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Artificial Intelligence: State of the Industry and History


#define GAME_AI

Steve Rabin (Nintendo of America)
AI Summit GDC 2009   
Abstract: This introductory presentation sets the stage for the AI Summit by discussing what we mean by game AI, along with some highlights of game AI over the years. We'll look at where it's all headed, what challenges we should be working on, and how the presenters of the AI Summit will help inspire and challenge us all to take game AI to the next level.

Structure vs. Style (Photoshop of AI)

Chris Hecker (EA Maxis)
GDC 2008 (free PowerPoint & Audio recording).
Abstract: My 2008 Game Developers Conference lecture was titled Structure vs. Style, wherein I analyzed how we solve what I call "hard interactive problems". This lecture became mildly famous in game Artificial Intelligence circles because in it I posit we will eventually have a Photoshop of AI, whatever that means! The lecture talks about the characteristics I think a tool like this will need to possess to be worthy of the name, but it's very hard to know what it really means, or how to get there.

Common Game AI Techniques

Steve Rabin (Nintendo of America)
AI Game Programming Wisdom 2, 2003.
Abstract: This article provides a survey of common game AI techniques that are well known, understood, and widely used. Each technique is explained in simplest terms along with references for delving deeper. Techniques include A* Pathfinding, Command Hierarchies, Dead Reckoning, Emergent Behavior, Flocking, Formations, Influence Mapping, Level-of-Detail AI, Manager Task Assignment, Obstacle Avoidance, Scripting, State Machines, Stack-Based State Machines, Subsumption Architectures, Terrain Analysis, and Trigger Systems.

Promising Game AI Techniques

Steve Rabin (Nintendo of America)
AI Game Programming Wisdom 2, 2003.
Abstract: This article provides a survey of promising game AI techniques that are on the forefront of game AI. Each technique is explained in simplest terms along with references for delving deeper. Techniques include Bayesian Networks, Blackboard Architectures, Decision Tree Learning, Filtered Randomness, Fuzzy Logic, Genetic Algorithms, N-Gram Statistical Prediction, Neural Networks, Perceptrons, Planning, Player Modeling, Production Systems, Reinforcement Learning, Reputation Systems, Smart Terrain, Speech Recognition and Text-to-Speech, and Weakness Modification Learning.

The Evolution of Game AI

Paul Tozour (Ion Storm Austin)
AI Game Programming Wisdom, 2002.
Abstract: This article provides a big-picture summary of game AI: what it is, where it's going, where it's been, how it can grow, and what makes it so different from any other discipline. We give a broad overview of the evolution of AI in games since the birth of the videogame, as well as the evolution of a game AI within the scope of a game's development. We also describe many of the academic AI techniques that have been applied to games, explain the important distinctions between the needs and approaches mainstream AI and game AIs of various genres, and discuss some of the ways that game AI technologies are likely to grow in the future.

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