Automatic Game AI Design

International Journal of Computer Applications

Computer games are an increasingly popular application for Artificial Intelligence(AI) research. This paper discusses some of the most interesting components and challenges faced by developers in designing and creation of a game based on artificial intelligence. Game AI provides players a richer gaming experience by going beyond scripted interactions, responsive interaction systems that are adaptive and intelligent.

Proceedings of the AAAI Conference on Artificial Intelligence

The General Video Game AI framework and competition pose the problem of creating artificial intelligence that can play a wide, and in principle unlimited, range of games. Concretely, it tackles the problem of devising an algorithm that is able to play any game it is given, even if the game is not known a priori. This area of study can be seen as an approximation of General Artificial Intelligence, with very little room for game-dependent heuristics. This short paper summarizes the motivation, infrastructure, results and future plans of General Video Game AI, stressing the findings and first conclusions drawn after two editions of our competition, and outlining our future plans.

2021

1-3Student, Information Technology, Xavier Institute of Engineering, Mumbai, India 4Assistant Professor, Department of Information Technology, Xavier Institute of Engineering, Mumbai, India -----------------------------------------------------------------------------***---------------------------------------------------------------------------Abstract Artificial intelligence has been a growing resource for video games for years now. Most video games, whether they’re racing games, shooting games, or strategy games, have various elements that are controlled by AI, such as the enemy bots or neutral characters. Even the ambiguous characters that don’t seem to be doing much are programmed to add more depth to the game, and to give you clues about what your next steps should be. AI in video games is a distinct subfield and differs from academic AI. It serves to improve the game-player experience rather than machine learning or decision making. During the golden age of arcade video games, ...

2006

Video games provide an opportunity and challenge for the soft computational intelligence methods like the symbolic games did for "good old-fashioned artificial intelligence." This article reviews the achievements and future prospects of one particular approach, that of evolving neural networks, or neuroevolution. This approach can be used to construct adaptive characters in existing video games, and it can serve as a foundation for a new genre of games based on machine learning. Evolution can be guided by human knowledge, allowing the designer to control the kinds of solutions that emerge and encouraging behaviors that appear visibly intelligent to the human player. Such techniques may allow building video games that are more engaging and entertaining than current games, and those that can serve as training environments for people. Techniques developed in these games may also be widely applicable in other fields, such as robotics, resource optimization, and intelligent assistants. *

2021

This work will explore three methods of machine learning that make it possible to train an algorithm to the extent that it can play the video game Super Mario and outperform human players. The aim is to find out which method of machine learning is best suited and what the differences are.

The most important case in artificial intelligence (AI) is AI development for all types of games, especially for Game-AI. A numbering of changes are occurring in computer game production: continuous online games, digital division of gates and stands, social and mobile games. Game AI agents should occur as "maker" able for managing a long-running group of live games, their player communities, and real-world context. In this article narrative review, I am compared and summarized the all relation and Chronological processing between AI and game development, in generally comparisons in design stage and construction step. The objective in this work presents the roles of AI in game production and advancing artificially, especially intelligent agent game. The methods in intelligent agent games are all algorithms and techniques but the first matter with teaching artificial intelligence (AI) for games is that numerous AI algorithms work in notion, but have production consequences in terms of speed or memory or performance when really applied in a game. In Conclusion, The main function of Artificial Intelligence (AI) in games production is winning importance and often affects the success or failure of a game.

This paper presents how artificial intelligence (AI) is used in computer games to solve common problems and provide game features. Specifically, non-playing character (NPC) path finding, decision making and learning are examined. Different AI techniques are looked at as to how they help provide a solution to these problems and features in computer games. This discussion is followed by a survey of research articles regarding the different type of AI techniques presented.

An artificial neural network is a system that tries in various degrees to emulate a human brain in order to perform tasks that other computer systems are usually not fit to handle. Artificial neural networks are used in many different areas due to their ability to learn and adapt to many different tasks and make complex predictions. In gaming, computer controlled opponent behavior is usually rule based and dependent on specific conditions and can thus be predictable to a certain degree. As the field of AI and learning systems using artificial neural networks is being developed and expanded, it is inevitable that its use in gaming will be explored thoroughly. This short survey looks at the attempts of using artificial neural networks for opponents in board games and modern computer games, as well as other uses in gaming throughout the last 20 years.

regarding AI core

2011

For now over a decade, real-time strategy (RTS) games have been challenging intelligence, human and artificial (AI) alike, as one of the top genre in terms of overall complexity. RTS is a prime example problem featuring multiple interacting imperfect decision makers. Elaborate dynamics, partial observability, as well as a rapidly diverging action space render rational decision making somehow elusive. Humans deal with the complexity using several abstraction layers, taking decisions on different abstract levels. Current agents, on the other hand, remain largely scripted and exhibit static behavior, leaving them extremely vulnerable to flaw abuse and no match against human players. In this paper, we propose to mimic the abstraction mechanisms used by human players for designing AI for RTS games. A non-learning agent for StarCraft showing promising performance is proposed, and several research directions towards the integration of learning mechanisms are discussed at the end of the paper.