This document discusses using video games for scientific purposes. It describes how video game controllers like the Wii remote and Kinect have been used for robot control, pattern recognition, and other scientific applications. It also discusses how video games incorporate scientific principles like physics and how artificial intelligence is an important area of research for game development. Some specific examples of research include evolving bot AI in Unreal Tournament using genetic algorithms and using games like Pac-Man and StarCraft for AI research challenges.
محاضرات متقدمة تدرس لطلاب حاسبات بنى سويف السنة الثالثة لتنمية قدراتهم البحثية وهذة الموضوعات تدرس على مستوى الدكتوراة - - نريد تميز طلاب حاسبات ليتميزو فى البحث العلمى -
The DENCLUE algorithm employs a cluster model based on kernel density estimation. A cluster is defined by a local maximum of the estimated density function. Observations going to the same local maximum are put into the same cluster. Clearly, DENCLUE doesn't work on data with uniform distribution.
An introduction to Swarm Intelligence, the most popular algorithms used and the applications of swarm intelligence.
This presentation talks about the Ant Colony Optimization and the Particle Swarm Optimization, while mentioning the other algorithms used.
Association genetics‟ or ‟association studies,” or ‟linkage disequilibrium mapping”.
Tool to resolve complex trait variation down to the sequence level by exploiting historical and evolutionary recombination events at the population level.
Natural population surveyed to determine MTA using LD.
Swarm intelligence is a modern artificial intelligence discipline that is concerned with the design of multiagent systems with applications, e.g., in optimization and in robotics. The design paradigm for these systems is fundamentally different from more traditional approaches.
Data Science - Part XIV - Genetic AlgorithmsDerek Kane
This lecture provides an overview on biological evolution and genetic algorithms in a machine learning context. We will start off by going through a broad overview of the biological evolutionary process and then explore how genetic algorithms can be developed that mimic these processes. We will dive into the types of problems that can be solved with genetic algorithms and then we will conclude with a series of practical examples in R which highlights the techniques: The Knapsack Problem, Feature Selection and OLS regression, and constrained optimizations.
Presentación de la charla impartida durante el pasado Curso de Verano del Centro Mediterráneo de Almuñécar, titulado "Animación y Videojuegos".
En la charla se habla de diversos aspectos que relacionan ciencia y videojuegos, desde las posibilidades de los sistemas de juego en relación a la ciencia, hasta la investigación científica en el campo de los videojuegos.
محاضرات متقدمة تدرس لطلاب حاسبات بنى سويف السنة الثالثة لتنمية قدراتهم البحثية وهذة الموضوعات تدرس على مستوى الدكتوراة - - نريد تميز طلاب حاسبات ليتميزو فى البحث العلمى -
The DENCLUE algorithm employs a cluster model based on kernel density estimation. A cluster is defined by a local maximum of the estimated density function. Observations going to the same local maximum are put into the same cluster. Clearly, DENCLUE doesn't work on data with uniform distribution.
An introduction to Swarm Intelligence, the most popular algorithms used and the applications of swarm intelligence.
This presentation talks about the Ant Colony Optimization and the Particle Swarm Optimization, while mentioning the other algorithms used.
Association genetics‟ or ‟association studies,” or ‟linkage disequilibrium mapping”.
Tool to resolve complex trait variation down to the sequence level by exploiting historical and evolutionary recombination events at the population level.
Natural population surveyed to determine MTA using LD.
Swarm intelligence is a modern artificial intelligence discipline that is concerned with the design of multiagent systems with applications, e.g., in optimization and in robotics. The design paradigm for these systems is fundamentally different from more traditional approaches.
Data Science - Part XIV - Genetic AlgorithmsDerek Kane
This lecture provides an overview on biological evolution and genetic algorithms in a machine learning context. We will start off by going through a broad overview of the biological evolutionary process and then explore how genetic algorithms can be developed that mimic these processes. We will dive into the types of problems that can be solved with genetic algorithms and then we will conclude with a series of practical examples in R which highlights the techniques: The Knapsack Problem, Feature Selection and OLS regression, and constrained optimizations.
Presentación de la charla impartida durante el pasado Curso de Verano del Centro Mediterráneo de Almuñécar, titulado "Animación y Videojuegos".
En la charla se habla de diversos aspectos que relacionan ciencia y videojuegos, desde las posibilidades de los sistemas de juego en relación a la ciencia, hasta la investigación científica en el campo de los videojuegos.
Super Mario (El Personaje y las Mario AI Competitions)Antonio Mora
Esta es una introducción en español al personaje de Super Mario y sus juegos, así como unos comentarios acerca de las competiciones relacionadas con temas de Inteligencia Artificial que se realizan en el ámbito científico.
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This is an introduction (in spanish) to the Super Mario character and his games,in addition to some comments related to the scientific Mario AI Competitions, devoted to the development of Gameplay, Level Generation and Learning agents.
