Alex Mendel: Robot Tatrabot Rieši Hru Sokoban
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Folha De Rosto ICS.Cdr
“For when established identities become outworn or unfinished ones threaten to remain incomplete, special crises compel men to wage holy wars, by the cruellest means, against those who seem to question or threaten their unsafe ideological bases.” Erik Erikson (1956), “The Problem of Ego Identity”, p. 114 “In games it’s very difficult to portray complex human relationships. Likewise, in movies you often flit between action in various scenes. That’s very difficult to do in games, as you generally play a single character: if you switch, it breaks immersion. The fact that most games are first-person shooters today makes that clear. Stories in which the player doesn’t inhabit the main character are difficult for games to handle.” Hideo Kojima Simon Parkin (2014), “Hideo Kojima: ‘Metal Gear questions US dominance of the world”, The Guardian iii AGRADECIMENTOS Por começar quero desde já agradecer o constante e imprescindível apoio, compreensão, atenção e orientação dos Professores Jean Rabot e Clara Simães, sem os quais este trabalho não teria a fruição completa e correta. Um enorme obrigado pelos meses de trabalho, reuniões, telefonemas, emails, conversas e oportunidades. Quero agradecer o apoio de família e amigos, em especial, Tia Bela, João, Teté, Ângela, Verxka, Elma, Silvana, Noëmie, Kalashnikov, Madrinha, Gaivota, Chacal, Rita, Lina, Tri, Bia, Quelinha, Fi, TS, Cinco de Sete, Daniel, Catarina, Professor Albertino, Professora Marques e Professora Abranches, tanto pelas forças de apoio moral e psicológico, pelas recomendações e conselhos de vida, e principalmente pela amizade e memórias ao longo desta batalha. Por último, mas não menos importante, quero agradecer a incessante confiança, companhia e aceitação do bom e do mau pela minha Twin, Safira, que nunca me abandonou em todo o processo desta investigação, do meu caminho académico e da conquista da vida e sonhos. -
Artificial General Intelligence in Games: Where Play Meets Design and User Experience
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by The IT University of Copenhagen's Repository ISSN 2186-7437 NII Shonan Meeting Report No.130 Artificial General Intelligence in Games: Where Play Meets Design and User Experience Ruck Thawonmas Julian Togelius Georgios N. Yannakakis March 18{21, 2019 National Institute of Informatics 2-1-2 Hitotsubashi, Chiyoda-Ku, Tokyo, Japan Artificial General Intelligence in Games: Where Play Meets Design and User Experience Organizers: Ruck Thawonmas (Ritsumeikan University, Japan) Julian Togelius (New York University, USA) Georgios N. Yannakakis (University of Malta, Malta) March 18{21, 2019 Plain English summary (lay summary): Arguably the grand goal of artificial intelligence (AI) research is to pro- duce machines that can solve multiple problems, not just one. Until recently, almost all research projects in the game AI field, however, have been very specific in that they focus on one particular way in which intelligence can be applied to video games. Most published work describes a particu- lar method|or a comparison of two or more methods|for performing a single task in a single game. If an AI approach is only tested on a single task for a single game, how can we argue that such a practice advances the scientific study of AI? And how can we argue that it is a useful method for a game designer or developer, who is likely working on a completely different game than the method was tested on? This Shonan meeting aims to discuss three aspects on how to generalize AI in games: how to play any games, how to model any game players, and how to generate any games, plus their potential applications. -
Implementation of A* Algorithm for Solving Sokoban Logic Games
Journal of Applied Intelligent System (e-ISSN : 2502-9401 | p-ISSN : 2503-0493) Vol. 4 No. 2, December 2019, pp. 104 – 111 Implementation of A* Algorithm for Solving Sokoban Logic Games Rosa Tri Setiani *1, Bonifacius Vicky Indriyono 2 School of Information and Computer Management Kadiri, Jl. Balowerti II No. 26-30 Kediri East Java,(0354-696770) E-mail : [email protected]*1, [email protected] Abstract - Sokoban is one of the puzzle category games that requires players to push boxes towards the top, bottom, left and right until the specified destination. Sometimes it takes a long time to complete a very difficult level so that a solution is needed. So that these difficulties can be overcome, we need an algorithm that can help find the right path. A * algorithm is an algorithm that minimizes the total cost of the track under the right conditions that will provide the best solution in optimal time. A * algorithm will find the distance of the fastest route to be taken by a starting point to the destination object by removing unnecessary steps. From the test results, this A * algorithm can help players in finding a solution to the road in the form of completion steps in the Sokoban game by determining the smallest F value. Keywords - Artificial Intelligence, Games, Sokoban, A * algorithm 1. INTRODUCTION The development of science and technology will never end and will grow faster along with the development of supporting technology. One of the many technologies that is growing is artificial intelligence. Artificial intelligence (AI) is the main focus in the world of research on how a computer program is able to learn and make decisions like humans. -
