T-search : buscador con tesauro para wikis

El auge de los entornos wiki, cada vez más extendidos, hace necesarias herramientas que permitan recuperar de forma eficiente la información de estos entornos. Hasta ahora los buscadores genéricos como Google son las mejores herramientas para encontrar en wikis como Wikipedia, pero no para wikis de menor difusión, donde Google y otros buscadores sólo tienen indizada una pequeña parte del sitio. Por otro lado, los buscadores que incorporan los entornos wiki son muy limitados, y sólo realizan búsquedas a partir del titulo o de la URL. Los buscadores que asisten a los usuarios con búsquedas de términos relacionados, son muy útiles en ciertos entornos, como por ejemplo en los entornos académicos, ya que permiten descubrir y buscar conceptos relacionados con los que se buscaban inicialmente. Esta característica puede ser muy útil en las wikis, que suelen ser utilizadas como enciclopedias. Este proyecto implementa un buscador para entornos wiki, de código abierto, multiplataforma, escrito en Java y PHP, distribuido y escalable, que se utilizará para buscar sobre el wiki del departamento de Ingeniería del Software de la Universidad Carlos III de Madrid.Ingeniería en Informátic

Painting Authorship and Forgery Detection Challenges with AI Image Generation Algorithms: Rembrandt and 17th Century Dutch Painters as a Case Study

Image authorship attribution presents many challenges and difficulties which have increased with the capabilities presented by synthetic image generation through different artificial intelligence algorithms available today. The hypothesis in this research considers the possibility of using artificial intelligence as a tool to detect forgeries through the usage of a deep learning algorithm. The proposed algorithm was trained using a dataset comprised of paintings by Rembrandt and other 17th century Dutch painters. Three experiments were performed with the proposed algorithm. The first was to build a classifier able to ascertain whether a painting belongs to the Rembrandt or non-Rembrandt category, depending on whether it was painted by this author or not. The second tests included other 17th century painters in four categories. Artworks could be classified as Rembrandt, Eeckhout, Leveck or other Dutch painters. The third experiment used paintings generated by Dall-e 2 and attempted to classify them using the prior categories. Experiments confirmed the hypothesis with best executions reaching accuracy rates of more than 90%. Future research with extended datasets and improved image resolution are suggested to improve the obtained results

SIMBA: a simulator for business education and research

Business simulators are used for decision-making since different scenarios can be evaluated without risk. They are also used in business management education. The main goal of this paper is to introduce SIMBA (SIMulator for Business Administration), a new simulator that serves as a web-based platform for business education, permitting both classroom and distance education. This paper also adds a research aspect in business intelligence because SIMBA can be used as a fieldwork tool for the development and evaluation of intelligent agents. The simulator creates a more complex competitive environment in which intelligent agents play the role of business decision makers.This work has been partially sponsored by a regional project CCG08-UC3M/TIC-4141 of the Comunidad de Madrid, a national project TIN2008-06701-C03-03 of the Ministerio de Ciencia e Innovación of Spain and a contract with Simuladores Empresariales S.L.Publicad

Instance-based defense against adversarial attacks in Deep Reinforcement Learning

Deep Reinforcement Learning systems are now a hot topic in Machine Learning for their effectiveness in many complex tasks, but their application in safety-critical domains (e.g., robot control or self-autonomous driving) remains dangerous without mechanism to detect and prevent risk situations. In Deep RL, such risk is mostly in the form of adversarial attacks, which introduce small perturbations to sensor inputs with the aim of changing the network-based decisions and thus cause catastrophic situations. In the light of these dangers, a promising line of research is that of providing these Deep RL algorithms with suitable defenses, especially when deploying in real environments. This paper suggests that this line of research could be greatly improved by the concepts from the existing research field of Safe Reinforcement Learning, which has been postulated as a family of RL algorithms capable of providing defenses against many forms of risks. However, the connections between Safe RL and the design of defenses against adversarial attacks in Deep RL remain largely unexplored. This paper seeks to explore precisely some of these connections. In particular, this paper proposes to reuse some of the concepts from existing Safe RL algorithms to create a novel and effective instance-based defense for the deployment stage of Deep RL policies. The proposed algorithm uses a risk function based on how far a state is from the state space known by the agent, that allows identifying and preventing adversarial situations. The success of the proposed defense has been evaluated in 4 Atari games

