The focus of this survey is on research in applying evolutionary and other metaheuristic search algorithms to automatically generating content for games, both digital and nondigital (such as board games). The term search-based procedural content generation is proposed as the name for this emerging field, which at present is growing quickly. A taxonomy for procedural content generation is devised, centering on what kind of content is generated, how the content is represented and how the quality/fitness of the content is evaluated; search-based procedural content generation in particular is situated within this taxonomy. This article also contains a survey of all published papers known to the authors in which game content is generated through search or optimisation, and ends with an overview of important open research problems.

http://julian.togelius.com/Togelius2011Searchbased.pdf

Search-Based Procedural Content Generation: A Taxonomy and Survey

Grids with blocked and unblocked cells are often used to represent terrain in robotics and video games. However, paths formed by grid edges can be longer than true shortest paths in the terrain since their headings are artificially constrained. We present two new correct and complete anyangle path-planning algorithms that avoid this shortcoming. Basic Theta* and Angle-Propagation Theta* are both variants of A* that propagate information along grid edges without constraining paths to grid edges. Basic Theta* is simple to understand and implement, fast and finds short paths. However, it is not guaranteed to find true shortest paths. Angle-Propagation Theta* achieves a better worst-case complexity per vertex expansion than Basic Theta* by propagating angle ranges when it expands vertices, but is more complex, not as fast and finds slightly longer paths. We refer to Basic Theta* and Angle-Propagation Theta* collectively as Theta*. Theta* has unique properties, which we analyze in detail. We show experimentally that it finds shorter paths than both A* with post-smoothed paths and Field D* (the only other version of A* we know of that propagates information along grid edges without constraining paths to grid edges) with a runtime comparable to that of A* on grids. Finally, we extend Theta* to grids that contain unblocked cells with non-uniformtraversal costs and introduce variants of Theta* which provide different tradeoffs between path length and runtime.

/pdf/theta-any-angle-path-planning-on-grids-2lbeowtap2.pdf

Theta*: any-angle path planning on grids

AI and Games Introduction What Is AI? Model of Game AI Algorithms, Data Structures, and Representations On the Website Layout of the Book Game AI The Complexity Fallacy The Kind of AI in Games Speed and Memory The AI Engine Techniques Movement The Basics of Movement Algorithms Kinematic Movement Algorithms Steering Behaviors Combining Steering Behaviors Predicting Physics Jumping Coordinated Movement Motor Control Movement in the Third Dimension Pathfinding The Pathfinding Graph Dijkstra A* World Representations Improving on A* Hierarchical Pathfinding Other Ideas in Pathfinding Continuous Time Pathfinding Movement Planning Decision Making Overview of Decision Making Decision Trees State Machines Behavior Trees Fuzzy Logic Markov Systems Goal-Oriented Behavior Rule-Based Systems Blackboard Architectures Scripting Action Execution Tactical and Strategic AI Waypoint Tactics Tactical Analyses Tactical Pathfinding Coordinated Action Learning Learning Basics Parameter Modification Action Prediction Decision Learning Naive Bayes Classifiers Decision Tree Learning Reinforcement Learning Artificial Neural Networks Board Games Game Theory Minimaxing Transposition Tables and Memory Memory-Enhanced Test Algorithms Opening Books and Other Set Plays Further Optimizations Turn-Based Strategy Games Supporting Technologies Execution Management Scheduling Anytime Algorithms Level of Detail World Interfacing Communication Getting Knowledge Efficiently Event Managers Polling Stations Sense Management Tools and Content Creation Knowledge for Pathfinding and Waypoint Tactics Knowledge for Movement Knowledge for Decision Making The Toolchain Designing Game AI Designing Game AI The Design Shooters Driving Real-Time Strategy Sports Turn-Based Strategy Games AI-Based Game Genres Teaching Characters Flocking and Herding Games Appendix Books, Periodicals, and Papers Games

Artificial Intelligence for Games

https://www.cc.gatech.edu/~athomaz/papers/ThomazBreazeal-AIJ3561.pdf

Teachable robots: Understanding human teaching behavior to build more effective robot learners

Classic approaches to game AI require either a high quality of domain knowledge, or a long time to generate effective AI behaviour. These two characteristics hamper the goal of establishing challenging game AI. In this paper, we put forward Monte-Carlo Tree Search as a novel, unified framework to game AI. In the framework, randomized explorations of the search space are used to predict the most promising game actions. We will demonstrate that Monte-Carlo Tree Search can be applied effectively to (1) classic board-games, (2) modern board-games, and (3) video games.

