Study on Genetic Algorithm Approaches to Improve an Autonomous Agent for a Fighting Game
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Work presented at EVO* 2022 conference as a Late-Breaking Abstract. ABSTRACT: A fi ghting 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. Arti cial 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).
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• GA – Mutation
• Research PaperThesis_Presentation
The document outlines a presentation on evolutionary algorithms and their performance on optimization problems. It introduces evolutionary algorithms and describes classical evolutionary programming (CEP), fast evolutionary programming (FEP), and improved fast evolutionary programming (IFEP). It then discusses benchmark functions used to evaluate the algorithms' performance, including sphere, Schwefel, Rastrigin, and Rosenbrock functions. Tables show the results of applying CEP, FEP, and IFEP to optimize 10 standard benchmark functions, finding that IFEP generally performs best.
Designing Games for Industrial Training.
Jessica Noriega, MD.
Manuel Arrieta, PhD.
educacion@ensuniversalis.com /
www.ensuniversalis.org
Florida Interactive Entertainment Academy
University of Central Florida, Orlando, Florida.
Our methodology brings together andragogic and ludic learning, and that is supported by emotional and rational processes which use emotional intelligence and thinking skills We have adopted a Gestaltic and Systematic methodology for that purpose, as well as principles from the existentialist humanist philosophy The use of all these tools allow us to create workshops and courses that meet the needs of both, the company and the employee.
Multi Plant Automobile Manufacturer GAME OBJECTIVE Each team must make the less amount of mistakes as a group and as a whole within the automobile plants role play This will be done by setting up the rules that will guide all the players throughout the game These instructions will be implemented at the beginning and meanwhile the game is in process, and all the teams should work under the same guidelines and conditions COMPANY OBJECTIVE: At the end of the game, all the teams will know the importance of team work, leadership, and the significance of working with internal teams, as well as external teams. WORKING TOOLS Lego cars 31040 working templets ( a whiteboard and one marker for each team One chronometer and one whistle for the trainer SETUP Each team is made up of three 3 to four 4 participants Every team most have a NAME or else, have a number, and all participants most play a role It is important to have at least four teams.
Similaire à Study on Genetic Algorithm Approaches to Improve an Autonomous Agent for a Fighting Game (20)
Introduction to Genetic Algorithms and Evolutionary Computation
Introduction to Genetic Algorithms and Evolutionary Computation
Beating uncertainty in racing bot evolution through enhanced exploration and ...
Beating uncertainty in racing bot evolution through enhanced exploration and ...
Designing and Evolving an Unreal Tournament 2004 Expert Bot
Designing and Evolving an Unreal Tournament 2004 Expert Bot
Performance Analysis of GA and PSO over Economic Load Dispatch Problem
Performance Analysis of GA and PSO over Economic Load Dispatch Problem
Designing and Evolving an Unreal Tournament 2004 Expert Bot
Designing and Evolving an Unreal Tournament 2004 Expert Bot
An innovative approach for feature selection based on chicken swarm optimization
An innovative approach for feature selection based on chicken swarm optimization
An Analytical Approach to Optimizing The Utility of ESP Games
An Analytical Approach to Optimizing The Utility of ESP Games
Structural Optimization using Genetic Algorithms - Artificial Intelligence Fu...
Structural Optimization using Genetic Algorithms - Artificial Intelligence Fu...
Plus de Antonio Mora
Optimización Adaptativa basada en Colonias de Hormigas para la Composición de...
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...
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 AI
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 Perfiles
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 Pruning
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.
Checking the difficulty of evolutionary-generated maps in a N-Body inspired m...
This document discusses using an evolutionary algorithm to procedurally generate maps of varying difficulty for a mobile game called Gravityvolve, which simulates the n-body problem in physics. An experiment was conducted with 30 players voting on the difficulty of 21 generated maps both with and without a help line. The results showed that maps were often voted as too easy, and the help line made maps seem easier than intended. The map generation algorithm needs improvement to better match the difficulties felt by players.
Applying Ant Colony Optimization for Service Function Chaining in a 5G Networ...
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 IA
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).
Inteligencia Computacional en Videojuegos (Meetup GranadAI 2019)
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 Generation
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.