Evolving the Cooperative Behaviour in Unreal BotsAntonio Mora
This work presents a research of the improvement of the Team AI in Unreal Tournament Bots by means of a Genetic Algorithm, which evolves the set of parameters that determines the behaviour of a bot inside a team.
Presented at IEEE Computer Intelligence and Games (CIG 2010). IT University of Copenhagen, Denmark.
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Este trabajo presenta la investigación llevada a cabo para mejorar la IA de los Bots en Unreal Tournament cuando éstos están dentro de un equipo.
Para ello se ha utilizado un Algoritmo Genético que evoluciona el conjunto de parámetros de los que depende el comportamiento de un Bot dentro de un equipo.
Presentada en el IEEE CIG 2010 (ITU, Copenhague, Dinamarca).
Designing and Evolving an Unreal Tournament 2004 Expert BotAntonio Mora
This presentation describes the design of a bot for the first person shooter Unreal Tournament 2004, which behaves as a human expert player in 1 vs. 1 death matches. This has been implemented modelling the actions (and tricks) of this player, using a state-based AI, and supplemented by a database for ‘learning’ the arena. The expert bot yields excellent results, beating the game default bots in the hardest difficulty, and even being a very hard opponent for the human players (including our expert). The AI of this bot is then improved by means of three different approaches of evolutionary algorithms, optimizing a wide set of parameters (weights and probabilities) which the expert bot considers when playing. The result of this process yields an even better rival; however the noisy nature of the fitness function (due to the pseudostochasticity of the battles) makes the evolution slower than usual.
FSM-Based Agents for Playing Super Mario GameAntonio Mora
Presentation of the work "Evolutionary FSM-Based Agents for Playing Super Mario Game" at LION 2013 (LION 7).
by Rosa Hidalgo, María Sandra Rodríguez, Antonio M. Mora,
Pablo García, Juan Julián Merelo y Antonio J. Fernández
Conference
http://www.intelligent-optimization.org/LION7/
TESIS: "Resolución de Problema Militar de Búsqueda de Camino Óptimo Multiobje...Antonio Mora
Presentación en español de la Tesis de A.M. Mora.
Titulada "Resolución de Problema Militar de Búsqueda de Camino Óptimo Multiobjetivo mediante el uso de Algoritmos de Optimización basados en Colonias de Hormigas"
Presentada el 5 de Mayo de 2009.
Optimización basada en colonias de hormigas. Conceptos principalesAntonio Mora
Breve introducción a la metaheurística inspirada en el comportamiento de las hormigas naturales para la resolución de problemas de optimización y búsqueda de caminos.
From Paddles To Pads: is controller design killing creativity in videogames?infovore
A talk I delivered at the O'Reilly Emerging Technology Conference in 2006, on how the devices we play games with affects the games we can play (amongst other things).
bueno mi primer ensayo y es claro sobre un tema muy conocido y sobre valorado como los mismo videojuegos para mas informacion https://www.facebook.com/andersonor
Super Mario 63 Genial Super Mario Bross juego donde vuelve mario bros, tienes diferentes aventuras con pantallas muy buenas. De los mejores juegos de Mario 63.
Understanding and improving games through machine learning - Natasha LatyshevaLauren Cormack
Data Scientist at Jagex - Jagex has a diverse games portfolio, including a large MMORPG (RuneScape), a tactical FPS (Block N Load) and a collectible card game (Chronicle). In this presentation, Natasha Latysheva will showcase recent and upcoming data science and machine learning projects at Jagex – including quest recommender systems, player clustering by playstyle, deep learning player lifecycle sequences, and automatic bot and abuse detection – to give a taster of the insights that a machine learning approach can provide, whilst also offering project ideas for your own games.
When the Heart BD2K grant was originally written. We proposed to build something called “Big Data World” to help advance citizen science, scientific crowdsourcing and science education – especially in bioinformatics. This past year, this idea has become Science Game Lab ( https://sciencegamelab.org ) . A collaboration between the Su laboratory at Scripps Research, Playmatics LLC, and recently the creators of WikiPathways.
Study on Genetic Algorithm Approaches to Improve an Autonomous Agent for a Fi...Antonio Mora
Work presented at EVO* 2022 conference as a Late-Breaking Abstract.
ABSTRACT:
A fighting game is a 1 vs 1 confrontation in which the players (characters) try to reduce the health bar of the opponent by punching or kicking him. Players
can normally also do special movements, which are more complicated to execute,
but which can reduce the rival's health in a higher amount. Articial Intelligence (AI) engines in this type of games have a big handicap in comparison to other genres, since they must yield very fast decisions, given the highly dynamic rival movements.
In this paper, we have created a Non-Player Character (or bot) aiming to beat to any opponent (being a human player or another NPC). To this end, we have started from a competitive bot (from the state of the art) having as AI engine a set of rules, depending on some conditions, threshold and weights.