Hyperstate Space Graphs for Automated Game Analysis
Hyperstate Space Graphs For Automated Game Analysis Michael Cook Azalea Raad Queen Mary University of London Max Planck Institute for Software Systems [email protected] [email protected] Abstract—Automatically analysing games is an important chal- combat, are either elided or combined into higher-level states. lenge for automated game design, general game playing, and co- Another option, commonly used in the design of AI agents, is creative game design tools. However, understanding the nature of to limit the size of the SSG – many Chess agents are limited an unseen game is extremely difficult due to the lack of a priori design knowledge and heuristics. In this paper we formally define in how far ahead they may look to evaluate a move. hyperstate space graphs, a compressed form of state space graphs These abstraction processes for state space compression which can be constructed without any prior design knowledge require game-specific expert knowledge: what is important and about a game. We show how hyperstate space graphs produce what is not, and which aspects of the game we wish to focus compact representations of games which closely relate to the on. However, many AI systems are required to play, analyse heuristics designed by hand for search-based AI agents; we show how hyperstate space graphs also relate to modern ideas or interact with games that have never been seen before by about game design; and we point towards future applications for anyone. Automated game design systems must understand a hyperstates across game AI research. game in order to be able to evaluate or change its design. -
Analysis and Development of a Game of the Genre Roguelike
UNIVERSIDADE FEDERAL DO RIO GRANDE DO SUL INSTITUTO DE INFORMÁTICA CURSO DE CIÊNCIA DA COMPUTAÇÃO CIRO GONÇALVES PLÁ DA SILVA Analysis and Development of a Game of the Roguelike Genre Final Report presented in partial fulfillment of the requirements for the degree of Bachelor of Computer Science. Advisor: Prof. Dr. Raul Fernando Weber Porto Alegre 2015 UNIVERSIDADE FEDERAL DO RIO GRANDE DO SUL Reitor: Prof. Carlos Alexandre Netto Vice-Reitor: Prof. Rui Vicente Oppermann Pró-Reitor de Graduação: Prof. Sérgio Roberto Kieling Franco Diretor do Instituto de Informática: Prof. Luís da Cunha Lamb Coordenador do Curso de Ciência da Computação: Prof. Raul Fernando Weber Bibliotecária-Chefe do Instituto de Informática: Beatriz Regina Bastos Haro ACKNOWLEDGEMENTS First, I would like to thank my advisor Raul Fernando Weber for his support and crucial pieces of advisement. Also, I'm grateful for the words of encouragement by my friends, usually in the form of jokes about my seemingly endless graduation process. I'm also thankful for my brother Michel and my sister Ana, which, despite not being physically present all the time, were sources of inspiration and examples of hard work. Finally, and most importantly, I would like to thank my parents, Isabel and Roberto, for their unending support. Without their constant encouragement and guidance, this work wouldn't have been remotely possible. ABSTRACT Games are primarily a source of entertainment, but also a substrate for developing, testing and proving theories. When video games started to popularize, more ambitious projects demanded and pushed forward the development of sophisticated algorithmic techniques to handle real-time graphics, persistent large-scale virtual worlds and intelligent non-player characters. -
The Tekatekitech System for Automated Minigame Generation: First Steps
The TekaTekiTech System for Automated Minigame Generation: First Steps Edward J. Powley, James Clewett and Simon Colton The MetaMakers Institute Academy for Innovation and Research Falmouth University, UK fedward.powley, james.clewett, [email protected] Abstract Our wider aim in this work is to investigate how players respond to games which are discovered rather than created. Automated generation of entire games is a challenging task in computational creativity. This paper presents If a human game designer produces a game which is impos- some initial explorations with a system for generating sible to win, or only winnable through superhuman dexter- small puzzle games with simple mechanics but com- ity, then the player might reasonably say that the designer plex emergent gameplay. Our system is designed to has made a mistake. However if such a game is discov- enable collaboration between human designers and cre- ered by an automatic game generator, we conjecture that the ative computer agents, and to explore the notion of writ- player will be much more tolerant of the unwinnable game, ing code which itself writes code. Our aim is to produce and indeed that coming up with an explanation for why it is games in which deciphering the mechanics of the game unwinnable might be a pleasure in itself. Ensuring solvabil- is itself a meta-mechanic, and where some puzzles may ity is often a concern when automatically generating game be unsolvable (but not obviously so). We draw an anal- content (Smith, Butler, and Popovic´ 2013); instead, we are ogy between unsolvable puzzles and unprovable math- ematical conjectures: we are intrigued to see whether interested in the region of design space where even the de- the same force that compels mathematicians to perse- signer does not know whether the content is solvable. -