Ciencia y Videojuegos (ULP 2019)
Este documento describe las conexiones entre los videojuegos y la ciencia. Explica cómo los videojuegos se han utilizado para aplicaciones científicas como el control de robots y la simulación física. También describe cómo la ciencia se ha incorporado a los videojuegos a través de técnicas como la inteligencia artificial y la simulación realista. Por último, señala que la investigación de videojuegos es ahora un campo importante que estudia temas como la optimización de la inteligencia no jugable y la modelización de aspectos del juego como
The Evolutionary Race: Improving the Process of Evaluating Car Controllers in...
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 ...
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 2017
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 supervivencia
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...
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 games
This document discusses gamification in education and how game mechanics can be used to motivate students. It defines gamification as applying game elements to non-game environments to influence behavior. Game mechanics like achievements, levels, scores, rewards, challenges and social elements can increase student motivation, concentration and effort. The document provides examples of gamification tools used in education, such as Zombiologia which teaches biology concepts through a zombie survival game. Overall, gamification is presented as a way to make learning more engaging and fun for students.
Ciencia y videojuegos (versión Extracción de Información) [UCA 05/2015]
Este documento presenta una introducción a la relación entre ciencia y videojuegos. Explica el crecimiento del mercado de videojuegos y la inclusión de nuevos grupos de jugadores. También describe diferentes sistemas y plataformas de videojuegos actuales, así como la inclusión de asignaturas y grados relacionados con los videojuegos en las universidades.
Ciencia y Videojuegos (v2)
Presentación hecha para el Granada Gaming Festival 2014.
Actualiza y añade varias transparencias respecto a la versión anterior. ;D
Resumen:
La charla presenta las relaciones existentes entre la ciencia y el mundo de los videojuegos a varios niveles: consideración de aspectos científicos en los juegos, uso de elementos diseñados para jugar en el mundo científico y, lo más importante, aplicaciones de la investigación al mundo de los videojuegos.
Evolving Evil: Optimizing Flocking Strategies through Genetic Algorithms for ...
*** Presentation created by Federico Liberatore ***
Flocking strategies are sets of behavior rules for the interaction of agents that allow to devise controllers with reduced complexity that generate emerging behavior. In this paper, we present an application of genetic algorithms and flocking strategies to control the Ghost Team in the game Ms. Pac-Man. In particular, we define flocking strategies for the Ghost Team and optimize them for robustness with respect to the stochastic elements of the game and effectivity against different possible opponents by means of genetic algorithm. The performance of the methodology proposed is tested and compared with that of other standard controllers. The results show that fl
ocking strategies are capable of modeling complex behaviors and produce effective and challenging agents.
Plus de Antonio Mora (20)
Optimización Adaptativa basada en Colonias de Hormigas para la Composición de...
Optimización Adaptativa basada en Colonias de Hormigas para la Composición de...
Adaptive Ant Colony Optimization for Service Function Chaining in a Dynamic 5...
Adaptive Ant Colony Optimization for Service Function Chaining in a Dynamic 5...
Research in Videogames. (Much) further than just AI
Research in Videogames. (Much) further than just AI
GRETIVE: Un Bot Evolutivo para HearthStone basado en Perfiles
GRETIVE: Un Bot Evolutivo para HearthStone basado en Perfiles
Improving the Performance of MCTS-Based μRTS Agents Through Move Pruning
Improving the Performance of MCTS-Based μRTS Agents Through Move Pruning
Checking the difficulty of evolutionary-generated maps in a N-Body inspired m...
Checking the difficulty of evolutionary-generated maps in a N-Body inspired m...
Applying Ant Colony Optimization for Service Function Chaining in a 5G Networ...
Applying Ant Colony Optimization for Service Function Chaining in a 5G Networ...
Investigación en videojuegos. (mucho) Mas allá de la IA
Investigación en videojuegos. (mucho) Mas allá de la IA
Inteligencia Computacional en Videojuegos (Meetup GranadAI 2019)
Inteligencia Computacional en Videojuegos (Meetup GranadAI 2019)
Free Form Evolution for Angry Birds Level Generation
Free Form Evolution for Angry Birds Level Generation
The Evolutionary Race: Improving the Process of Evaluating Car Controllers in...
The Evolutionary Race: Improving the Process of Evaluating Car Controllers in...
Predicción de Quiebra Financiera de Empresas Mediante Equilibrado de Datos y ...
