Modelado automático del comportamiento de agentes inteligentes

Las teorías más recientes sobre el cerebro humano confirman que un alto porcentaje de su capacidad es utilizado para predecir el futuro, incluyendo el comportamiento de otras personas. Para actuar de la forma más adecuada en un contexto social, los humanos tratan de reconocer el comportamiento de las personas que les rodean y así hacer predicciones basadas en estos reconocimientos. Cuando este proceso se lleva a cabo por agentes software, se conoce como modelado de agentes donde un agente puede ser un robot, un agente software o un humano. El modelado de agentes es un proceso que permite a un agente extraer y representar conocimiento (comportamiento, creencias, metas, acciones, planes, etcétera) de otros agentes en un entorno determinado. Un agente capaz de reconocer el comportamiento de otros, puede realizar diversas tareas como predecir el comportamiento futuro de los agentes observados, coordinarse con ellos, facilitarles la ejecución de sus acciones o detectar sus posibles errores. Si este reconocimiento puede ser realizado de forma automática, su utilidad puede ser muy relevante en muchos dominios. En esta tesis doctoral se aborda la tarea de adquirir automáticamente conocimiento acerca del comportamiento de otros agentes inteligentes. Actualmente, las técnicas para modelar el comportamiento de otros agentes están comenzando a surgir de forma importante en el campo de la Inteligencia Artificial. Cabe destacar que en la mayoría de las investigaciones actuales, se proponen modelados no generales de un determinado tipo de agentes en un dominio concreto, es decir, modelados ad hoc. Esta tesis doctoral presenta tres enfoques diferentes para el modelado automático del comportamiento de agentes inteligentes basado en la identificación de patrones en un comportamiento observado. Estos enfoques permitirán que un agente situado en un entorno determinado, sea capaz de adquirir conocimiento acerca de otros agentes situados en el mismo entorno. Cada enfoque propuesto posee características particulares que le permiten adecuarse a un tipo de dominio, lo que implica que se puede adquirir conocimiento de otros agentes en diversos Sistema Multiagente. Los tres enfoques propuestos transforman las observaciones del comportamiento de uno o varios agentes en una secuencia de eventos que lo definen. Esta secuencia es analizada con la finalidad de obtener su correspondiente modelo de comportamiento. De esta forma, en esta tesis doctoral, la tarea de modelado e identificación del comportamiento de uno o varios agentes es tratado, principalmente, como un problema de minería de secuencias de eventos. La aplicación de cada enfoque propuesto en dominios muy diferentes demuestra su generalidad.-----------------------------------------------------------------------------------There are new theories which claim that a high percentage of the human brain capacity is used for predicting the future, including the behavior of other humans. Planning for future needs, not just current ones, is one of the most formidable human cognitive achievements. To make good decisions in a social context, humans often need to recognize the plan underlying the behavior of others, and make predictions based on this recognition. This process, when carried out by software agents, is known as agent modeling where an agent can be a software agent, a robot or a human being. Agent modeling is the process of extracting and representing knowledge (behavior, beliefs, goals, actions, plans, etcetera) from other agents. By recognizing the behavior of others, many different tasks can be performed, such as to predict their future behavior, to coordinate with them or to assist them. This behavior recognition can be very useful in many applications if it can be done automatically. This thesis is framed in the field of agent behavior modeling. Most existing techniques for plan recognition assume the availability of carefully hand-crafted plan libraries, which encode the a-priori known behavioral repertoire of the observed agents; during run-time, plan recognition algorithms match the observed behavior of the agents against the plan-libraries, and matches are reported as hypotheses. Unfortunately, techniques for automatically acquiring plan-libraries from observations, e.g., by learning or data-mining, are only beginning to emerge. This thesis presents three different approaches for creating automatically the model of an agent behavior based on the analysis of its atomic behaviors. Each approach is suitable for different purposes, but in all of them, the observations of an agent behavior are transformed into a sequence of events which is analyzed in order to get the corresponding behavior model. Therefore, in this thesis, the problem of behavior classification is examined as a problem of learning to characterize the behavior of an agent in terms of sequences of atomic behaviors. In order to demonstrate the generalization of the proposed approaches, their performance has been experimentally evaluated in different domains

