In eighties, the idea of using potential fields was first introduced in the field of the robotics. The purpose of using potential fields was to achieve the natural movement in robotics. Many researchers proceeded this idea to enhance their research. The idea of using potential fields was also introduced in real time strategy games for the better movement of objects. In this thesis we worked on the idea of using asymmetric potential fields in the game environment. The purpose of our study was to analyze the affect of asymmetric potential fields on unit’s formation and their movement in game environment. In this study performance of asymmetric potential fields was also compared with symmetric potential fields. By literature review the potential field and its usage in RTS games were studied. The methodology to implement the potential fields in RTS game was also identified in literature review. In experimental part the asymmetric potential fields implemented by using the methodology proposed by Hagelbäck and Johansson. By following that methodology asymmetric potential field was applied on StarCraft bot by using the BWAPI. Experiment was also designed to test the asymmetric potential field bot. Asymmetric potential field bot was tested on the two maps of StarCraft: Brood War game. On these two maps, bot implemented with asymmetric potential field and the bot implemented with symmetric potential field competed with four bots. Three bots were selected from StarCraft competition and one was built-in bot of this game. The results of these competition shows that asymmetric potential field bot has better performance than symmetric potential field bot. The results of experiments show that the performance of bot implemented with asymmetric potential fields was better than symmetric potential field on single unit type and two unit types. This study shows that with the help of asymmetric potential fields interesting unit formation can be formed in real time strategy games, which can give better result than symmetric potential fields.

In this study we examine how much latency is required in an RTS-game (Real-Time Strategy) before the player quits the game. In the study latency is simulated in the games Age of Mythology and Starcraft: Brood War with the program WANem, and then the testplayers are asked “Do you want to quit because of latency?”. This data is then gathered and compiled into graphs to show the results. These results show that the limit for when players want to quit differs between the two games and that there are two factors that play into this. These factors are mainly the games mechanic, how the game is played, and how the game itself handles the latency. The results show that the limit for when players want to quit in Age of Mythology lies around 1000 ms, while in Starcraft: Brood War the limit is around 600 ms. / I denna studie undersöks det hur hög latency som krävs i ett RTS-spel (RealtidsStrategi) innan spelaren avslutar spelet. I studien simuleras latency i spelen Age of Mythology och Starcraft: Brood War med hjälp av programmet WANem, därefter får testpersonerna följande fråga ställda till sig: “Vill du sluta spela på grund av latency?”, denna data samlas sedan in och kompileras till grafer för att kunna visa resultaten. Resultaten som studien får fram är att gränsen skiljer sig mellan spelen och att det finns två faktorer som bidrar till detta, faktorerna är Spelets mekanik och Hur spelet hanterar latency. De siffror som studien har fått fram är att gränsen i Age of Mythology ligger på cirka 1000 ms medan gränsen i Starcraft: Brood War ligger på cirka 600 ms.

In real-time strategy (RTS) games, the human player faces tasks such as resource allocation, mission planning, and unit coordination. An Artificial Intelligence (AI) system that acts as an opponent against the human player need to be quite powerful, in order to create one cohesive strategy for victory. Even though the goal for an AI system in a computer game is not to defeat the human player, it might still need to act intelligently and look credible. It might however also need to provide just enough difficulty, so that both novice and expert players appreciates the game. The behavior of computer controlled opponents in RTS games of today has to a large extent been based on static algorithms and structures. Furthermore, the AI in RTS games performs the worst at the strategic level, and many of the problems can be tracked to its static nature. By introducing an adaptive AI at the strategic level, many of the problems could possibly be solved, the illusion of intelligence might be strengthened, and the entertainment value could perhaps be increased. The aim of this dissertation has been to investigate how dynamic scripting, a technique for achieving adaptation in computer games, possibly could be applied at the strategic level in an RTS game. The dynamic scripting technique proposed by Spronck, et al. (2003), was originally intended for computer role-playing games (CRPGs), where it was used for online creation of scripts to control non-player characters (NPCs). The focus in this dissertation has been to investigate: (1) how the structure of dynamic scripting possibly could be modified to fit the strategic level in an RTS game, (2) how the adaptation time possibly could be lowered, and (3) how the performance of dynamic scripting possibly could be throttled. A new structure for applying dynamic scripting has been proposed: a goal-rule hierarchy, where goals are used as domain knowledge for selecting rules. A rule is seen as a strategy for achieving a goal, and a goal can in turn be realized by several different rules. The adaptation process operates on the probability of selecting a specific rule as strategy for a specific goal. Rules can be realized by sub-goals, which create a hierarchical system. Further, a rule can be coupled with preconditions, which if false initiates goals with the purpose of fulfilling them. This introduces planning. Results have shown that it can be more effective, with regard to adaptation time, re-adaptation time, and performance, to have equal punishment and reward factors, or to have higher punishments than rewards, compared to having higher rewards than punishments. It has also been shown that by increasing the learning rate, or including the derivative, both adaptation, and re-adaptation times, can effectively be lowered. Finally, this dissertation has shown that by applying a fitness-mapping function, the performance of the AI can effectively be throttled. Results have shown that learning rate, and maximum weight setting, also can be used to vary the performance, but not to negative performance levels.