/pdf/monte-carlo-tree-search-a-new-framework-for-game-ai-ksklydgb5g.pdf

Monte-carlo tree search: a new framework for game AI

From the Publisher:
Learn how AI experts create intelligent game objects and characters with this firstvolume in the AI Game Programming Wisdom series. This unique collection of articles gives programmers and developers access to the insights and wisdom of over thirty AI pros. Each article delves deep into key AI game programming issues and provides insightful new ideas and techniques that can be easily integrated into your own games. Everything from general AI architectures, rule based systems, level-of-detail AI, scripting language issues, to expert systems, fuzzy logic, neural networks, and genetic algorithms are thoroughly covered. If you're a game programmer (AI/logic, front-end, user interface, tools, graphics, etc.) this comprehensive resource will help you take your skills and knowledge to the next level. 
KEY FEATURES 
- Contains new AI techniques and solutions written by over thirty industry experts 
- Provides comprehensive coverage of all aspects of AI programming 
- Includes ready-to-use ideas and code 
- Provides skill enhancement for beginning/ intermediate programmers, and insightful new ideas for the pros 
- Companion CD includes all code for easy implementation 
Author Biography: Steve Rabin is a 10-year video game industry veteran working at Nintendo of America. He's written AI for three published games and was a contributor to both Game Programming Gems 1 and 2. He was also the AI section editor for Game Programming Gems 2. He's spoken on AI at the Game Developers Conference and holds a degree in Computer Engineering from the University of Washington, where he specialized in robotics.

AI game programming wisdom

Based on the most recent curriculum guidelines of the IGDA, updated in 2008, "Introduction to Game Development, Second Edition" surveys all aspects of the theory and practice of game development, design, and production. Divided into seven independent parts: Critical Game Studies, Game Design, Game Programming (Languages and Architecture), Game Programming Mathematics, Collision Detection, and Physics), Game Programming (Graphics, Animation, Artificial Intelligence, Audio, and Networking), Audio Visual Design and Production, and Game Production and the Business of Games, it features contributions from twenty seven of the leading game developers, programmers, and designers. A must-have resource for anyone looking to understand the entire game development process, the accompanying CD-ROM includes tutorials, animations, images, demos, source code, and PowerPoint lecture slides that reinforce the concepts presented in the book. Benefits: Fully revised to match the current curriculum guidelines created by the IGDA and for current technology. Includes contributions from numerous game industry leaders. The accompany CD-ROM features all source code and images used in the book as wel as tutorials and slides to reinforce concepts learned.

Introduction to Game Development

Game AI Pro2: Collected Wisdom of Game AI Professionals presents cutting-edge tips, tricks, and techniques for artificial intelligence (AI) in games, drawn from developers of shipped commercial games as well as some of the best-known academics in the field. It contains knowledge, advice, hard-earned wisdom, and insights gathered from across the community of developers and researchers who have devoted themselves to game AI. In this book, 47 expert developers and researchers have come together to bring you their newest advances in game AI, along with twists on proven techniques that have shipped in some of the most successful commercial games of the last few years. The book provides a toolbox of proven techniques that can be applied to many common and not-so-common situations. It is written to be accessible to a broad range of readers. Beginners will find good general coverage of game AI techniques and a number of comprehensive overviews, while intermediate to expert professional game developers will find focused, deeply technical chapters on specific topics of interest to them. Covers a wide range of AI in games, with topics applicable to almost any game Touches on most, if not all, of the topics necessary to get started in game AI Provides real-life case studies of game AI in published commercial games Gives in-depth, technical solutions from some of the industrys best-known games Includes downloadable demos and/or source code, available at http://www.gameaipro.com

Game AI Pro 2: Collected Wisdom of Game AI Professionals

An example system and method controls a rotatable object on a display using a handheld control device comprising an accelerometer. The example system and method involve determining an orientation of the handheld control device using an interpolation of an output for a first axis of the accelerometer and an output for a second axis of the accelerometer. The rotation of the rotatable object is controlled based on the determined orientation.

System and method for using accelerometer outputs to control an object rotating on a display

The disclosure provide for improved depiction of interactions between virtual objects and physical objects in augmented reality (AR) images, and may include operations for: receiving a captured image of the real world physical structure, the captured image provided by a camera disposed in a housing; identifying, by image processing performed on the captured image using at least one processor arrangement, the at least one marker disposed at a location of the real world physical structure; determining aspects of user viewpoint of the housing relative to the physical structure at least in part in response to the identified at least one marker; using an at least partial three dimensional model of the real world physical structure to determine a view of interaction between the real world physical structure and the provided virtual object from the determined user viewpoint; and displaying, on a display, an augmented reality view of the captured image including the determined view of the interaction.

Steve Rabin

Papers

AI game programming wisdom

Introduction to Game Development

Game AI Pro 2: Collected Wisdom of Game AI Professionals

System and method for using accelerometer outputs to control an object rotating on a display

Visually Convincing Depiction of Object Interactions in Augmented Reality Images