Developing an Effective and Efficient Real Time Strategy Agent for Use as a Computer Generated Force

Computer Generated Forces (CGF) are used to represent units or individuals in military training and constructive simulation. The use of CGF significantly reduces the time and money required for effective training. For CGF to be effective, they must behave as a human would in the same environment. Real Time Strategy (RTS) games place players in control of a large force whose goal is to defeat the opponent. The military setting of RTS games makes them an excellent platform for the development and testing of CGF. While there has been significant research in RTS agent development, most of the developed agents are only able to exhibit good tactical behavior, lacking the ability to develop and execute overall strategies. By analyzing prior games played by an opposing agent, an RTS agent can determine the opponent\u27s strengths and weaknesses and develop a strategy which neutralizes the strengths and capitalizes on the weaknesses. It can then execute this strategy in an RTS game. This research develops such an RTS agent called the Killer Bee Artificial Intelligence (KBAI). KBAI builds a classifier for an opposing RTS agent which allows it to predict game outcomes. It then takes this classifier, uses it to generate an effective counter-strategy, and executes the tactics required for the strategy. KBAI is both effective and efficient against four high-quality scripted agents: it wins 100% of the time, and it wins quickly. When compared to native artificial intelligence, KBAI has superior performance. It exhibits strategic behavior, as well as the tactics required to execute a developed strategy

Swarm intelligence for autonomous cooperative agents in battles for real-time strategy games

This paper investigates the use the swarm intelligence of honey bees to create groups of co-operative AI for an RTS game in order to create and re-enact battle simulations. The behaviour of the agents are based on the foraging and defensive behaviours of honey bees, adapted to a human environment. The groups consist of multiple model-based reflex agents, with individual blackboards for working memory, with a colony level blackboard to mimic the foraging patterns. An agent architecture and environment is proposed that allows for creation of autonomous cooperative agents. The behaviour of agents is then evaluated and their intelligence is tested using an adaptation of Anytime Universal Intelligence Test

The Creative and Reflexive Realms of Gamaturgy

This article introduces the synergy between theatre, games, and social activism that I have coined “Gamaturgy.” Gamaturgy, in both the creative and reflexive realms, as I describe them, is derived from theatrical dramaturgy and provides new ideas for creating and critically analyzing serious videogames, especially social issue games. First, I sketch out the formative dramaturgical influences from Augusto Boal’s Forum Theatre, Paulo Friere's transitive pedagogy, and Victor Turner's concepts of the liminoid and social justice. I then expand this unique way of play-making into the realm of creative gamaturgy as a way of creating experiential interactions and constructing meanings in the design and creation of serious videogames. As for the aim of finding a new form of thematic analysis for videogames, I use my original case study The Pipeline Pinball Energy Thrill Ride Game to demonstrate a method of recovering meanings from games through the implementation of reflexive gamaturgy

Chain of command in autonomous cooperative agents for battles in real-time strategy games

This paper investigates incorporating chain of command in swarm intelligence of honey bees to create groups of ranked co-operative autonomous agents for an RTS game in to create and re-enact battle simulations. The behaviour of the agents are based on the foraging and defensive behaviours of honey bees, adapted to a human environment. The chain of command is implemented using a hierarchical decision model. The groups consist of multiple model-based reflex agents, with individual blackboards for working memory, with a colony level blackboard to mimic the foraging patterns and include commands received from ranking agents. An agent architecture and environment are proposed that allows for creation of autonomous cooperative agents. The behaviour of agents is then evaluated both mathematically and empirically using an adaptation of anytime universal intelligence test and agent believability metric

Creating AI characters for fighting games using genetic programming

This paper proposes a character generation approach for the M.U.G.E.N. fighting game that can create engaging AI characters using a computationally cheap process without the intervention of the expert developer. The approach uses a Genetic Programming algorithm that refines randomly generated character strategies into better ones using tournament selection. The generated AI characters were tested by twenty-seven human players and were rated according to results, perceived difficulty and how engaging the gameplay was. The main advantages of this procedure are that no prior knowledge of how to code the strategies of the AI character is needed and there is no need to interact with the internal code of the game. In addition, the procedure is capable of creating a wide diversity of players with different strategic skills, which could be potentially used as a starting point to a further adaptive process

