A Data Fusion System for Simulation of Critical Scenarios and Decision-Making

Abstract

<jats:p>The decision-making (DM) process in critical environments is a complex process that can be simulated due to current telematic capabilities, which allow the real time interaction of large amounts of data. This document describes the proposed architecture from a research process, developed by the FAC Aerospace Technology Development Center (CETAD), where using computational and expert system tools, allowed to create a computational environment for decision maker evaluated his options to prepares for real events, simulating characteristics, resources and strategies in a real time environment.&#x0D; This document describes an investigation product resulted in a simulation system, based on a combination of fuzzy logic, genetic algorithms and decision trees which let modelled and simulated various entities and their automatic response according to simulated patterns and situations, in which, through operators, decision maker can modify entities behaviour, according to parameterized restrictions and physical conditions. Also based on business intelligence tools, reports are generated to evaluate the decisions made. This type of technologies improves planning capacity and facilitate the decision-making process.&#x0D; System allows simulating any media deployment in national security and critical events context. Thus, a case study was developed for implementation of a support in natural disaster scenario simulation</jats:p>

Special Issue in Artificial Intelligence

“…AI applications include prediction, recommendation, classification and recognition, object detection, natural language processing, autonomous systems, among others. The topics of the articles in this special issue include deep learning applied to medicine [1,3], support vector machines applied to ecosystems [2], human-robot interaction [4], clustering in the identification of anomalous patterns in communication networks [5], expert systems for the simulation of natural disaster scenarios [6], real-time algorithms of artificial intelligence [7], and big data analytics for natural disasters [8].…”