Then we have optimized these values by means of different schemes of Genetic Algorithms (GAs).
Optimización Adaptativa basada en Colonias de Hormigas para la Composición de...Antonio Mora
Las redes 5G dependen en gran medida de la gestión y el procesamiento basados en software. Las redes
definidas por software (SDN) y la virtualización de funciones de red (NFV) forman parte del núcleo de estas. Los servicios ofrecidos dentro de este entorno se componen de varias funciones de red virtuales (VNF) que deben ejecutarse en un orden (normalmente) estricto. Esto se conoce como Service Function Chaining (SFC) y, dado que esas VNFs podrían estar ubicadas en diferentes nodos a lo largo de la red, además de la baja latencia esperada en el procesamiento de los servicios 5G, hace que el SFC sea un problema de optimización difícil de resolver. En un trabajo anterior, los autores presentaron un algoritmo de Optimización de
Colonias de Hormigas (ACO) para la minimización del coste de enrutamiento de la composici´on de la cadena de servicios, tratándose de una aproximación preliminar capaz de resolver instancias simples y 'estáticas'; es decir, aquellas en las que la topología de la red permanece invariable durante la resolución. Esto dista mucho de la situación real de las redes, en las que normalmente los nodos (y los enlaces)
aparecen y desaparecen continuamente. Así, en este trabajo describimos una evolución de nuestra propuesta anterior, que considera un modelo dinámico del problema, más cercano al escenario real. De manera que, en las instancias, los nodos y enlaces pueden ser eliminados o activados repentinamente.
El algoritmo ACO será capaz de adaptarse a estos cambios y seguir ofreciendo soluciones óptimas. Dicho método ha sido probado en tres instancias dinámicas de diferentes tamaños, obteniendo resultados muy prometedores.
Adaptive Ant Colony Optimization for Service Function Chaining in a Dynamic 5...Antonio Mora
Paper published at the last International Work Conference on Artificial Neural Networks (IWANN 2021).
ABSTRACT:
5G Networks are strongly dependent on software-based management and processing. Services offered inside this environment are composed of several Virtual Network Functions (VNFs) that must be executed in a (normally) strict order. This is known as Service Function Chaining (SFC) and, given that those VNFs could be placed in different nodes along the network together with the expected low latency in the processing of 5G services, makes SFC a tough optimization problem.
In a previous work, the authors presented an Ant Colony Optimization (ACO) algorithm for the minimization of the routing cost of service chain composition, but it was a preliminary approach able to solve simple and 'static' instances (i.e. network topology is invariable). Thus, in this work we describe an evolution of our previous proposal, which consider a dynamic model of the problem, closer to the real scenario. So, in the instances nodes and links can be removed suddenly or, on the
contrary, they could arise. The ACO algorithm will be able to adapt to these changes and still yield optimal solutions. The Adaptive Ant-SFC method has been tested in three dynamic instances with different sizes, obtaining very promising results.
Research in Videogames. (Much) further than just AIAntonio Mora
Videogames have become one of the most prolific and interesting testbed for research in the last few years; mainly due to their growing impact on both, the society and in the scientific community. Thus, there are arising many tools and frameworks aimed to ease the implementation and test of effective Artificial Intelligence engines, able to control challenging or human-like autonomous agents (NPCs, Non-Player Characters). However, many of these engines are still implemented using basic techniques and just a few of them apply advanced Computational Intelligence methods. Moreover, videogames are themselves a varied domain with many other problems to address, that researchers are becoming to study.
This talk will introduce videogames as a research domain and will present the main problems to solve as well as the main CI techniques applied in this scope.
GRETIVE: Un Bot Evolutivo para HearthStone basado en PerfilesAntonio Mora
Artículo presentado en el reciente CoSECiVi 2020, celebrado online el 7 y 8 de octubre de 2020.
RESUMEN DEL TRABAJO:
En los últimos años, la competición internacional de IA en Hearthstone ha aumentado exponencialmente su fama entre la comunidad científica, que ha participado de forma activa con múltiples agentes. Uno de los mejores, EVA, aplicaba un enfoque greedy combinado con un algoritmo evolutivo (AE). Sin embargo, casi todas las propuestas (incluida EVA) fueron diseñadas para funcionar de manera generalista, es decir, para cualquiera de los posibles héroes del juego. Esta generalización presenta una gran carencia, ya que no se explotan las cartas exclusivas de cada héroe, así como sus posibles perfiles de comportamiento diferentes. Este artículo sigue una filosofía similar a EVA, también híbrida (Greedy + AE), pero teniendo en cuenta tres arquetipos o perfiles muy extendidos entre la comunidad de jugadores: Aggro (ofensivo), Control (defensivo) y Midrange (intermedio). De forma que, en esencia, se han optimizado tres comportamientos diferentes con el objetivo de crear un agente más especializado capaz de usar un motor de comportamiento diferente dependiendo del héroe con el que juegue. Para demostrar la valía de este enfoque, se han llevado a cabo varios experimentos, comparando los agentes evolucionados con EVA en muchas partidas diferentes, usando tres héroes distintos. Los resultados muestran una mejora sobre EVA para los tres agentes basados en perfil, y un gran rendimiento en general frente a otras propuestas menos competitivas.
