Laurent Prosperi

Sorbonne Université, CNRS, INRIA, LIP6 · Delys

We introduce a novel comprehensive framework for epistemic reasoning in multi-agent systems where agents may behave asynchronously and may be byzantine faulty. Extending Fagin et al.’s classic runs-and-systems framework to agents who may arbitrarily deviate from their protocols, it combines epistemic and temporal logic and incorporates fine-grained...

Causality is an important concept both for proving impossibility results and for synthesizing efficient protocols in distributed computing. For asynchronous agents communicating over unreliable channels, causality is well studied and understood. This understanding, however, relies heavily on the assumption that agents themselves are correct and rel...

Causal analysis of asynchronous distributed multi-agent systems

Causality is an important concept both for proving impossibility results and for synthesizing efficient protocols in distributed computing. For asynchronous agents communicating over unreliable channels, causality is well studied and understood. This understanding, however, relies heavily on the assumption that agents themselves are correct and rel...

We present an extension incorporating Byzantine agents into the epistemic runs-and-systems framework for modeling distributed systems introduced by Fagin et al. [FHMV95]. Our framework relies on a careful separation of concerns for various actors involved in the evolution of a message-passing distributed system: the agents' protocols, the underlyin...

Stream processing applications handle unbounded and continuous flows of data items which are generated from multiple geographically distributed sources. Two approaches are commonly used for processing: Cloud-based analytics and Edge analytics. The first one routes the whole data set to the Cloud, incurring significant costs and late results from th...