Supporting the Collaboration between Programmers and Designers Building Game AI

Part 3: PostersInternational audienceThe design of the behavior of non-player characters (NPCs) in a game is a collaborative task between programmers and designers. Nevertheless this collaboration is an open problem since the limits, responsibilities and competences are not well defined.Behavior trees are the technology of choice nowadays for programming the behavior of NPCs, and they are first and foremost a programmers tool. In this paper we describe an experiment that shows that with the right division of labor and a reduced background in Programming, designers can also build behavior trees and thus find a principled way to collaborate with programmers in that task

Comparison of a Tablet Versus Computer-Based Classical Theatre Game Among 8–13 Year Children

In the last ten years, many studies have shown the advantages of videogames as tools for learning, engagement, raising awareness, or increasing interest in different fields. Schools are often the main focus of those studies. However, schools have either PCs or tablets in their classrooms, but rarely have both. That represents a severe limitation to videogame researchers since they can only deploy their video games in schools with the adequate platform for their video games. Researchers are therefore restricted in the number of schools in which they can conduct their experiments. In this paper, we study a videogame's effectiveness in increasing interest towards classical theatre, depending on the platform deployed (computer or mobile device). To that aim, we used 'The Courtesy of Spain,' a point-and-click videogame created explicitly for this study, based on the play of the same name by Golden Age playwright Lope de Vega. To measure the abovementioned effectiveness, we implemented a quasi-experimental design with a comparison and an experimental group. The experiment involved 542 students between 8 and 13 years old from several middle schools in Madrid's Community (Spain). The study indicates that the videogame developed is equally effective on both devices (Sig <0.05). Our results will allow serious game developers to design one-fits-all games without jeopardizing their efficiency, which broadens the target schools where their games can be tested

Using Graphs of Queues and Genetic Algorithms to Fast Approximate Crowd Simulations

The use of Crowd Simulation for re-enacting different real life scenarios has been studied in the literature. In this field of research, the interplay between ambient assisted living solutions and the behavior of pedestrians in large installations is highly relevant. However, when designing these simulations, the necessary simplifications may result in different ranges of accuracy. The more realistic the simulation task is, the more complex and computational expensive it becomes. We present an approach towards a reasonable trade-off: given a complex and computational expensive crowd simulation, how to produce fast crowd simulations whose results approximate the results of the detailed and more realistic model. These faster simulations can be used to forecast the outcome of several scenarios, enabling the use of simulations in decision-making methods. This work contributes with a simplified faster simulation model that uses a graph of queues for modeling an environment where a set of agents will navigate. This model is configured using Genetic Algorithms (GA) applied to data obtained from complex 3D crowd simulations. This is illustrated with a proof-of-concept scenario where a 3D simulation of one floor of a faculty building, with its corresponding students, is re-enacted in the network of queues version. The success criteria are achieving a similar total number of people in particular floor areas along the simulation in both the simplified simulation and the original one. The experiments confirm that this approach approximates the number of people in each area with a sufficient degree of fidelity with respect to the results that are obtained by a more complex 3D simulator

Understanding Everything NPCs Can Do: Metrics for Action Similarity in Non-Player Characters

Non-Player Characters (NPCs) have actions that allow them to reason about what they can do in a game and how they can do it. The background information about what they can do are the components of the action, and how they can do it is the form or shape of an action, which may be built up from several sub- actions. The components and shape of an action must be fully defined in a game, which can be tedious when several similar actions are needed. Furthermore, as the number of nuanced actions grows, more pressure is placed on an already constrained reasoning system. By discovering the similarities between actions an NPC can do, a given action set can be intelligently organized with similar components being generalized using an action taxonomy. To understand the similarity between actions we have developed measures of action similarity based on their constituent components and form. From this, wediscover a metric to determine the generalization ability of an organization strategy for an NPC action set. We examine the use of our measures on a previously developed action set to show the nuances between those actions. Lastly, we find that intelligently organizing actions has a positive effect on virtual character reasoning abilities.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Comp Graphics & Visualisatio

CEV 2022 - 1st Spanish Congress on Videogames 2022

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