Neural-logic multi-agent system for flood event detection
What is it about?
This paper shows the capabilities offered by an integrated neural-logic multi-agent system (MAS). Our case study encompasses logical agents and a deep learning (DL) component, to devise a system specialised in monitoring flood events for civil protection purposes. More precisely, we describe a prototypical framework consisting of a set of intelligent agents, which perform various tasks and communicate with each other to efficiently generate alerts during flood crisis events. Alerts are only delivered when at least two separates sources agree on an event on the same zone, i.e. aerial images and severe weather reports. Images are segmented by a neural network trained over eight classes of topographical entities. The resulting mask is analysed by a Logic Image Descriptor (LID) which then submit the perception to a logical agent.
Why is it important?
Our system is designed with the primary goal of providing accurate and reliable alerts or pre-alertsfor severe weather situations. To achieve this, we have employed a perception-fusion approach that integrates logical agents and a deep learning component to enable effective reasoning with various forms of information. our system represents a significant step towards our long-term objective of developing an advanced version capable of rapidly identifying flood events and alerting relevant authorities. This system is a first example of a class of real-time applications where a stream of real-time data must be analyzed, filtered, evaluated, and provided to the user together with explanations, to enable prompt countermeasures.