Meta-learning computational intelligence architectures

In computational intelligence, the term \u27memetic algorithm\u27 has come to be associated with the algorithmic pairing of a global search method with a local search method. In a sociological context, a \u27meme\u27 has been loosely defined as a unit of cultural information, the social analog of genes for individuals. Both of these definitions are inadequate, as \u27memetic algorithm\u27 is too specific, and ultimately a misnomer, as much as a \u27meme\u27 is defined too generally to be of scientific use. In this dissertation the notion of memes and meta-learning is extended from a computational viewpoint and the purpose, definitions, design guidelines and architecture for effective meta-learning are explored. The background and structure of meta-learning architectures is discussed, incorporating viewpoints from psychology, sociology, computational intelligence, and engineering. The benefits and limitations of meme-based learning are demonstrated through two experimental case studies -- Meta-Learning Genetic Programming and Meta- Learning Traveling Salesman Problem Optimization. Additionally, the development and properties of several new algorithms are detailed, inspired by the previous case-studies. With applications ranging from cognitive science to machine learning, meta-learning has the potential to provide much-needed stimulation to the field of computational intelligence by providing a framework for higher order learning --Abstract, page iii

Artificial Societies, Virtual Worlds, and Their Meaningful Integration

Artificial societies and virtual worlds are two areas of interest to modern social scientists that are distinctly separate in modern academic study, and are yet undeniably related. Artificial societies are multi-agent systems comprised of autonomous social agents, programmed with their own set of rules and behavior. While virtual worlds are occupied in large part by human controlled agents participating in a collective virtual experience and space. Within both types of virtual environments there can be found a scarcity of resources and intricate cross-entity interaction. This often results in the development and evolution of complex economic and cultural structures. In addition, by examining the modern research and common history shared by each field, it is possible to compile a set of shared attributes. This work attempts to capitalize on these shared features and promote a new type of integrated analysis that holds potential for future development in both fields. The concrete implementation of these ideas takes form as a simple economic model containing meaningful computer and human interaction as well as a framework designed for future extensibility

PENGEMBANGAN PERILAKU KARAKTER PEMANCING PADA GAME BERBURU KOI BERBASIS SISTEM MULTI AGENT

ABSTRAK Game adalah permainan terstruktur pada sebuah sistem dimana pemain terlibat dalam konflik buatan. Game sangat banyak jenisnya ada FPS (First Person Shooter), RPG (Role Play Game), dll. Dalam Game Berburu Koi ini termasuk jenis game RPG. Dalam pembuatan game berburu koi adalah berbasis system multi agent dan pengembangan karakter pemancing hanya di non playable charater. NPC yang terdapat pada game berburu koi yaitu pemancing NPC, ikan koi NPC, dan ikan piranha NPC. Memancing merupakan hobi kebanyakan orang, karena memancing dapat mengajari kita kesabaran, kecepatan, dan memahami gerakan seekor ikan, sehingga penulis membuat game berburu koi dimana pemain user lebih tertantang untuk melawan pemancing NPC dan piranha sebagai halangan untuk menangkap ikan koi. Pembuatan game ini menggunakan software unity. Dengan memainkan game ini pemain merasa terhibur sebesar 85.57% dengan pengujian survey dan skala likert yang dilakukan. Kata kunci : system multi agent, non playable charater, software unit

Editorial

calls & calendarEDITORIA

Internet Memes as Instruments of Subversion in the Context of Islam and Muslims

This research investigates the nature of internet memes as instruments of subversion in the context of Islam and Muslims. For the purpose of this research, internet memes including Twitter hashtags have been conceived as idea units. The study employed network analysis to examine roughly 208,000 Twitter hashtags related to Islam and Muslims. Based on this data, actor and hashtag networks were created in order to understand the relationship between leading actors, co- occurring hashtags, dominant discursive practices, and their subversion. Thematic analysis of internet memes was also undertaken in order to study the visual and textual elements in the larger context in which the memes were set. Two major themes emerged: ‘Everyday life and Lived Religion’, and ‘Terrorism, Security and Surveillance’. The study provides evidence of agency of individuals to create fissures in the institutional narratives by reappropriating and subverting the popular symbols originally created by social structures as well as creating their own set of language which is unique to the format of internet memes. The findings derived from the network analysis as well as the thematic analysis also demonstrated the relevance of Richard Dawkins’s (1976) gene-meme analogy