Context. Video games are a big entertainment industry. Many video games let players play against or together. Some video games also make it possible for players to play against or together with computer controlled players, called bots. Artificial Intelligence (AI) is used to create bots. Objectives. This thesis aims to implement cooperative behaviors between two bots and determine if the behaviors lead to an increase in win ratio. This means that the bots should be able to cooperate in certain situations, such as when they are attacked or when they are attacking. Methods. The bots win ratio will be tested with a series of quantitative experiments where in each experiment two teaming bots with cooperative behavior will play against two teaming bots without any cooperative behavior. The data will be analyzed with a t-test to determine if the data are statistical significant. Results and Conclusions. The results show that cooperative behavior can increase performance of two teaming Real Time Strategy bots against a non-cooperative team with two bots. However, the performance could either be increased or decreased depending on the situation. In three cases there were an increase in performance and in one the performance was decreased. In three cases there was no difference in performance. This suggests that more research is needed for these cases.

We are studying the use of tilt-enabled handheld touchscreen devices as an interface for top-down strategy games. We will explore how using different input modes (tilt and touch) compare for certain tasks in terms of efficiency and comfort. Real-time and turn-based strategy games are a popular form of electronic gaming, though these games currently have only minor representation on tablets. This genre of game requires both a wide variety of input and the display of a wealth of information. We are exploring whether, with suitable interface developments, this genre can become as accessible on tablet devices as on traditional computers. These interface approaches may also prove useful for expanding the presence of other game genres in the mobile space.

Real-Time Strategy (RTS) is a sub-genre of strategy games which is running in real-time, typically in a war setting. The player uses workers to gather resources, which in turn are used for creating new buildings, training combat units, build upgrades and do research. The game is won when all buildings of the opponent(s) have been destroyed. The numerous tasks that need to be handled in real-time can be very demanding for a player. Computer players (bots) for RTS games face the same challenges, and also have to navigate units in highly dynamic game worlds and deal with other low-level tasks such as attacking enemy units within fire range. This thesis is a compilation grouped into three parts. The first part deals with navigation in dynamic game worlds which can be a complex and resource demanding task. Typically it is solved by using pathfinding algorithms. We investigate an alternative approach based on Artificial Potential Fields and show how an APF based navigation system can be used without any need of pathfinding algorithms. In RTS games players usually have a limited visibility of the game world, known as Fog of War. Bots on the other hand often have complete visibility to aid the AI in making better decisions. We show that a Multi-Agent PF based bot with limited visibility can match and even surpass bots with complete visibility in some RTS scenarios. We also show how the bot can be extended and used in a full RTS scenario with base building and unit construction. In the next section we propose a flexible and expandable RTS game architecture that can be modified at several levels of abstraction to test different techniques and ideas. The proposed architecture is implemented in the famous RTS game StarCraft, and we show how the high-level architecture goals of flexibility and expandability can be achieved. In the last section we present two studies related to gameplay experience in RTS games. In games players usually have to select a static difficulty level when playing against computer oppo- nents. In the first study we use a bot that during runtime can adapt the difficulty level depending on the skills of the opponent, and study how it affects the perceived enjoyment and variation in playing against the bot. To create bots that are interesting and challenging for human players a goal is often to create bots that play more human-like. In the second study we asked participants to watch replays of recorded RTS games between bots and human players. The participants were asked to guess and motivate if a player was controlled by a human or a bot. This information was then used to identify human-like and bot-like characteristics for RTS game players.

<p>Artificial intelligence (AI) is already a fundamental component of computer games. In this context is emotions a growing part in simulating real life. The proposed emotional state module, provides a way for the game agents to select an action in real-time virtual environments. The modules function has been tested with the open-source strategy game ORTS. This thesis proposes a new approach for the design of an interacting network, similar to a spreading activation system, of emotional states that keeps track of emotion intensities changing and interacting over time. The network of emotions can represent any number of persisting states, such as moods, emotions and drives. Any emotional signal can affect every state positively or negatively. The states' response to emotional signals are influenced by the other states represented in the network. The network is contained within an emotional state module. This interactions between emotions are not the focus of much research, neither is the representation model. The focus tend to be on the mechanisms eliciting emotions and on how to express the emotions.</p>

Artificial intelligence (AI) is already a fundamental component of computer games. In this context is emotions a growing part in simulating real life. The proposed emotional state module, provides a way for the game agents to select an action in real-time virtual environments. The modules function has been tested with the open-source strategy game ORTS. This thesis proposes a new approach for the design of an interacting network, similar to a spreading activation system, of emotional states that keeps track of emotion intensities changing and interacting over time. The network of emotions can represent any number of persisting states, such as moods, emotions and drives. Any emotional signal can affect every state positively or negatively. The states' response to emotional signals are influenced by the other states represented in the network. The network is contained within an emotional state module. This interactions between emotions are not the focus of much research, neither is the representation model. The focus tend to be on the mechanisms eliciting emotions and on how to express the emotions.

