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Artificial Intelligence: Neural Networks


Constructing Adaptive AI Using Knowledge-Based Neuroevolution

Ryan Cornelius, Kenneth O. Stanley, and Risto Miikkulainen (The University of Texas at Austin)
AI Game Programming Wisdom 3, 2006.
Abstract: Machine learning can increase the appeal of videogames by allowing non-player characters (NPCs) to adapt to the player in real-time. Although techniques such as real-time NeuroEvolution of Augmenting Topologies (rtNEAT) have achieved some success in this area by evolving artificial neural network (ANN) controllers for NPCs, rtNEAT NPCs are not smart out-of-the-box and significant evolution is often required before they develop even a basic level of competence. This article describes a technique that solves this problem by allowing developers to convert their existing finite state machines (FSMs) into functionally equivalent ANNs that can be used with rtNEAT. This means that rtNEAT NPCs will start out with all the abilities of standard NPCs and be able to evolve new behaviors of potentially unlimited complexity.

Artificial Neural Networks on Programmable Hardware

Thomas Rolfes
Game Programming Gems 4, 2004.

How to Build Neural Networks for Games

Penny Sweetser (School of ITEE, University of Queensland)
AI Game Programming Wisdom 2, 2003.
Abstract: Neural networks are a machine learning technique inspired by the human brain. They are a flexible technique that has a wide range of applications in a variety of industries. This article will first introduce neural networks, describing their biological inspiration. Then, it will describe the important components of neural networks and demonstrate how they can be implemented with example code. Next, it will explain how neural networks can be trained, both in-game and prior to shipping, and how a trained neural network can be used for decision-making, classification and prediction. Finally, it will discuss the various applications of neural networks in games, describing previous uses and giving ideas for future applications. Each of these sections will be illustrated with relevant game examples and sample code where appropriate.

Imitating Random Variations in Behavior using a Neural Network

John Manslow
AI Game Programming Wisdom, 2002.
Abstract: As game AI has increased in sophistication, it has become possible to create computer controlled agents that display remarkably human-like behavior. One of the few indications that an agent is non-organic is the frequently clinical nature of their actions, an effect exacerbated by the often ad hoc mechanisms used to add random variations. This article shows how neural networks can be taught to imitate the actual random variations in behavior that are exhibited by real people. This makes it possible to simulate the playing styles of different sports personalities in unprecedented detail - even the extent to which, for example, the cueing direction and position of the cue ball relative to the cushion affect the accuracy of a pool player's shots. The article assumes minimal knowledge of neural networks and illustrates the techniques through their application to a real game. The CD that accompanies the book contains all the source code for the game, along with that for the neural network class, which is designed as a plug-in component that can easily be transferred to other applications.

The Dark Art of Neural Networks

Alex J. Champandard (Artificial Intelligence Depot)
AI Game Programming Wisdom, 2002.

Using a Neural Network in a Game: A Concrete Example

John Manslow
Game Programming Gems 2, 2001.
Abstract: Neural networks are a powerful artificial intelligence technique that are based on an abstraction of the neurocomputational functions of the human brain. One of their most important characteristics is that they can learn by example, and do not need to be programmed in the conventional sense. For example, Codemasters (the developers of Colin McRae Rally 2.0) discovered that a neural network could learn how to drive a rally car by imitating the developers' play, thus avoiding the need to construct a complex set of rules. This article guides the reader through all the steps that are necessary to incorporate neural networks into their own game. Assuming no prior understanding, the article presents a case study of applying one of the most popular, easy to use, and effective neural networks, the multilayer perceptron, to a real game. All the steps required for successful neural network development are described, as are the most common problems, and their solutions. The CD that accompanies the book includes all the source code for the game, and the neural network class that lies at the heart of its AI. The class is designed to be used as a drop-in module in other games and hence contains no application specific code.

A Neural-Net Primer

André LeMothe (Xtreme Games)
Game Programming Gems, 2000.

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