*** Presentación creada por Alejandro Romero ***
Improving the Performance of MCTS-Based μRTS Agents Through Move PruningAntonio Mora
Paper published in the past 2020 IEEE Conference on Games.
The abstract of the paper is:
The impressive performance of Monte Carlo Tree Search (MCTS) based game-playing agents in high branching factor
domains, such as Go, motivated researchers to apply and adapt MCTS to even more challenging domains. Real-time
strategy (RTS) games feature a large combinatorial branching
factor, and a real-time aspect that pose significant challenges to a broad spectrum of AI techniques, including MCTS. Various
MCTS enhancements were proposed such as the combinatorial multi-armed bandit (CMAB) based sampling, state/action abstractions and machine learning. In this paper, we propose to apply move pruning to MCTS-based agents, in the context of RTS games. We describe a class of possibly detrimental playeractions, and elaborate a number of pruning approaches, targeting this specific class. The experimentation results in mRTS indicate that this could be a promising direction.
Testing hybrid computational intelligence algorithms for general game playing...Antonio Mora
Work presented in EvoAPPs 2020, included the Special Session "Soft Computing applied to Games".
Granted with the *** BEST PAPER AWARD *** of the Conference. :D
The abstract is:
General Videogame Playing is one of the hottest topics in
the research field of AI in videogames. It aims at the implementation of algorithms or autonomous agents able to play a set of unknown games efficiently, just receiving the set of rules to play in real time. Thus, this work presents the implementation of eight approaches based on the main techniques applied in the literature to face this problem, including two different hybrid implementations combining Montecarlo Tree Search and
Genetic Algorithms. They have been created within the General Video Game Artificial Intelligence (GVGAI) Competition platform. Then, the algorithms have been tested in a set of 20 games from that competition, analyzing its performance. According to the obtained results, we can conclude that the proposed hybrid approaches are the best approaches, and they would be a very competitive entry for the competition.
Applying Ant Colony Optimization for Service Function Chaining in a 5G Networ...Antonio Mora
This is the presentation of the paper with the same title presented today (22 October 2019) in Granada, on the "6th IEEE International Conference on Internet of Things: Systems, Management and Security (IOTSMS 2019)", and in this, inside the Workshop "The International Workshop on Efficient and Smart 5G Technologies for IoT (ES5TI)".
Investigación en videojuegos. (mucho) Mas allá de la IAAntonio Mora
En esta presentación cuento las posibilidades de investigación que hay para ir más allá de los motores de IA básicos o avanzados utilizados en videojuegos.
Además, presento algunas otras líneas que se están investigando en todo el mundo dentro del ámbito de los juegos.
Charla impartida dentro de la Madrid Games Week 2019 (foro de desarrolladores).
Beating uncertainty in racing bot evolution through enhanced exploration and ...Antonio Mora
Presentation of the paper published in IEEE Conference on Games 2019.
ABSTRACT:
One of the main problems in the design throughoptimization of car racing bots is the inherent noise in the optimization process: besides the fact that the fitness is a heuristic based on what we think are the keys to success and as such
just a surrogate for the ultimate objective, winning races, fitness itself is uncertain due to the stochastic behavior of racing conditions and the rest of the (simulated) racers. The fuzzy-based genetic controller for the car racing simulator TORCS that we have defined in previous works is based on two fuzzy subcontrollers, one for deciding on the wheel steering angle and another to set the car target speed at the next simulation tick.
They are both optimized by means of an Evolutionary Algorithm, which considers an already tested fitness function focused on the maximization of the average speed during the race and the minimization of the car damage. The noisy environment asks for keeping diversity high during evolution, that is why we have added a Blend Crossover (BLX-alpha) operator, which is, besides, able to exploit current results at the same time it explores. Additionally, we try to address uncertainty in selection by introducing a novel selection policy of parents based in races, where the individuals are grouped and compete against others in several races, so just the firsts ranked will remain in the population as parents. Several experiments have been conducted, testing the value of the different controllers. The results show that the combination of a dynamic BLX-alpha crossover operator plus the pole position selection policy clearly beats the rest of approaches. Moreover, in the comparison of this controller with one of the participants of the prestigious international Simulated Car Racing Championship, our autonomous driver obtains much better results than the opponent.
