Artificial Intelligence for Games
By I. Millington. 2006
154 citations recorded by the source corpus.
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
Subfields: Artificial Intelligence
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Sebastian Risi
- Andrea Soltoggio
- Kenneth O. Stanley
- Julian Togelius
- Jeff Clune
- Joel Lehman
- Sergey Karakovskiy
- Bongwon Suh
Citations in the corpus
- Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, methodology): “, when applying AI to games (Allis et al., 1994; Millington and Funge, 2016) or articulated robotic tasks.”
- The 2009 Mario AI Competition (2010, background): “With a heuristic that overestimates by x, the path will be at most x too long[ 8 ].”
- I Lead, You Help but Only with Enough Details: Understanding User Experience of Co-Creation with Artificial Intelligence (2018, background): “It will not only assist humans in repetitive and arduous tasks but also take on complex and elaborative works [32, 38, 49].”
- A Survey of Behavior Trees in Robotics and AI (2020, unknown): “In fact, the absence of a reactive capability was pointed out as the main drawback of BTs in [104], where a BT formulation without running was used.”