Developing real-time strategy game AI is a challenging task due to that an AI-player has to deal with many different decisions and actions in an ever changing complex game world. Humans have little problem when it comes to dealing with the complexity of the game genre while it is a difficult obstacle to overcome for the computer. Adapting to the opponents strategy is one of many things that players typically have to do during the course of a game in the real-time strategy genre. This report presents a finite-state machine based solution to the mentioned problem and implements it with the help of the existing Starcraft: Broodwar AI Opprimobot. The extension is experimentally compared to the original implementation of Opprimobot. The comparison shows that both manages to achieve approximately the same win ratio against the built-in AI of Starcraft: Broodwar, but the modified version provides away to model more complex strategies.

Communicating design is in many aspects a difficult process. Game design is not only directives on look and feel, but also carries intentionality. To properly convey intentionality, a common abstract vocabulary is a well-established method for expressing design. Game design patterns are an attempt to formalize and establish such a vocabulary. Game design patterns are a debated tool and this paper aims to examine the practical application of a pattern through a quantitative study in order to strengthen the potential for a more cohesive definition of the term. This is done by first establishing a game design pattern through observation of RTS games. The pattern is then studied through implementation in three commercial RTS games. The results focus on quantitative data gathered from AI vs AI matches related to game pacing. Through testing and analysis of the AI matches it can be stated that game design patterns in a contextualized setting supports the idea of using game design patterns as a formal tool. It was further concluded that the AI also came with limitations in how the collected data is applicable to the overall design of the games. Additional studies using quantitative data in conjunction with qualitative observations could lend further support to game design patterns as a useful tool for both researchers and developers. / Kommunikation av design är i många avseenden en invecklad process. Design av spel innebär inte enbart riktlinjer för utseende och känsla, utan också intentionalitet. En beprövad metod för att uttrycka design och intentionalitet är skapandet av ett gemensamt vokabulär. Game design patterns är ett försök att upprätta och formalisera just ett sådant vokabulär inom speldesign. Game design patterns är ett debatterat verktyg och detta arbetet ämnar undersöka den praktiska tillämpningen av ett pattern genom en kvantitativ studie för att stärka potentialen för en mer sammanhängande definition av termen. Detta utförs genom att först etablera ett game design pattern med hjälp av observation av RTS-spel. Sedan studeras det genom implementation i tre kommersiella RTS-spel. Resultatet fokuseras på kvantitativ data relaterat till pacing som insamlas från matcher mellan två AI. Genom analys av AI-matcherna kan det anses att game design pattern i en kontextualiserad inramning stöder teorin att använda design patterns som ett formellt designverktyg. Vidare drogs slutsatsen att användandet av AI också innebär begränsningar i hur tillämplig den insamlade datan är i den övergripande designen av spel. Fler studier med kvantitativ data ihop med kvalitativa observationer kan ytterligare stödja idén om game design pattern som ett användbart verktyg för både forskare och utvecklare inom spel.

Real-time strategy (RTS) games constitute one of the largest game genres today and have done so for the past decade. A central feature of real-time strategy games is opponent AI which is suggestively the “last frontier” of game development because the focus of research has primarily been on other components, graphics in particular. This has led to AI research being largely ignored within the commercial game industry but several methods have recently been suggested for improving the strategic ability of AI agents in real-time strategy games. The aim of this project is to evaluate how a method called qualitative spatial reasoning can improve AI on a tactical level in a selected RTS game. An implementation of an AI agent that uses qualitative spatial reasoning has been obtained and an evaluation of its performance in an RTS game example monitored and analysed. The study has shown that qualitative spatial reasoning affects AI agent’s behaviour significantly and indicates that it can be used to deduce a rule-base that increases the unpredictability and performance of the agent.

Real-time strategy games are an exciting area of research, as creating a game AI poses many challenges - from managing a single unit to completing an objective of the game. This thesis explores possible solutions to this task, using genetic programming and neuroevolution. It presents and compares findings and differences between the models. Both methods performed reasonably well, but genetic programming was found to be a bit more effective in performance and results.

