Editor's Note

The international conference “Disruptive Technologies Tech Ethics and Artificial Intelligence” (DITTET) provides a forum to present and discuss the latest scientific and technical advances and their implications in the field of ethics. It also provides a forum for experts to present their latest research in disruptive technologies, promoting knowledge transfer. It provides a unique opportunity to bring together experts in different fields, academics, and professionals to exchange their experience in the development and deployment of disruptive technologies, artificial intelligence, and their ethical problems. This Special Issue contains extended versions of selected works presented at the 1st International Conference on Disruptive Technologies, Tech Ethics and Artificial Intelligence (DiTTEt 2021), held in Salamanca (Spain) in September 2021

Self-organizing multi-agent system for management and planning surveillance routes

This paper presents the THOMAS architecture, specially designed to model open multi-agent systems, and its application in the development of a multi-agent system for managing and planning surveillance routes for security personnel. THOMAS uses agents with reasoning and planning capabilities. These agents can perform a dynamic self-organization when they detect changes in the environment. THOMAS is appropriate for developing systems in highly dynamic environments similar to the one presented in this study, as demonstrated by the results obtained after having applied the system to a case study.Web of Science3151100108

Dealing with Demand in Electric Grids with an Adaptive Consumption Management Platform

The control of consumption in homes and workplaces is an increasingly important aspect if we consider the growing popularity of smart cities, the increasing use of renewable energies, and the policies of the European Union on using energy in an efficient and clean way. These factors make it necessary to have a system that is capable of predicting what devices are connected to an electrical network. For demand management, the system must also be able to control the power supply to these devices. To this end, we propose the use of a multiagent system that includes agents with advanced reasoning and learning capacities. More specifically, the agents incorporate a case-based reasoning system and machine learning techniques. Besides, the multiagent system includes agents that are specialized in the management of the data acquired and the electrical devices. The aim is to adjust the consumption of electricity in networks to the electrical demand, and this will be done by acting automatically on the detected devices. The proposed system provides promising results; it is capable of predicting what devices are connected to the power grid at a high success rate. The accuracy of the system makes it possible to act according to the device preferences established in the system. This allows for adjusting the consumption to the current demand situation, without the risk of important home appliances being switched off

Self-Organizing Multi-Agent System for Management and Planning Surveillance Routes

This paper presents the THOMAS architecture, specially designed to model open multi-agent systems, and its application in the development of a multi-agent system for managing and planning surveillance routes for security personnel. THOMAS uses agents with reasoning and planning capabilities. These agents can perform a dynamic self-organization when they detect changes in the environment. THOMAS is appropriate for developing systems in highly dynamic environments similar to the one presented in this study, as demonstrated by the results obtained after having applied the system to a case study

idMAS-SQL: Intrusion Detection Based on MAS to Detect and Block SQL injection through data mining

This study presents a multiagent architecture aimed at detecting SQL injection attacks, which are one of the most prevalent threats for modern databases. The proposed architecture is based on a hierarchical and distributed strategy where the functionalities are structured on layers. SQL-injection attacks, one of the most dangerous attacks to online databases, are the focus of this research. The agents in each one of the layers are specialized in specific tasks, such as data gathering, data classification, and visualization. This study presents two key agents under a hybrid architecture: a classifier agent that incorporates a Case-Based Reasoning engine employing advanced algorithms in the reasoning cycle stages, and a visualizer agent that integrates several techniques to facilitate the visual analysis of suspicious queries. The former incorporates a new classification model based on a mixture of a neural network and a Support Vector Machine in order to classify SQL queries in a reliable way. The latter combines clustering and neural projection techniques to support the visual analysis and identification of target attacks. The proposed approach was tested in a real-traffic case study and its experimental results, which validate the performance of the proposed approach, are presented in this paperSpanish Ministry of Science projects OVAMAH (TIN 2009-13839-C03-03) and MIDAS (TIN 2010-21272-C02-01), funded by the European Regional Development Fund, projects of the Junta of Castilla and Leon BU006A08 and JCYL-2002-05; Projects of the Spanish Government SA071A08, CIT-020000-2008-2 and CIT-020000-2009-12; the Professional Excellence Program 2006-2010 IFARHU-SENACYT-Panama. The authors would also like to thank the vehicle interior manufacturer, Grupo Antolin Ingenieria S.A., within the framework of the project MAGNO2008 - 1028. - CENIT Project funded by the Spanish Ministry