Inteligencia Computacional en Videojuegos (Meetup GranadAI 2019)Antonio Mora
En esta presentación se describen conceptos principales de Inteligencia Artificial (IA) e Inteligencia Computacional (IC) en videojuegos, así como las principales técnicas de IA utilizadas en videojuegos (incluso en muchos comerciales).
Igualmente se presentan algunas líneas de investigación relacionadas con videojuegos en las que se aplica IC, y se muestran varios ejemplos desarrollados dentro del grupo de investigación en el que trabaja Antonio Mora (Grupo GeNeura, de la Universidad de Granada).
* Charla realizada en el Meetup del grupo GranadAI del día 1 de julio de 2019. *
Free Form Evolution for Angry Birds Level GenerationAntonio Mora
Short presentation of the paper published in EVO* 2019 (EvoAPPS 2019).
ABSTRACT:
This paper presents an original approach for building structures that are stable under gravity for the physics-based puzzle game Angry Birds, with the ultimate objective of creating levels with the minimum number of constraints. This approach consists of a search-based procedural level generation method that uses evolutionary algorithms.
In order to evaluate the stability of the levels, they are executed in an adaptation of an open source version of the game called Science Birds.
In the same way, an open source evolutionary computation framework has been implemented to fit the requirements of the problem. The main challenge has been to design a fitness function that, first, avoids if possible the actual execution of the simulator, which is time consuming, and, then, to take into account the different ways in which a structure is not structurally sound and consider them in different ways to provide a smooth landscape that eventually achieves that soundness. Different representations and operators have been considered and studied. In order to test the method four experiments have been carried out, obtaining a variety of stable structures, which is the first path for the generation of levels that are aesthetically pleasing as well as playable.
The Evolutionary Race: Improving the Process of Evaluating Car Controllers in...Antonio Mora
Presentation shown at IEEE Conference on Computational Intelligence and Games 2018, held between 13 and 17 of August in Maastricht (The Netherlands).
The ABSTRACT of the work is:
Simulated car races have been used for a long time as an environment where car controlling algorithms can be tested; they are an interesting testbed for all kinds of algorithms,
including metaheuristics such as evolutionary algorithms. However, the challenge in the evolutionary algorithms is to design a reliable and effective evaluation process for the individuals that eventually translates into good solutions to the car racing problem: finding a controller that is able to win in a wide range of tracks and with a good quantity of opponents. Evaluating individual car controllers involves not only the design of a proper fitness function representing how good the car controller would be in a competitive race, but also the selection of the best solution for the optimization problem being solved; this decision might not be easy when uncertainty is present in the problem environment; in this case, weather and track conditions as well as unpredictable behavior of other drivers. Creating a methodology for the automatic design of the controller of an autonomous driver for a car racing simulator such as TORCS is an optimization problem which offers all these challenges. Thus, in this paper we describe an analysis and some proposals to improve the evaluation of optimized fuzzy drivers for TORCS over previous attempts to do so. It builds on preliminary results obtained in previous papers as a baseline and aims to obtain a more competitive autonomous driver via redesign of the fitness evaluation procedure; to this end, two different fitness functions are studied in several experiments, along with a novel race-based approach for the selection of the best individual in the evolution.
Predicción de Quiebra Financiera de Empresas Mediante Equilibrado de Datos y ...Antonio Mora
Póster presentado en las Terceras Jornadas Andaluzas de Informática, celebradas en septiembre de 2017 en Canillas de Aceituno (Málaga, España).
El resumen del mismo es:
La predicción de situaciones financieras adversas en empresas, como la temida quiebra o bancarrota es un problema que se ha venido estudiando desde hace varias d´ecadas con un éxito relativo. En este trabajo se muestra una propuesta efectiva
para anticipar esta situación considerando un conjunto de datos real sobre empresas españolas. Dicho conjunto se encuentra
claramente desequilibrado en lo relativo al n´umero de patrones
que no presentan bancarrota respecto a los que sí la presentan.
Esto afecta dramáticamente a los modelos de clasificación y a la fiabilidad de sus resultados, por lo aquí se propone la aplicación de técnicas de equilibrado (o balanceo) para paliar estos efectos.
Sobre los datos corregidos, en distintas aproximaciones, se aplican y comparan, utilizando diversas métricas, los clasificadores J48, Random Forest y Naïve Bayes, como base comparativa. De acuerdo a los cuatro experimentos realizados, Random Forest proporciona los mejores resultados, siendo éstos bastante fiables.
Driving in TORCS using modular fuzzy controllers - Poster - EvoGAMES 2017Antonio Mora
Poster for presenting the paper at EvoGAMES 2017 in Amsterdam.
