to distributed computability (but our setting works for other communication patterns). There are topological invariants preserved from the initial epistemic complex to the one after the action model is applied, which determine the knowledge that the agents gain after communication. Finally, we describe how a distributed task specification can be modeled as a DEL action model, and show that the topological invariants determine whether the task is solvable. We thus provide a bridge between DEL and the topological theory of distributed computability, which studies task solvability in a shared memory or message passing architecture.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Logic in CS
๐ฎ
๐ฎ
The Ethereal
Safe Reinforcement Learning via Shielding
๐ฎ
๐ฎ
The Ethereal
Formal Verification of Piece-Wise Linear Feed-Forward Neural Networks
๐ฎ
๐ฎ
The Ethereal
Heterogeneous substitution systems revisited
๐ฎ
๐ฎ
The Ethereal
Omega-Regular Objectives in Model-Free Reinforcement Learning
๐ฎ
๐ฎ
The Ethereal