Matching Games and Algorithms for General Video Game Playing
Proceedings, The Twelfth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-16) Matching Games and Algorithms for General Video Game Playing Philip Bontrager, Ahmed Khalifa, Andre Mendes, Julian Togelius New York University New York, New York 11021 [email protected], [email protected], [email protected], [email protected] Abstract This paper examines the performance of a number of AI agents on the games included in the General Video Game Playing Competition. Through analyzing these results, the paper seeks to provide insight into the strengths and weaknesses of the current generation of video game playing algorithms. The paper also pro- vides an analysis of the given games in terms of inher- ent features which define the different games. Finally, the game features are matched with AI agents, based on performance, in order to demonstrate a plausible case for algorithm portfolios as a general video game play- ing technique. Introduction The General Video Game AI Framework (GVGAI) is an Figure 1: Different games in the GVGAI framework. attempt to create general framework for game-based test- ing of artificial intelligence methods (Levine et al. 2013; Perez et al. 2015b; Perez-Liebana et al. 2016). Unlike the is a complicated task. One needs to know the best features general game playing competition (GGP), the GVGP is fo- that can help predict the performance of an algorithm on a cused on video games, in particular two-dimensional games game. In the face of limited data sets, one also needs to avoid similar to classic arcade games. The general video game overfitting by focusing on the most relevant regularities. -
AI-Assisted Game Debugging with Cicero
AI-assisted game debugging with Cicero Tiago Machado Daniel Gopstein Andy Nealen Oded Nov Julian Togelius New York University New York University New York University New York University New York University [email protected] [email protected] [email protected] [email protected] [email protected] Abstract—We present Cicero, a mixed-initiative application type and simulate their games by using intelligent agents for prototyping two-dimensional sprite-based games across dif- together with other computational methods like visualizations ferent genres such as shooters, puzzles, and action games. Cicero and queries. Cicero is built on top of the General Video provides a host of features which can offer assistance in different Game AI Framework (GVGAI) and offers more generality than stages of the game development process. Noteworthy features systems built for stand-alone games. Users can apply all of include AI agents for gameplay simulation, a game mechanics Cicero’s features to a number of different genres within the recommender system, a playtrace aggregator, heatmap-based game analysis, a sequential replay mechanism, and a query system gridphysics-based-games sphere. To highlight the immediate that allows searching for particular interaction patterns. In order advantage these methods offer game designers we describe to evaluate the efficacy and usefulness of the different features of a user study in which we compare how humans perform Cicero, we conducted a user study in which we compared how debugging games with and without AI assistance. To detail the users perform in game debugging tasks with different kinds of contrasting styles of features within Cicero, we also present a assistance. -
Challenges for Game Designers Brenda Brathwaite And
CHALLENGES FOR GAME DESIGNERS BRENDA BRATHWAITE AND IAN SCHREIBER Charles River Media A part of Course Technology, Cengage Learning Australia, Brazil, Japan, Korea, Mexico, Singapore, Spain, United Kingdom, United States Challenges for Game Designers © 2009 Course Technology, a part of Cengage Learning. ALL RIGHTS RESERVED. No part of this work covered by the copyright Brenda Brathwaite and Ian Schreiber herein may be reproduced, transmitted, stored, or used in any form or by any means graphic, electronic, or mechanical, including but not limited to photocopying, recording, scanning, digitizing, taping, Web distribution, Publisher and General Manager, information networks, or information storage and retrieval systems, except Course Technology PTR: as permitted under Section 107 or 108 of the 1976 United States Copyright Stacy L. Hiquet Act, without the prior written permission of the publisher. Associate Director of Marketing: For product information and technology assistance, contact us at Sarah Panella Cengage Learning Customer & Sales Support, 1-800-354-9706 For permission to use material from this text or product, Content Project Manager: submit all requests online at cengage.com/permissions Jessica McNavich Further permissions questions can be emailed to [email protected] Marketing Manager: Jordan Casey All trademarks are the property of their respective owners. Acquisitions Editor: Heather Hurley Library of Congress Control Number: 2008929225 Project and Copy Editor: Marta Justak ISBN-13: 978-1-58450-580-8 ISBN-10: 1-58450-580-X CRM Editorial Services Coordinator: Jen Blaney eISBN-10: 1-58450-623-7 Course Technology Interior Layout: Jill Flores 25 Thomson Place Boston, MA 02210 USA Cover Designer: Tyler Creative Services Cengage Learning is a leading provider of customized learning solutions with office locations around the globe, including Singapore, the United Kingdom, Indexer: Sharon Hilgenberg Australia, Mexico, Brazil, and Japan. -