The abstract of the paper is:
"When driving a car it is essential to take into account all possible factors; even more so when, like in the TORCS simulated race game, the objective is not only to avoid collisions, but also to win the race within a limited budget. In this paper, we present the design of an autonomous driver for racing car in a simulated race. Unlike previous controllers, that only used fuzzy logic approaches for either acceleration or steering, the proposed driver uses simultaneously two fuzzy controllers for steering and computing the target speed of the car at every moment of the race. They use the track border sensors as inputs and besides, for enhanced safety, it has also taken into account the relative position of the other competitors. The proposed fuzzy driver is evaluated in practise and timed races giving good results across a wide variety of racing tracks, mainly those that have many turning points."
Sólo puede quedar uno: Evolución de Bots para RTS basada en supervivenciaAntonio Mora
Artículo presentado en el 3er Congreso de la Sociedad Española para las Ciencias del Videojuego (CoSECiVi'16).
RESUMEN:
Este artículo propone un algoritmo evolutivo para optimizar el comportamiento de bots (NPCs) que no requiere de una función de fitness explícita,
usando en su lugar combates por pares (a modo de "justa") en los que sólo uno
de los contendientes sobrevivirá. Este proceso hará las veces de mecanismo de
selección del algoritmo, en el que sólo los ganadores pasarán a la siguiente generación del mismo. Se ha utilizado un algoritmo de Programación Genética,
diseñado para generar motores de comportamiento para bots del conocido RTS
Planet Wars. Este método tiene dos objetivos principales: en primer lugar, paliar
el efecto que la naturaleza "ruidosa" de la función de fitness añade a la evaluación
y, en segundo lugar, generar bots más generales (menos especializados) que los
que se obtienen mediante algoritmos evolutivos en los que se usa siempre un contendiente común para evaluar los individuos. Más aún, la omisión de un proceso
de evaluación explícito reduce el número de combates necesarios para evolucionar, lo que reduce a su vez el tiempo de cómputo del algoritmo. Los resultados
demuestran que el método converge y que es menos sensible al ruido que otros
métodos más tradicionales. Además de esto, con este algoritmo se obtienen bots
muy competitivos en comparación con otros bots de la literatura.
Living-UGR: Una aventura gráfica geolocalizada para difundir el patrimonio de...Antonio Mora
Artículo presentado en el 3er Congreso de la Sociedad Española para las Ciencias del Videojuego (CoSECiVi 2016).
RESUMEN:
El presente artículo describe una aplicación para dispositivos móviles desarrollada en el marco de un proyecto de investigación cuyo objetivo es fomentar las visitas al patrimonio, tanto cultural como académico y científico, de la Universidad de Granada (España). Dicha aplicación
se ha planteado como un juego serio que hace uso de mecanismos de geolocalización para ofrecer una experiencia gamificada que guiará la visita del usuario a través de varios centros del complejo universitario. El juego se desarrolla como una aventura gráca en la que el jugador/usuario es
protagonista, y plantea una gran diversidad de desafíos que combinan aspectos físicos e intelectuales para incentivar al jugador a recopilar las "piezas" distribuidas por los distintos edificios con el n último de dar vida (o no) a un nuevo Frankenstein. Actualmente se dispone de un prototipo
de la aplicación/juego que será probado próximamente en una experiencia organizada con varios grupos que competirán para crear el Frankenstein en menos tiempo. Esta experiencia permitirá analizar la efectividad del juego en el objetivo con el que fue diseñado: motivar a locales y turistas a visitar el patrimonio de la Universidad de Granada y adquirir conocimientos sobre el mismo.
Gamification in Teaching - How to motivate students through gamesAntonio Mora
This presentation introduces some concepts about gamification and some of its typical applications. In addition, the slides describe some approaches for applying this technique to different levels of academic education.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
Thinking of getting a dog? Be aware that breeds like Pit Bulls, Rottweilers, and German Shepherds can be loyal and dangerous. Proper training and socialization are crucial to preventing aggressive behaviors. Ensure safety by understanding their needs and always supervising interactions. Stay safe, and enjoy your furry friends!
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
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2. • Introduction
videogames market, players taxonomy,
current game systems. Videogames at the
University.
• Videogames applied to science
videogame system-based science, engineering
and technology.
• Science in videogames
scientific principles of videogames.
• Researching in videogames
main research fields in videogames.
• Examples: Our works
4. • Very big growing of videogames market, due to
their movement to new groups of interest:
people older than 25 and younger than 10,
including parents and grandparents, in
addition to the feminine sector.
• This growing is mainly due to a change in the
videogames philosophy, offering more ‘adult’
contents, or the contrary, easier and child-
focused, in addition to direct and brief action
games.
There is a wide market for science!!!
5. • In the current market has arisen the so-called casual gamers:
sporadic players users of brief and direct action games (arcade, sports, mini-
games), or the so-called no-games (training games, art games, and so on).
• The usual players have auto renamed as hardcore gamers.
They (really) enjoy and profit the games, they are informed, like most types of
games, and play for long periods (if possible).