Predicción de Quiebra Financiera de Empresas Mediante Equilibrado de Datos y ...
Driving in TORCS using modular fuzzy controllers - Poster - EvoGAMES 2017
Driving in TORCS using modular fuzzy controllers - Poster - EvoGAMES 2017
Sólo puede quedar uno: Evolución de Bots para RTS basada en supervivencia
Sólo puede quedar uno: Evolución de Bots para RTS basada en supervivencia
Living-UGR: Una aventura gráfica geolocalizada para difundir el patrimonio de...
Living-UGR: Una aventura gráfica geolocalizada para difundir el patrimonio de...
Gamification in Teaching - How to motivate students through games
Gamification in Teaching - How to motivate students through games
Ciencia y videojuegos (versión Extracción de Información) [UCA 05/2015]
Ciencia y videojuegos (versión Extracción de Información) [UCA 05/2015]
Evolving Evil: Optimizing Flocking Strategies through Genetic Algorithms for ...
Evolving Evil: Optimizing Flocking Strategies through Genetic Algorithms for ...
Dernier
Pests of Storage_Identification_Dr.UPR.pdf
InIndia-post-harvestlosses-unscientificstorage,insects,rodents,micro-organismsetc.,accountforabout10percentoftotalfoodgrains
Graininfestation
Directdamage
Indirectly
•theexuviae,skin,deadinsects
•theirexcretawhichmakefoodunfitforhumanconsumption
About600speciesofinsectshavebeenassociatedwithstoredgrainproducts
100speciesofinsectpestsofstoredproductscauseeconomiclosses
waterlessdyeingtechnolgyusing carbon dioxide chemicalspdf
The technology uses reclaimed CO₂ as the dyeing medium in a closed loop process. When pressurized, CO₂ becomes supercritical (SC-CO₂). In this state CO₂ has a very high solvent power, allowing the dye to dissolve easily.
Immersive Learning That Works: Research Grounding and Paths Forward
We will metaverse into the essence of immersive learning, into its three dimensions and conceptual models. This approach encompasses elements from teaching methodologies to social involvement, through organizational concerns and technologies. Challenging the perception of learning as knowledge transfer, we introduce a 'Uses, Practices & Strategies' model operationalized by the 'Immersive Learning Brain' and ‘Immersion Cube’ frameworks. This approach offers a comprehensive guide through the intricacies of immersive educational experiences and spotlighting research frontiers, along the immersion dimensions of system, narrative, and agency. Our discourse extends to stakeholders beyond the academic sphere, addressing the interests of technologists, instructional designers, and policymakers. We span various contexts, from formal education to organizational transformation to the new horizon of an AI-pervasive society. This keynote aims to unite the iLRN community in a collaborative journey towards a future where immersive learning research and practice coalesce, paving the way for innovative educational research and practice landscapes.
The binding of cosmological structures by massless topological defects
Assuming spherical symmetry and weak field, it is shown that if one solves the Poisson equation or the Einstein field
equations sourced by a topological defect, i.e. a singularity of a very specific form, the result is a localized gravitational
field capable of driving flat rotation (i.e. Keplerian circular orbits at a constant speed for all radii) of test masses on a thin
spherical shell without any underlying mass. Moreover, a large-scale structure which exploits this solution by assembling
concentrically a number of such topological defects can establish a flat stellar or galactic rotation curve, and can also deflect
light in the same manner as an equipotential (isothermal) sphere. Thus, the need for dark matter or modified gravity theory is
mitigated, at least in part.
Describing and Interpreting an Immersive Learning Case with the Immersion Cub...
Current descriptions of immersive learning cases are often difficult or impossible to compare. This is due to a myriad of different options on what details to include, which aspects are relevant, and on the descriptive approaches employed. Also, these aspects often combine very specific details with more general guidelines or indicate intents and rationales without clarifying their implementation. In this paper we provide a method to describe immersive learning cases that is structured to enable comparisons, yet flexible enough to allow researchers and practitioners to decide which aspects to include. This method leverages a taxonomy that classifies educational aspects at three levels (uses, practices, and strategies) and then utilizes two frameworks, the Immersive Learning Brain and the Immersion Cube, to enable a structured description and interpretation of immersive learning cases. The method is then demonstrated on a published immersive learning case on training for wind turbine maintenance using virtual reality. Applying the method results in a structured artifact, the Immersive Learning Case Sheet, that tags the case with its proximal uses, practices, and strategies, and refines the free text case description to ensure that matching details are included. This contribution is thus a case description method in support of future comparative research of immersive learning cases. We then discuss how the resulting description and interpretation can be leveraged to change immersion learning cases, by enriching them (considering low-effort changes or additions) or innovating (exploring more challenging avenues of transformation). The method holds significant promise to support better-grounded research in immersive learning.
