Distributed Non-monotonic Reasoning for Transportation Network Congestion Monitoring: Theoretical Foundation
A Boulmakoul, L Karim, G Cherradi… - … Symposium on Intelligent …, 2021 - Springer
International Symposium on Intelligent and Distributed Computing, 2021•Springer
In this work, we formulate premises of a non-monotonic and distributed logical theory to
model the traffic control actions of urban networks. The distributed intelligent agent
transaction mechanisms based on distributed default logic are used. We demonstrate the
validity of our approach and the contribution of the revisable reasoning formalism and the
knowledge representation of multi-agent systems for the problems of traffic congestion
management and supervision according to a route or arterial approach. In distributed and …
model the traffic control actions of urban networks. The distributed intelligent agent
transaction mechanisms based on distributed default logic are used. We demonstrate the
validity of our approach and the contribution of the revisable reasoning formalism and the
knowledge representation of multi-agent systems for the problems of traffic congestion
management and supervision according to a route or arterial approach. In distributed and …
Abstract
In this work, we formulate premises of a non-monotonic and distributed logical theory to model the traffic control actions of urban networks. The distributed intelligent agent transaction mechanisms based on distributed default logic are used. We demonstrate the validity of our approach and the contribution of the revisable reasoning formalism and the knowledge representation of multi-agent systems for the problems of traffic congestion management and supervision according to a route or arterial approach. In distributed and collaborative environments, revisable reasoning processes provide necessary facts to activate default rules, which gives the reactive agent the best use of its local and external knowledge. These mechanisms allow the network’s symbolic control system to operate efficiently in a dynamic and hypothetical environment, where basic facts and observations are often problematic.
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