Editorial

calls & calendarEDITORIA

Study of Computational Intelligence Algorithms to Detect Behaviour Patterns

In order to achieve the game flow and increase player retention, it is important that games difficulty matches player skills. As a consequence, to evaluate how people play a game is a crucial component, because detecting gamers strategies in video-games, it is possible to fix the game difficulty. The main problem to detect the strategies is whether attributes selected to define the strategies correctly detect the actions of the player. To study the player strategies, we will use a Real Time Stategy (RTS) game. In a RTS the players make use of units and structures to secure areas of a map and/or destroy the opponents resources. In this work, we will extract the real-time information about the players strategies using a platform base on the RTS game. After gathering information, the attributes that define the player strategies are evaluated using unsupervised learning algorithm (K-Means and Spectral Clustering). Finally, we will study the similitude among several gameplays where players use different strategies.A fin de lograr que el flujo del juego mejore y la captación de jugadores aumente, es importante que la dificultad del juego se ajuste a las habilidades del jugador. Como consecuencia, evaluar como juega la gente un juego es un aspecto importante, porque detectando las estrategias de los jugadores en los vídeo juegos, permite adapta la dificultad del juego. El problema principal para detectar las estrategias es si los atributos seleccionados para definir las estrategias definen correctamente las acciones del jugador. Para estudiar las estrategias de los jugadores, usaremos un juego de estrategia en tiempo real (Reat Time Strategy (RTS) en inglés). En un RTS los jugadores hacen uso de unidades y estructuras para asegurar áreas del mapa y/o destruir los recursos de los oponentes. En este trabajo, extraeremos información en tiempo real acerca de las estrategias usando una plataforma basada en un juego de RTS. Después de recoger la información, los atributos que definen las estrategias de los jugadores son evaluados mediante algoritmos de aprendizaje no supervisado (K-Means y Spectral Clustering). Finalmente, estudiaremos la similitud entre diversas partidas donde los jugadores utilizar diferentes estrategias.Este trabajo ha sido financiado por Airbus Defence & Space (Proyecto Savier: FUAM-076914) y parcialmente por TIN2010-19872

From the Hands of an Early Adopter's Avatar to Virtual Junkyards: Analysis of Virtual Goods' Lifetime Survival

One of the major questions in the study of economics, logistics, and business forecasting is the measurement and prediction of value creation, distribution, and lifetime in the form of goods. In "real" economies, a perfect model for the circulation of goods is impossible. However, virtual realities and economies pose a new frontier for the broad study of economics, since every good and transaction can be accurately tracked. Therefore, models that predict goods' circulation can be tested and confirmed before their introduction to "real life" and other scenarios. The present study is focused on the characteristics of early-stage adopters for virtual goods, and how they predict the lifespan of the goods. We employ machine learning and decision trees as the basis of our prediction models. Results provide evidence that the prediction of the lifespan of virtual objects is possible based just on data from early holders of those objects. Overall, communication and social activity are the main drivers for the effective propagation of virtual goods, and they are the most expected characteristics of early adopters.Comment: 28 page

Detection and Analysis of Anomalies in People Density and Mobility Through Wireless Smartphone Tracking

One of the challenges of this century is to use the data that a smart-city provides to make life easier for its inhabitants. Speci cally, within the area of urban mobility, the possibility of detecting anomalies in the movement of pedestrians and vehicles is an issue of vital importance for the planning and administration of a city. The aim of this paper is to propose a methodology to detect the movement of people from the information transmitted by their smart mobile devices, analyze these data, and be able to detect or recognize anomalies in their behavior. In order to validate this methodology, different experiments have been carried out based on real data aiming to extract knowledge, as well as obtaining a characterisation of the anomalies detected. The use of this methodology might help the city policy makers to better manage their mobility and transport resources.This work was supported by in part by the Dirección General de Tráfico under Project SPIP2017-02116, in part by the Ministerio de Ciencia, Innovación y Universidades under Grant RTI2018-102002-A-I00, in part by the Ministerio español de Economía y Competitividad under Grant TIN2017-85727-C4-2-P, in part by the FEDER under Grant TEC2015-68752, and in part by the FEDER y Junta de Andalucía under Project B-TIC-402-UGR18