A friendly way to say “Virgin until the age of 37” More fun if they plug-in the console
7. • In addition to PC and mobile systems (iOS, Android, etc), there are some
extended systems:
• Home consoles
Wii Xbox 360 Playstation 3
• Portable consoles
Nintendo Playstation
3DS Vita
8. • And many more (open-philosphy consoles), not known for most of the people.
GP2X Wiz Caanoo
Dingoo Pandora
9. • Another (positive) consequence is the adaptation of the study plans
for videogames development.
• In Spain there are arising courses in Grades and Masters
• Anyway, we are still far from other countries in Europe:
– Example: Center for Computer Game Research (Copenhagen)
12. Its (at the beginning) novel controller (Wiimote) has been very famous
mainly among the scientific and technical community:
• Robots control, reactive/touch-detecting screens or surfaces, or
pattern/subjects recognition, among others.
http://www.youtube.com/watch?v=v1AJ_OBJUpY http://www.youtube.com/watch?v=TkmxhVtvLoM http://www.youtube.com/watch?v=0awjPUkBXOU
13. There is even a project, WiiLab, which has created a Matlab toolbox for
interacting with Wiimote…
…and with Game Maker (an easy
game development framework)
http://code.google.com/p/giimote/
http://www.youtube.com/watch?v=EeBAYeoX7-8
14. • It was initially used for building
console-based clusters (super
computers), due to the powerful
chip Cell, and the cheap price it
had.
• It was possible to use an
additional Linux O.S. (Yellow
Dog), very flexible.
• But later, the console was
updated for not admitting the
installation of any additional
O.S., so Linux was lost forever.
15. Very famous in the community due to Kinect:
• Robots control by movement and/or voice, pattern and person
recognition, among others.
http://www.youtube.com/watch?v=Sw4RvwhQ73E http://www.youtube.com/watch?v=c6jZjpvIio4
18. • Videogames have always respected physics rules, even a ‘simple’ one
(in appearance) such as Super Mario Bros. (jumps, trajectories,
inertia,…).
• Nowadays the tendency is to develop completely realistic games in
that sense, by implementing specific engines for physics modeling.
http://www.youtube.com/watch?v=B7_rPDwSKe8
19. The scientific principles of operation of the first main controller for
a console based in movement (Wiimote) are:
http://www.youtube.com/watch?v=ETAKfSkec6A
21. • In addition to visual and physics realism, it is desired to model enemies and
partners , with an ‘intelligent’ (human) behavior.
• Thus a big amount of resources have been focused on artificial intelligence.
Realistic Game
22. • AI is the area of computer science devoted to implement
nonliving rational agents (at least in appearance).
• Inside a videogame, AI is focused on defining behavior
techniques for non-playable characters (NPCs), commonly
named bots, which simulate being rational. These characters
could be enemies or partners.
• It is not a matter of literally showing human behavior, since it
means the consideration of mistakes.
23. • In the very beginning, NPCs followed some
predefined behavior patterns, that the
programmer implemented at game
implementation and which were invariable.
• Reactive AIs proposed NPC’s actions as a
response to player’s actions.
• Dedicated AIs set different ‘personalities’
for NPCs.
24. • Later there were introduced the finite state machines, which
define a set of possible states for the NPC, and a set of
transitions between them. Transitions are based in
perceptions about the game or about the players.
By Fergu
25. • Other extended methods include rule-based systems and
decision trees. In both cases, there is a set of rules that the
NPC will follow, depending on the inputs or perceptions about
its environment.
26. • Nowadays it is usual to mix some of these techniques, thus in most games
NPCs follow predefined behavioral models (scripts), depending on player’s
actions.
• Their advantage is that it is easy to define them, considering programmer’s
experience and modeling player’s behavior.
• Their main disadvantage is the low flexibility they have in order to adapt to
new situations/events.
• Moreover, NPCs have additional advantages over the human player, such as
perfect aim (based in exact coordinates), or navigation points (waypoints in
maps modeling advantageous routes, shortest paths, etc).
• Just a few ‘scientific techniques’ have been used in commercial games…
27. • Traditionally in the scientific area it was called Game Theory, a
branch of applied mathematics in which there are some rewards
depending on the chosen decision. It involved simple, but difficult
to solve, games: Hanoi towers, prisoner’s dilemma, game of life.
• These games proposed problems to be solved by means of exact
methods, heuristics or metaheuristics: tree-based search, A*,
evolutionary algorithms, ant colony optimization,…
• Moreover, the resolution of traditional games (usually puzzles) has
also been studied from the ‘ancient times’ in science life: chess,
backgammon, mastermind, sudoku
28. • videogames provide a new environment for solving heterogeneous
problems.
• The most famous (and probably the first) problem addressed was AI
related issues. It still remains as the main (the most studied) problem
in the area.
