LIPIcs.CONCUR.2019.29.pdf
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Reasoning About Distributed Knowledge of Groups with Infinitely Many Agents
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30th International Conference on Concurrency Theory (CONCUR 2019)
Series: Leibniz International Proceedings in Informatics (LIPIcs)
Conference: International Conference on Concurrency Theory (CONCUR) - License:
Creative Commons Attribution 3.0 Unported license
- Publication Date: 2019-08-20
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Michell Guzmán, Sophia Knight, Santiago Quintero, Sergio Ramírez, Camilo Rueda, and Frank Valencia. Reasoning About Distributed Knowledge of Groups with Infinitely Many Agents. In 30th International Conference on Concurrency Theory (CONCUR 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 140, pp. 29:1-29:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
https://doi.org/10.4230/LIPIcs.CONCUR.2019.29
https://doi.org/10.4230/LIPIcs.CONCUR.2019.29
Abstract
Spatial constraint systems (scs) are semantic structures for reasoning about spatial and epistemic information in concurrent systems. We develop the theory of scs to reason about the distributed information of potentially infinite groups. We characterize the notion of distributed information of a group of agents as the infimum of the set of join-preserving functions that represent the spaces of the agents in the group. We provide an alternative characterization of this notion as the greatest family of join-preserving functions that satisfy certain basic properties. We show compositionality results for these characterizations and conditions under which information that can be obtained by an infinite group can also be obtained by a finite group. Finally, we provide algorithms that compute the distributive group information of finite groups.
Subject Classification
ACM Subject Classification
- Theory of computation → Concurrency
- Theory of computation → Distributed computing models
- Theory of computation → Semantics and reasoning
Keywords
- Reasoning about Groups
- Distributed Knowledge
- Infinitely Many Agents
- Reasoning about Space
- Algebraic Modeling
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References
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