Embedded agents to monitor sounds

Ambient intelligent has advanced in the last years. The inclusion of Artificial Intelligent techniques, as pattern recognition, has allowed these systems to have a better adaptation to the environments. In this work, a multiagent system based on PANGEA and embedded agents to manage and monitor alarms is shown. The system incorporates embedded agents in Arduino hardware devices with modules to detect sounds and luminosity bands

Obtaining Relevant Genes by Analysis of Expression Arrays with a Multi-Agent System

Triple negative breast cancer (TNBC) is an aggressive form of breast cancer. Despite treatment with chemotherapy, relapses are frequent and response to these treatments is not the same in younger women as in older women. Therefore, the identification of genes that provoke this disease is required, as well as the identification of therapeutic targets.There are currently different hybridization techniques, such as expression ar-rays, which measure the signal expression of both the genomic and tran-scriptomic levels of thousands of genes of a given sample. Probesets of Gene 1.0 ST GeneChip arrays provide the ultimate genome transcript coverage, providing a measurement of the expression level of the sample.This paper proposes a multi-agent system to manage information of expres-sion arrays, with the goal of providing an intuitive system that is also extensible to analyze and interpret the results.The roles of agent integrate different types of techniques, from statistical and data mining techniques that select a set of genes, to search techniques that find pathways in which such genes participate, and information extraction techniques that apply a CBR system to check if these genes are involved in the disease

A new clustering algorithm applying a hierarchical method neural network.

[EN]Clustering is a branch of multivariate analysis that is used to create groups of data. Most of the existing clustering techniques require defining additional information, including the actual number of clusters, before they can be carried out. This article presents a novel neural network that is capable of creating groups by using a combination of hierarchical clustering and self-organizing maps, without requiring the number of existing clusters to be specified beforehand. The self-organized cluster automatic detection neural network is described in detail, focusing on the density, the average distance, the division algorithm, the update algorithm and the training phase. Three case studies have been carried out in this research in order to evaluate the performance of the neural network, and the results obtained are presented within this article

Traffic Optimization Through Waiting Prediction and Evolutive Algorithms

Traffic optimization systems require optimization procedures to optimize traffic light timing settings in order to improve pedestrian and vehicle mobility. Traffic simulators allow obtaining accurate estimates of traffic behavior by applying different timing configurations, but require considerable computational time to perform validation tests. For this reason, this project proposes the development of traffic optimizations based on the estimation of vehicle waiting times through the use of different prediction techniques and the use of this estimation to subsequently apply evolutionary algorithms that allow the optimizations to be carried out. The combination of these two techniques leads to a considerable reduction in calculation time, which makes it possible to apply this system at runtime. The tests have been carried out on a real traffic junction on which different traffic volumes have been applied to analyze the performance of the system

State of the Art and System Overview

The promotion of changes in users’ behaviors with the aim of saving energy consumption in public buildings is a complex task that requires the use of multiple technologies. In this sense, context-aware technologies such as Wireless Sensor Networks and Real-Time Locating Systems, along with the use of Collaborative Learning, Virtual Organizations of Agents and Social Computing, provide a great potential for the development of serious games that foster the acquisition of good energy and healthy habits among workers and users in the public building. This paper presents the development of a serious game to change the users’ behaviors when using resources in public buildings using CAFCLA, a framework that allows the integration of multiple technologies that facilitate both context-awareness and social computing.info:eu-repo/semantics/publishedVersio