This thesis deals with game development on the Xbox 360 platform and implementation of a starter kit for this platform. After introduction of the Xbox 360 as a modern and very powerful gaming console, the XNA technology that makes development of games for console possible for the first time in history not only for professionals but also for amateurs, is introduced. The next part mentions existing starter kits for 3D XNA games, existing tools widely used to analyze implemented solution with focus on performance and tools commonly used for game content creation. The main part of thesis is dedicated to design, implementation and testing of a real time strategy starter kit prototype, with emphasis on efficiency, understandability and usability by XNA community members in mind.

This thesis documents the development of a video game about sustainable energy use that unites fun with learning. Many other educational games do not properly translate knowledge, facts, and lessons into the language of games: mechanics, rules, rewards, and feedback. This approach differs by using game mechanics in new ways to express lessons about energy sustainability. This design is based on the real time strategy (RTS) genre. Players of these types of games must manage economic problems such as extracting, refining, and allocating resources, as well as industrial problems such as producing buildings and military units. These games often use imaginative fantasy elements to connect with their audience, but also made-up economic numbers and fictional resources such as magic crystals which have little to do with the real world. This thesis' approach retains the fantasy elements and gameplay conventions of this popular genre, but uses numbers, resources, and situations based on research about real-world energy production. The intended result is a game in which the player learns about energy use simply by trying to overcome the game's challenges. In addition, a combined quantitative/qualitative study was performed, which shows that players of the game learned new things, enjoyed the game, and became more interested in the topic of energy use.

Made available in DSpace on 2015-05-14T12:36:51Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 4482656 bytes, checksum: 11b85e413d691749edd8d5be0d8f56d4 (MD5) Previous issue date: 2014-03-28 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Electronic games and, in particular, real-time strategy (RTS) games, are increasingly seen as viable and important fields for artificial intelligence research because of commonly held characteristics, like the presence of complex environments, usually dynamic and with multiple agents. In commercial RTS games, the computer behavior is mostly designed with simple ad hoc, static techniques that require manual definition of actions and leave the agent unable to adapt to the various situations it may find. This approach, besides being lengthy and error-prone, makes the game relatively predictable after some time, allowing the human player to eventually discover the strategy used by the computer and develop an optimal way of countering it. Using machine learning techniques like reinforcement learning is a way of trying to avoid this predictability, allowing the computer to evaluate the situations that occur during the games, learning with these situations and improving its behavior over time, being able to choose autonomously and dynamically the best action when needed. This work proposes a modeling for the use of SARSA, a reinforcement learning technique, applied to combat situations in RTS games, with the goal of allowing the computer to better perform in this fundamental area for achieving victory in an RTS game. Several tests were made with various game situations and the agent applying the proposed modeling, facing the game's default AI opponent, was able to improve its performance in all of them, developing knowledge about the best actions to choose for the various possible game states and using this knowledge in an efficient way to obtain better results in later games / Jogos eletrônicos e, em especial, jogos de estratégia em tempo real (RTS), são cada vez mais vistos como campos viáveis e importantes para pesquisas de inteligência artificial por possuírem características interessantes para a área, como a presença de ambientes complexos, muitas vezes dinâmicos e com múltiplos agentes. Nos jogos RTS comerciais, o comportamento do computador é geralmente definido a partir de técnicas ad hoc simples e estáticas, com a necessidade de definição manual de ações e a incapacidade de adaptação às situações encontradas. Esta abordagem, além de demorada e propícia a erros, faz com que o jogo se torne relativamente previsível após algum tempo, permitindo ao jogador eventualmente descobrir a estratégia utilizada pelo computador e desenvolver uma forma ótima de enfrentá-lo. Uma maneira de tentar combater esta previsibilidade consiste na utilização de técnicas de aprendizagem de máquina, mais especificamente do aprendizado por reforço, para permitir ao computador avaliar as situações ocorridas durante as partidas, aprendendo com estas situações e aprimorando seu conhecimento ao longo do tempo, sendo capaz de escolher de maneira autônoma e dinâmica a melhor ação quando necessário. Este trabalho propõe uma modelagem para a utilização de SARSA, uma técnica do aprendizado por reforço, aplicada a situações de combate em jogos RTS, com o objetivo de fazer com o que o computador possa se portar de maneira mais adequada nessa área, uma das mais fundamentais para a busca da vitória em um jogo RTS. Nos testes realizados em diversas situações de jogo, o agente aplicando a modelagem proposta, enfrentando o oponente padrão controlado pela IA do jogo, foi sempre capaz de melhorar seus resultados ao longo do tempo, obtendo conhecimento acerca das melhores ações a serem tomadas a cada momento decisório e aproveitando esse conhecimento nas suas partidas futuras