• However, with the advances and improvement of technology,
videogames have increased their complexity, so new researching lines
have been arisen:
– Search in maps, combat prediction, or simulation, to cite a few
• Nowadays, there are a huge number of research fields inside
videogames scope, so research studies and publications have grown
exponentially.
29. • AI branch which applies metaheuristics and bioinspired
methods for the resolution of complex problems, usually by
means of adaptive systems.
• It is necessary to model the game (or a part of it) as an
optimization, search or learning problem, among others.
• Examples:
– Pathfinding
– Combat prediction
– Automated generation of behavioral rules
– Parameter tuning
– Objective decision
30. • The most used metaheuristics are: Genetic Algorithms (GA),
Ant Colony Optimization (ACO), Monte-Carlo Tree Search
(MCTS), A*, Genetic Programming (GP), Fuzzy Logic, Neural
Networks…
• Which are mostly applied over finite state machines (FSM),
scripts, rule-based systems (RS) or expert system (ES), among
others.
31. • NPC’s AI: try to model AI aspects for enemies
or partners. It is usual to apply GAs to
optimize parameters considered in behavioral
rules.
• Rule system generation: automated definition
of behavioral rules sets, which determine the
way the NPCs act in different situations. It is
usual to apply GP.
• Human-like behavior analysis and modeling: the
objective is to model NPCs which behave as
human players. Data mining and learning
techniques are usually employed.
32. • Cheating detection: trick detection
techniques, based on the study of statistics
about matches.
• Move and battle prediction: prediction
methods are trained (using neural networks)
analyzing data from recorded matches,
trying to anticipate future movements and
actions.
• Learning in games: adaptive agents can be
created by means of reinforcement learning.
33. • Game mechanics and features analysis: game
components are analyzed and parameterized in
order to get numeric valuations of the game
components.
• Exploration and search in games: search
algorithms are applied in order to find the best
paths to objectives in maps, or to explore some
areas maximizing the covering, for instance.
• Content, characters, levels and story generation:
is the so-called procedural content generation,
and is aimed to generate automatically contents.
They are valued by the players (interactive
methods) or by means of mathematical models.
35. i initial population
f evaluation
function (fitness)
? stop condition
Se selection
Cr crossover
Mu mutation
Re replacement
by Johann Dréo
37. Unreal is a first person shooter (FPS).
Famous due to the excelent AI of the enemies (bots), which makes it an
amazing multiplayer game. Unreal Tournament series is very well considered.
It offers an editor (UnrealEd) which lets us change almost anything in the game
even the behavior of the bots. It uses the language UnrealScript.
38. A java middleware for Unreal Tournament series games and
Defcon games.
The architecture is as follows:
It is possible to interact with the game from a java program, getting higher
independence (avoiding Unrealscript restrictions) and increasing the
Possibilities (java libraries).
On the contrary, the structures, classes, functions and workflows defined
in the Unreal engine, cannot be accessed, nor used.
39. • Analyze FSM
• Identify behavioral parameters
• Optimize them
Bot based in GA FITNESS EVALUATION
(GA-Bot)
population Std
AI Std Std
AI AI
Evolutionary
process
A.M. Mora et al.: Evolving bot AI in Unreal. EVO* 2010. LNCS 6024, Springer, pp. 170–179
40. • Analyze FSM
• Identify parameters devoted to
team performance
• Optimize them
Team of bots
based on GAs FITNESS EVALUATION
(GT-Bot) Std
Std
AI
AI
Std
AI
population
vs
Evolutionary
Process Or
A.M. Mora et al.: Evolving the cooperative behaviour in unreal bots. IEEE CIG 2010, pp. 241–248
41. • Define a FSM based in expert’s knowledge:
– Two state levels, Set of rules
• Optimize parameters by means of a GA
42. • Examples of NPCs/Bots/Agents:
http://www.youtube.com/watch?v=EiAWYGNpu9M http://www.youtube.com/watch?v=0Khtp2tEU1k
43. A good way to start working:
• 2K BotPrize: Unreal bots which should behave as human as possible.
• Starcraft: combats inside the famous RTS.
• Planet Wars: simpler RTS game. Google AI Challenge 2010.
• ANTS: RTS modeling ant’s fighting. Google AI Challenge 2011.
• Pac-Man: It can be implemented pac-man’s or ghosts intelligence.
• Simulated Car Racing: Car races, track generation, mechanical
optimization.
• Mario AI: Agent, learning, level generation.
http://geneura.ugr.es/cig2012/competitions.html
44. • Conferences:
– IEEE CIG
– CGAMES
– GAME-ON
– CGAT
– Special Sessions:
LION, IWANN, EVO*, GECCO, WCCI
• Journals:
– Transactions on Computational
Intelligence and AI in Games (IEEE)
– Entertainment Computing (Springer)
…
– Anyone which accept your paper