El impacto de la crisis sobre la inmigración ecuatoriana en España

El objetivo del artículo es medir el impacto de la crisis económica sobre el colectivo de origen ecuatoriano residente en España; el estudio se basa en una encuesta realizada a dicha población, utilizando técnicas estadísticas de análisis multivariable, se elabora un índice sintético, que segmenta a la población ecuatoriana en función de cómo ha sido afectada por la recesión. El grupo más perjudicado se caracteriza por un empeoramiento en los indicadores asociados a la vivienda y a la situación económica. En el lado opuesto ¾menor impacto de la crisis¾, se sitúan aquellas personas que han llegado más recientemente, son más jóvenes y con un mayor nivel de estudios

Creating user profiles from a command-line interface: a statistical approach

Proceeding of: 17th International Conference on User Modeling, Adaptation, and Personalization (UMAP), Trento, Italy, June 22-26 2009.Knowledge about computer users is very beneficial for assisting them, predicting their future actions or detecting masqueraders. In this paper, an approach for creating and recognizing automatically the behavior profile of a user from the commands (s)he types in a commandline interface, is presented. Specifically, in this research, a computer user behavior is represented as a sequence of UNIX commands. This sequence is transformed into a distribution of relevant subsequences in order to find out a profile that defines its behavior. Then, statistical methods are used for recognizing a user from the commands (s)he types. The experiment results, using 2 different sources of UNIX command data, show that a system based on our approach can efficiently recognize a UNIX user. In addition, a comparison with a HMM-base method is done. Because a user profile usually changes constantly, we also propose a method to keep up to date the created profiles using an age-based mechanism.Publicad

Comparing behavior in agent modelling task

Proceeding of: IADIS International Conference Applied Computing 2006. February 25-28, 2006, San Sebastian, Spain.Reprint from a paper published in the Proceedings of the IADIS International Conference AC 2006In multi-agent system, agents have to analyze several features in order to adapt their behavior to the current situation. This extracted information is usually related to the environment and other agents influence. In this paper we present a method that compare two different agent models in order to extract the qualitative differences between them. This proposed comparative method captures several features of the two agent models and model them considering its behavior.Publicad

The RoboCup agent behavior modeling challenge

Proceedings of: XI Workshop of Physical Agents 2010 in the framework of the Congreso Español De Informática, CEDI 2010. Valencia, Spain. 9th - 10th September, 2010.RoboCup is an international joint project that aims to foster Arti cial Intelligence (AI) and intelligent robotics research by providing a standard problem. RoboCup offers different challenges for intelligent agent researchers in a dynamic, real-time and multi-agent domain. One of these challenges, especially in the Simulation League, is the opponent modeling, which is crucial for the ultimate goal of the RoboCup project: develop a team of fully autonomous. In order to emphasize opponent-modeling approaches, the RoboCup Coach Competition was created and it was held every year (with some changes) from 2001 to 2006. Although there were several interesting research works about the agent modeling challenge during that time, several considerations were not well de ned and the competition was suspended after RoboCup Coach Competition 2006. In this paper, we propose a new approach for the competition to face the opponent modeling challenge in the RoboCup competition.No publicad

A comparing method of two team behaviours in the simulation coach competition

Proceeding of: Third International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2006, Tarragona, Spain, April 3-5, 2006.The main goal of agent modelling is to extract and represent the knowledge about the behaviour of other agents. Nowadays, modelling an agent in multi-agent systems is increasingly becoming more complex and significant. Also, robotic soccer domain is an interesting environment where agent modelling can be used. In this paper, we present an approach to classify and compare the behaviour of a multi-agent system using a coach in the soccer simulation domain of the RoboCup.Publicad

CAOS Coach 2006 Simulation Team: an opponent modelling approach

Agent technology l'epresents a vel'Y intel'esting new means for analyzing, designing and building complex software systems. Nowadays, agent modelling in multi-agent systems is increasingly becoming more complex and significant. RoboCup Coach Cornpetition is an exciting competition in the RoboCup Soccel' League and its main goal is to encourage reseal'ch in multiagent modelling. This papel' describes a novel method used by the team CAOS (CAOS Coach 2006 Simulation Tearn) in this competition. The objective of the team is to model successfully the behaviour of a multi-agent system.This work has been supported by the Spanish Ministry of Education and Science under project TRA-2007-67374-C02-02

An evolving framework for clustering computer users

Proceeding of: International Symposium on Evolving Intelligent Systems (EIS'10) in the 36th Annual Convention of the Society for the Study of Artificial Intelligence and Simulation of Behavior, AISB'10. Leicester, United Kingdom, 29th March - 1st April, 2010.The idea of clustering computer users is very beneficial for making recommendations to a user based on the histories of other users with similar preferences, detecting changes in the behavior of a user, and so on. However, computer users have different needs as they learn to use a software system or their goals changes. Although there are several approaches for clustering users, most of them do not consider the changes in their behavior. In this paper, we present an approach for clustering automatically the behavior profile of a computer user and an evolving method based on Evolving Systems to keep up to date the created profile clusters.This work is partially supported by the Spanish Government under project TRA2007-67374-C02-02No publicad

