La charla titulada Teaching Big Data with Pen, Paper and Scissors se ha presentado en la  7th International Congress on Education and Learning en París, Francia, el 18 de Julio de 2018. Este trabajo se ha realizado gracias a la financiación ofrecida por la Universidad Politécnica de Madrid bajo la inciativa de Innovación Educativa, en relación al Proyecto de Innovación Educativa no. IE1718.0902, titulado Un enfoque de Aprendizaje basado en Retos para Técnicas de Análisis de Datos.

The paper A Model of Radicalization Growth using Agent-based Social Simulation, by Tasio Méndez, J. Fernando Sánchez-Rada and Carlos A. Iglesias in collaboration with Paul Cummings from George Mason University is being presented at the 6th International Workshop on Engineering Multi-Agent Systems (EMAS) which has been held as part of the International Conference on Autonomous Agents and Multiagent Systems (AAMAS) since 2013 and was affiliated to AAMAS through AOSE, ProMAS, DALT since the inception of these earlier workshops.

EMAS 2018 aims to gather researchers and practitioners in the domains of agent-oriented software engineering, programming multi-agent systems, declarative agent languages and technologies, machine learning, and AI in general, and AI and machine learning to present and discuss their research and emerging results in MAS engineering.

Abstract. This work presents an agent based model of radicalization growth based on social theories. The model aims at improving the understanding of the influence of social links on radicalism spread. The model consists of two main entities, a Network Model and an Agent Model. The Network Model updates the agent relationships based on proximity and homophily, it simulates information diffusion and updates the agents' beliefs. The model has been evaluated and implemented in Python with the agent-based social simulator Soil. In addition, it has been evaluated using a sensitivity analysis.

The EMAS 2018 workshop is being hold July 14-15, at Stockholm, Sweden.


The 2nd year review meeting of the project EMOSPACES is being hold at CEA, Paris.

GSI is presenting the Emospaces ontology and the emotion aware task automation system. 


The paper An Agent-based Simulation Model for Emergency Egress, by Álvaro Carrera, Eduardo Merino, Pablo Aznar, Guillermo Fernández y Carlos A. Iglesias, has been presented at the 15th International Conference on Distributed Computing and Artificial Intelligence (DCAI 2018) in a Special Session on Social Modelling of Ambient Intelligence in Large Facilities (SMAILF 2018).

Abstract. Unfortunately, news regarding tragedies involving crowd evacuations are becoming more and more common. Understanding disasters and crowd emergency evacuation behaviour is essential to define effective evacuation protocols. This paper proposes an agent-based model of egress behaviour consisting of three complementary models: (i) model of people moving in a building in normal circumstances, (ii) policies of egress evacuation, and (iii) social models for integrating models (e.g. affiliation) that explain the social behaviour and help in mass evacuations. The proposed egress model has been evaluated in a university building and the results show how these models can help to better understand egress behaviour and apply this knowledge for improving the design and execution evacuation plans.

The DCAI 2018 conference was held June 20-22, at Toledo, Spain.

Cuándo:  martes 19 de junio a las 16:00

Dónde: sala B-225 de la ETSIT (UPM).

Ponente: Jürgen Dunkel

Título: Learning Complex Event Processing Rules with Genetic Programming

Resumen: Complex Event Processing (CEP) is an established software technology to extract relevant information from massive data streams. Currently, domain experts have to determine manually CEP rules that define a situation of interest. However, often CEP rules cannot be formulated by experts, because the relevant interdependencies and relations between the data are not explicitly known, but inherently hidden in the data streams. To cope with this problem, we present a new learning approach for CEP rules, which is based on Genetic Programming. We discuss in detail the different building blocks of Genetic Programming and how to adjust them to CEP rule learning. The extensive experiments, with synthetic data as well a real world data, show, that Genetic Programming has a high potential for learning CEP rules. In most of our experiments we could derive CEP rules with a nearly perfect recall and precision.