|
Artificial Intelligence: State of the Industry and History
Common Game AI Techniques |
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 |
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.
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.
|
