- Preface
- Acknowledgments
- Web Materials
- Editors
- Contributors
- Section I - General Wisdom
- Chapter 1 - Game AI Appreciation, Revisited
- Chapter 2 - Combat Dialogue in FEAR: The Illusion of Communication
- Chapter 3 - Dual-Utility Reasoning
- Chapter 4 - Vision Zones and Object Identification Certainty
- Chapter 5 - Agent Reaction Time: How Fast Should an AI React?
- Chapter 6 - Preventing Animation Twinning Using a Simple Blackboard
- Section II - Architecture
- Chapter 7 - Possibility Maps for Opportunistic AI and Believable Worlds
- Chapter 8 - Production Rules Implementation in 1849
- Chapter 9 - Production Systems: New Techniques in AAA Games
- 9.1 Introduction
- 9.2 What Decisions Is the System Trying to Make?
- 9.3 Choice of Rules Representation
- 9.4 Method of Rules Authoring
- 9.5 Choice of Matching System
- 9.6 What Happens If the AI Fails to Find a Rule?
- 9.7 What Happens If There Are Multiple Rules?
- 9.8 Execution and the Design of the RHS
- 9.9 Debugging and Tuning
- 9.10 Conclusion
- References
- Chapter 10 - Building a Risk-Free Environment to Enhance Prototyping: Hinted-Execution Behavior Trees
- Chapter 11 - Smart Zones to Create the Ambience of Life
- Chapter 12 - Separation of Concerns Architecture for AI and Animation
- Chapter 13 - Optimizing Practical Planning for Game AI
- Section III - Movement and Pathfinding
- Chapter 14 - JPS+: An Extreme A* Speed Optimization for Static Uniform Cost Grids
- Chapter 15 - Subgoal Graphs for Fast Optimal Pathfinding
- Chapter 16 - Theta* for Any-Angle Pathfinding
- Chapter 17 - Advanced Techniques for Robust, Efficient Crowds
- 17.1 Introduction
- 17.2 Pathfinding’s Utopian Worldview
- 17.3 Congestion Map Approach
- 17.4 Augmenting Path Planning with Congestion Maps
- 17.5 Path Smoothing
- 17.6 Flow Fields with Congestion Maps and Theta
- 17.7 Current Alternatives
- 17.8 Benefits
- 17.9 Drawbacks
- 17.10 Performance Considerations
- 17.11 Future Work
- 17.12 Conclusion
- References
- Chapter 18 - Context Steering: Behavior-Driven Steering at the Macro Scale
- Chapter 19 - Guide to Anticipatory Collision Avoidance
- Chapter 20 - Hierarchical Architecture for Group Navigation Behaviors
- Chapter 21 - Dynamic Obstacle Navigation in Fuse
- Section IV - Applied Search Techniques
- Chapter 22 - Introduction to Search for Games
- Chapter 23 - Personality Reinforced Search for Mobile Strategy Games
- Chapter 24 - Interest Search: A Faster Minimax
- 24.1 Introduction
- 24.2 Background
- 24.3 Rethinking Selective Search
- 24.4 Selection by Variation: Interest Search
- 24.5 Quantifying the Interest Search Idea
- 24.6 Classifying the Moves in Terms of Interest
- 24.7 Does This Work?
- 24.8 Performance of Interest Search for Treebeard Chess
- 24.9 Applying Interest Search to Japanese Chess (Shogi)
- 24.10 Dynamic Calculation of Score and Interest
- 24.11 Impact of Interest Search on Shogi
- 24.12 Analysis
- 24.13 Other Games
- 24.14 Conclusion
- References
- Chapter 25 - Monte Carlo Tree Search and Related Algorithms for Games
- Chapter 26 - Rolling Your Own Finite-Domain Constraint Solver
- 26.1 Introduction
- 26.2 Simple Example
- 26.3 Algorithm 1: Brute Force
- 26.4 Algorithm 2: Backward Checking
- 26.5 Algorithm 3: Forward Checking
- 26.6 Detecting Inconsistencies
- 26.7 Algorithm 4: Forward Checking with Backtracking and Undo
- 26.8 Gory Implementation Details
- 26.9 Extensions and Optimizations
- 26.10 Conclusion
- References
- Section V - Tactics, Strategy, and Spatial Awareness
- Chapter 27 - Looking for Trouble: Making NPCs Search Realistically
- Chapter 28 - Modeling Perception and Awareness in Tom Clancy’s Splinter Cell Blacklist
- Chapter 29 - Escaping the Grid: Infinite-Resolution Influence Mapping
- 29.1 Introduction
- 29.2 Influence Mapping
- 29.3 Limitations
- 29.4 Point-Based Influence
- 29.5 Making Queries Fast
- 29.6 Temporal Influence Propagation
- 29.7 Handling Obstacles and Nontrivial Topologies
- 29.8 Optimization Queries
- 29.9 Traveling to the Third Dimension
- 29.10 Example Implementation
- 29.11 Suitability Considerations
- 29.12 Conclusion
- References
- Chapter 30 - Modular Tactical Influence Maps
- Chapter 31 - Spatial Reasoning for Strategic Decision Making
- Chapter 32 - Extending the Spatial Coverage of a Voxel-Based Navigation Mesh
- Section VI - Character Behavior
- Chapter 33 - Infected AI in The Last of Us
- Chapter 34 - Human Enemy AI in The Last of Us
- Chapter 35 - Ellie: Buddy AI in The Last of Us
- Chapter 36 - Realizing NPCs: Animation and Behavior Control for Believable Characters
- Chapter 37 - Using Queues to Model a Merchant’s Inventory
- Chapter 38 - Psychologically Plausible Methods for Character Behavior Design
- Section VII - Analytics, Content Generation, and Experience Management
- Chapter 39 - Analytics-Based AI Techniques for a Better Gaming Experience
- Chapter 40 - Procedural Content Generation: An Overview
- 40.1 Introduction
- 40.2 Technical Approaches to Content Generation
- 40.3 Understanding PCG’s Relationship to a Game
- 40.4 Choosing an Approach
- 40.5 Tuning and Debugging a Content Generator
- 40.6 Conclusion
- References
- Chapter 41 - Simulation Principles from Dwarf Fortress
- Chapter 42 - Techniques for AI-Driven Experience Management in Interactive Narratives
Web Materials
Example programs and source code to accompany the chapters are available at http://www.gameaipro.com.
General System Requirements
The following is required to compile and execute the example programs:
- DirectX August 2009 SDK
- DirectX 9.0 compatible or newer graphics card
- Windows 7 or newer
- Visual C++.NET 2012 or newer
Updates
Updates of the example programs and source code will be updated as needed.
Comments and Suggestions
Please send any comments or suggestions to [email protected].
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