Looking for Archetypes: Applying Game Data Mining to Hearthstone Decks

Digital Collectible Cards Games such as Hearthstone have become a very proli c test-bed for Arti cial Intelligence algorithms. The main researches have focused on the implementation of autonomous agents (bots) able to effectively play the game. However, this environment is also very attractive for the use of Data Mining (DM) and Machine Learning (ML) techniques, for analysing and extracting useful knowledge from game data. The objective of this work is to apply existing Game Mining techniques in order to study more than 600,000 real decks (groups of cards) created by players with many di erent skill levels. Data visualisation and analysis tools have been applied, namely, Graph representations and Clustering techniques. Then, an expert player has conducted a deep analysis of the results yielded by these methods, aiming to identify the use of standard - and well-known - archetypes de ned by the players. The used methods will also make it possible for the expert to discover hidden relationships between cards that could lead to nding better combinations of them, enhancing players' decks or, otherwise, identify unbalanced cards that could lead to a disappointing game experience. Moreover, although this work is mostly focused on data analysis and visualization, the obtained results can be applied to improve Hearthstone Bots' behaviour, e.g. predicting opponent's actions after identifying a speci c archetype in his/her deck.Spanish Government PID2020-113462RB-I00 PID2020-115570 GB-C22Junta de Andalucia B-TIC-402-UGR18 P18-RT-4830 A-TIC-608-UGR2

In-game action list segmentation and labeling in real-time strategy games

Data set available at http://ink.library.smu.edu.sg/data/1/</p

Примена виртуелних светова у истраживању теорије агената и инжењерском образовању

The focus of this doctoral dissertation is on exploring the potentials of virtual worlds, for applications in research and education. Regarding this, there are two central aspects that are explored in the dissertation. The first one considers the concept of autonomous agents, and agent theory in general, in the context of virtual worlds. The second aspect is related to the educational applications of virtual worlds, while especially focusing on the concept of virtual laboratories. An introduction to basic terminology related to the subject is given at the start of the dissertation. After that, a thorough analysis of the role of agents in virtual worlds is presented. This, among others, includes the analysis of the techniques that shape the agent’s behavior. The development of the virtual gamified educational system, specially dedicated to agents is then presented in the dissertation, along with a thorough description. While, in the end, analysis of the concept of virtual laboratories in STE (Science, Technology, and Engineering) disciplines is performed, and existing solutions are evaluated according to the criteria defined in the dissertation.Фокус ове докторске дисертације је на истраживању потенцијала виртуелних светова за примене у истраживањима и образовању. У вези са тим, постоје два главна аспекта која су обрађена у дисертацији. Први аспект се тиче концепта аутономних агената, као и теорије агената у целини, а у контексту виртуелних светова. Други аспект је везан за примену виртуелних светова у образовању, при чему је посебан акценат стављен на виртуелне лабораторије. На почетку дисертације је дат кратак увод који се тиче терминологије и појединих појмова везаних за област којом се ова дисертција бави. Након тога је представљена систематична и темељна анализа улоге агената у виртуелним световима. Између осталог, ово укључује и анализу техника потребних за обликовање понашања агената. Потом је у дисертацији детаљно представљен развој оригиналног виртуелног образовног система посвећеног агентима. На крају, анализиран је концепт виртуелних лабораторија у НТИ (наука, технологија, инжењерство) дисциплинама и извршена је евалуација постојећих решења у складу са критеријумима који су дефинисани у дисертацији

Rogueinabox: a Rogue environment for AI learning. Framework development and Agents design.

In this thesis we introduce Rogueinabox: a higly modular learning environment built around the videogame Rogue, the father of the roguelike genre. It offers easy ways to interact with the game and a whole framework to build, customize and run learning agents. We discuss the interest and challengies of this game for machine learning and deep learning, and discuss our initial experiments of training. We show the userfulness and convenience of Rogueinabox employing it in combination with QLearning tecniques to build an agent that explores the dungeon