Spectroelectrochemical Study of the Photoinduced Catalytic Formation of 4,4′-Dimercaptoazobenzene from 4-Aminobenzenethiol Adsorbed on Nanostructured Copper

Surface-enhanced raman scattering (SERS) spectra of self-assembled monolayers of 4-aminobenzenethiol (4-ABT) on copper (Cu) and silver (Ag) surfaces decorated with Cu and Ag nanostructures, respectively, have been obtained with lasers at 532, 632.8, 785, and 1064 nm. Density functional theory (DFT) has been used to obtain calculated vibrational frequencies of the 4-ABT and 4,4′-dimercaptoazobenzene (4,4′-DMAB) molecules adsorbed on model Cu surfaces. The features of the SERS spectra depend on the electrode potential and the type and power density of the laser. SERS spectra showed the formation of the 4,4′-DMAB on the nanostructured Cu surface independently of the laser employed. For the sake of comparison SERS spectra of a self-assembled monolayer of the 4-ABT on Ag surfaces decorated with Ag nanostructures have been also obtained with the same four lasers. When using the 532 and 632.8 nm lasers, the 4,4′-DMAB is formed on Cu surface at electrode potentials as low as −1.0 V (AgCl/Ag) showing a different behavior with respect to Ag (and others metals such as Au and Pt). On the other hand, the surface-enhanced infrared reflection absorption (SEIRA) spectra showed that in the absence of the laser excitation the 4,4′-DMAB is not produced from the adsorbed 4-ABT on nanostructured Cu in the whole range of potentials studied. These results point out the prevalence of the role of electron–hole pairs through surface plasmon activity to explain the obtained SERS spectra.Financial support from Ministerio de Economía y Competitividad (Projects CTQ2013-48280-C3-3-R and CTQ2013-44083-P), Fondos Feder, and the University of Alicante are greatly acknowledged

El autoengaño como mecanismo de mantenimiento de la adicción a las drogas

Background: This study was aimed at: (i) examining levels of self-deception in substance dependent individuals following addiction treatment, and (ii) examining the association between participants’ levels of self-deception and (a) personality disorders, (b) addiction-related beliefs, (c) duration of abstinence, and (d) estimates of craving. Method: We administered self-report questionnaires of self-deception and mixtification, and core beliefs related to addiction and craving. The sample comprised 79 outpatients who were consecutively recruited at the Centro Provincial de Drogodependencias in Granada: 87.3% were males and the mean age was 37.68 years old. Thirty-four percent of participants were diagnosed with comorbid personality disorders. Results: Results showed that individuals with substance dependence exhibit elevated scores of self-deception, particularly in the domains of active denial, selective amnesia, projection, and confabulation. Individuals with comorbid personality disorders display greater levels of self-deception compared to individuals without dual diagnosis. Conclusions: Moreover, there is a significant association between levels of self-deception and addiction-related beliefs and craving. In addition, there is a negative association between levels of self-deception and duration of abstinenceAntecedentes: los objetivos de este estudio fueron: (i) conocer el nivel de autoengaño de drogodependientes en tratamiento por su adicción, y (ii) estudiar la relación del autoengaño con (a) los trastornos de personalidad, (b) las creencias, (c) la abstinencia y (d) el craving en estos pacientes. Método: se utilizaron los cuestionarios de autoengaño y mixtificación (IAM) y de creencias relacionadas con el consumo de drogas y craving. La muestra estaba compuesta por 79 pacientes atendidos de forma consecutiva en el Centro Provincial de Drogodependencias de Granada. El 34.5% de los pacientes presentaban un trastorno de la personalidad. Resultados: los resultados mostraron que los drogodependientes obtienen puntuaciones elevadas en autoengaño, especialmente en los factores negación, amnesia selectiva, proyección y pensamiento fantaseado. Además, los pacientes con trastornos de la personalidad presentan niveles de autoengaño más elevados en comparación a los que no presentan este tipo de psicopatología, observándose una relación significativa entre las creencias nucleares relacionadas con el consumo y con el craving con el nivel de autoengaño. Conclusiones: se constata igualmente que el nivel de autoengaño se relaciona de forma negativa con el tiempo de abstinencia, lo que convierte al autoengaño en una diana terapéutica para mejorar el pronósticoS