ESR spectroscopy in liquid food and beverages.pptx
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
The debris of the ‘last major merger’ is dynamically young
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
ESA/ACT Science Coffee: Diego Blas - Gravitational wave detection with orbita...
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In this talk I will describe some recent ideas to find gravitational waves from supermassive black holes or of primordial origin by studying their secular effect on the orbital motion of the Moon or satellites that are laser ranged.Sexuality - Issues, Attitude and Behaviour - Applied Social Psychology - Psyc...
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Study on Genetic Algorithm Approaches to Improve an Autonomous Agent for a Fighting Game
- 2. Aim To create an agent/bot to win in fighting games combats. Focused on the Fighting Game AI Competition (FGAIC). (http://www.ice.ci.ritsumei.ac.jp/~ftgaic/index-2h.html) One of the few studies using Evolutionary Algorithms (Genetic Algorithms) in this scope. EvoAPPS 2021
- 3. Aim Start from a state-of-the-art agent: Mizuno (C. Yin Chu and R. Thawonmas). Fuzzy controller together with a classification method (kNN) to select the best action to perform. Depends on 10 values: distances and health levels. Optimize behavioural parameters by means of Genetic Algorithms. EvoAPPS 2021
- 4. Dare FightingICE (formerly FightingICE) Simulator used in the FGAIC. Framework to test own created AIs (agents). Two modes: time and health. 3 action buttons (kick, punch, special) and 9 directions. EvoAPPS 2021 https://www.ice.ci.ritsumei.ac.jp/~ftgaic/index-2.html
- 5. GAs scheme Individual vector of 10 values. Uniform crossover. Random mutation (one gene). Fitness (Time mode) – set of combats: ti → remaining time in combat i. sai → health of own agent in combat i. sri → health of rival agent in combat i. n → number of combats EvoAPPS 2021
- 6. GAs implemented • Generational without Elitism: o The whole population could be replaced. o Binary tournament (all individuals 1 vs 1). The best in their respective combats will remain. o Next population generated offspring + half random individuals o High exploration factor. EvoAPPS 2021
- 7. GAs implemented • Generational with Elitism: o The whole population could be replaced. o Binary tournament (all individuals 1 vs 1). The best in their respective combats will remain. o Next population best parents + best generated offspring o The best overall always survive. o Higher exploitation factor. EvoAPPS 2021
- 8. Experimental setup EvoAPPS 2021 USED AGENTS (FITNESS) BCP Dora CONFIGURATION - 20 generations - 16 individuals - 60% crossover probability - 10% mutation probability - 6 combats/fitness - Health mode - 300 health points - 10 runs TESTS (9 combats) vs Dora vs BCP vs Thunder
- 9. Experiments EvoAPPS 2021 4 best per run All vs All Best for the run Best for every run All vs All Best of experiment 01 02
- 10. Results (win rate) EvoAPPS 2021 Optimized bots are able to win to some tough agents
- 11. Results (health difference) EvoAPPS 2021 Optimized bots get more reduction of rivals’ health
- 12. Conclusions Preliminary study using GAs to optimize fighting game agents. Non-impressive results, but promising ones. Evolved bots are able to win to tough rivals and get a higher difference in health with rivals. Elitists versions perform better. EvoAPPS 2021
- 13. Future Work Implement a higher exploitation factor. Use more advanced GA schemes. Select a more suited configuration as well as specific and adapted operators (such as a better fitness function). Perform more complete tests against top-level agents from the Fighting Game AI Competition. EvoAPPS 2021
- 14. Thanks! Contact: Noelia Escalera: escaleranm@gmail.com Antonio Mora: amorag@ugr.es Pablo García: pablogarcia@ugr.es EvoAPPS 2021 Stickers: Panfri (Stickers Cloud)