Deep Learning for Procedural Content Generation
Noname manuscript No. (will be inserted by the editor) Deep Learning for Procedural Content Generation Jialin Liu1 · Sam Snodgrass2 · Ahmed Khalifa3 · Sebastian Risi2,4 · Georgios N. Yannakakis2,5,6 · Julian Togelius2,3 Received: date / Accepted: date Abstract Procedural content generation in video applied to generate game content directly or indirectly, games has a long history. Existing procedural content discusses deep learning methods that could be used for generation methods, such as search-based, solver-based, content generation purposes but are rarely used today, rule-based and grammar-based methods have been ap- and envisages some limitations and potential future di- plied to various content types such as levels, maps, char- rections of deep learning for procedural content gener- acter models, and textures. A research field centered on ation. content generation in games has existed for more than Keywords Procedural content generation · Game a decade. More recently, deep learning has powered a design · Deep learning · Machine learning · Computa- remarkable range of inventions in content production, tional and artificial intelligence which are applicable to games. While some cutting-edge deep learning methods are applied on their own, oth- ers are applied in combination with more traditional 1 Introduction methods, or in an interactive setting. This article sur- veys the various deep learning methods that have been Deep learning has powered a remarkable range of in- ventions in content production in recent years, includ- J. Liu ing new methods for generating audio, images, 3D ob- E-mail: [email protected] jects, network layouts, and other content types across S. Snodgrass a range of domains. -
Simulating Strategy and Dexterity for Puzzle Games
Simulating Strategy and Dexterity for Puzzle Games Aaron Isaksen∗‡, Drew Wallacey, Adam Finkelsteiny, Andy Nealen∗ ∗NYU Tandon School of Engineering yPrinceton University [email protected] Abstract— We examine the impact of strategy and dexterity on video games in which a player must use strategy to decide between multiple moves and must use dexterity to correctly execute those moves. We run simulation experiments on variants of two popular, interactive puzzle games: Tetris, which exhibits dexterity in the form of speed-accuracy time pressure, and Puzzle Bobble, which requires precise aiming. By modeling dexterity and strategy as separate components, we quantify the effect of each type of difficulty using (a) Best Move (b) Intended Move normalized mean score and artificial intelligence agents and Strategy Error and Dexterity Errors that make human-like errors. We show how these Fig. 1. Examples from Tetris demonstrating how players use strategy to decide techniques can model and visualize dexterity and between multiple moves and use dexterity to correctly execute such moves. strategy requirements as well as the effect of scoring systems on expressive range. Index Terms—AI-assisted game design, dexterity, strategy, The design and modeling of games that combine strategy and difficulty, automated play testing dexterity requires new techniques: existing quantitative work typically analyzes strategy games and dexterity games sepa- I. INTRODUCTION rately (Sec. I-B). In contrast, this paper presents an approach Video games have various sources of difficulty that affect the for simulating and quantifying the interactions between these experience of players with different skills in different ways. In two types of difficulty. -
General Video Game Playing with Goal Orientation
General Video Game Playing with Goal Orientation Blaine Ross September 2014 Master Thesis Advanced Computer Science University of Strathclyde Supervisor: Dr. John Levine (Senior Lecturer, University of Strathclyde) Abstract Artificial Intelligence of the past has been primarily designed to deal with specific tasks, but within the field of general video game playing intelligent agents must be created that can cope with multiple different unknown scenarios. The AAAI started a competition in 2005 that allowed board games to be the focus of this research, and a new competition has been started in 2014 to help further this which now focuses on video games, and will be concluded at the IEEE CIG conference in Germany. This competition features a library of Java based games where anyone can try and create general video game playing agents and then submit them for evaluation. To date, focus has been given to tree search based approaches such as Monte-Carlo Tree Search, but this method has its drawbacks which does not make it applicable to all types of gameplay. In this report a possible solution is suggested to cope with one of these limitations by looking at a more goal oriented approach in combination with Monte-Carlo Tree Search. By using this newly designed method a comparison is able to be drawn with a variety of other techniques that do not have this goal based focus. The results are promising and show potential not only in the scores achieved during gameplay, but in the overall behaviour of AI controlled agents when playing video games with greater goal orientation.