Real-Time Strategy (RTS) is a sub-genre of strategy games which is running in real-time, typically in a war setting. The player use workers to gather resources, which in turn is used for creating new buildings, training combat units and build upgrades and research. The game is won when all buildings of the opponents have been destroyed. The numerous tasks that need to be handled in real-time can be very demanding for a player. Computer players (bots) for RTS games face the same challenges, and also have to navigate units in highly dynamic game worlds and deal with other low-level tasks such as attacking enemy units within fire range. This thesis is a compilation of nine papers. The first four papers deal with navigation in dynamic game worlds, which can be very complex and resource demanding. Typically it is solved by using pathfinding algorithms. We investigate an alternative approach based on Artificial Potential Fields and show how a PF based navigation system can be used without any need of pathfinding algorithms. In RTS games players usually have a limited visibility of the game world, known as Fog of War. Bots on the other hand often have complete visibility to aid the AI in making better decisions. In a paper we show that a Multi-Agent PF based bot with limited visibility can match and even surpass bots with complete visibility in some RTS scenarios. In the sixth paper we show how the bot can be extended and used in a full RTS scenario with base building and unit construction. This is followed by a paper where we propose a flexible and expandable RTS game architecture that can be modified at several levels of abstraction to test different techniques and ideas. The proposed architecture is implemented in the famous RTS game StarCraft, and we show how the high-level architecture goals of flexibility and expandability can be achieved. The last two papers present two studies related to gameplay experience in RTS games. In games players usually have to select a static difficulty level when playing against computer opponents. In the first study we use a bot that during runtime can adapt the difficulty level depending on the skills of the opponent, and study how it affects the perceived enjoyment and variation in playing against the bot. To create bots that are interesting and challenging for human players a goal is often to create bots that play more human-like. In the second study we asked participants to watch replays of recorded RTS games between bots and human players. The participants were asked to guess and motivate if a player was controlled by a human or a bot. This information was then used to identify human-like and bot-like characteristics for RTS game players.

Title: Bridging Pogamut and Defcon Author: Bc. Radek Píbil Department: Department of Software and Computer Science Education Supervisor of the master thesis: Mgr. Jakub Gemrot Abstract: In this thesis we are going to discuss the support of Pogamut AI frame- work for the Defcon PC game. Defcon is a multiplayer real-time strategy putting player into control of one part of the world's sea force, air force and nuclear ar- senal. We are going to cover five main topics. First is concerned with bridging Pogamut and Defcon. Next discusses provided algorithms useful for agent pro- gramming for such a kind of environment. Third describes the implementation of a purely Java agent. Fourth shows an implementation using Jason MAS frame- work. Final is going to evaluate the performance of the agents. Our main reason for bridging Pogamut is that as the gaming AI becomes more and more prominent in academia, more and more computer games allow programmers to implement their own AI. Pogamut AI platform follows this trend by expanding into two new environments Starcraft and Defcon, which introduce real-time strategy environ- ments to Pogamut, whose origins are in first-person shooters. Keywords: Defcon, Artificial Intelligence, Real-Time Strategy Games, Pogamut

Clausewitz and Jomini are two big figures in the military science community. Their principles of the concentration of force are still prevalent today in the further development of principles. With this in mind there have been some studies where computer games have been used to improve military training. The study aims to study how the principles of war can lead to victory in the real-time strategy game Starcraft II. The purpose of this study to contribute to the body of scientific knowledge with using computer games to increase the understanding of the principles of war. The method used in this study is a quantitative content analysis to gather data for analysis in the SPSS-program. The results of the study were that all the use of principles that were chosen, with the exception of surprise, correlated with victory. It also showed that spatial ability leads to higher win probability, this find lowers the reliability of the study since it cannot prove to what extent this leads to victory. However, this result is not applicable in the physical world, it can only contribute to a theoretical understanding of the principles of war.

<p>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.</p><p>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.</p><p>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.</p><p>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.</p><p>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.</p>

<p>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.</p><p>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.</p><p>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.</p>