book

Game AI Pro 2

Name: Game AI Pro 2
Author: Steven Rabin
ISBN: 9781498760423

by Steven Rabin

April 2015

Beginner

577 pages

17h 58m

English

A K Peters/CRC Press

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Preface
Acknowledgments
Web Materials
General System RequirementsUpdatesComments and Suggestions
Editors
Contributors
Section I - General Wisdom
Chapter 1 - Game AI Appreciation, Revisited
1.1 Introduction1.2 What Is Game AI?1.3 Fuzzy Border1.4 AI as the Nexus of Game Development1.5 Frontiers of the Field1.5.1 Pathfinding1.5.2 Conversations1.5.3 Dynamic Storylines1.5.4 Player Modeling1.5.5 Modeling and Displaying Emotion1.5.6 Social Relationships1.5.7 Scale1.5.8 Content Explosion1.5.9 The Unexplored1.6 ConclusionReferences
Chapter 2 - Combat Dialogue in FEAR: The Illusion of Communication
2.1 Introduction2.2 From Barks to Dialogues2.3 ConclusionReferences
Chapter 3 - Dual-Utility Reasoning
3.1 Introduction3.2 Dual-Utility Reasoning3.3 Dual-Utility Reasoning in Zoo Tycoon 23.4 ConclusionReference
Chapter 4 - Vision Zones and Object Identification Certainty
4.1 Introduction4.2 Vision Zones and Object Identification Certainty4.2.1 Vision Sweet Spot4.3 Incorporating Movement and Camouflage4.4 Producing Appropriate Behavior from Identification Certainty4.4.1 Alternative 1: Object Certainty with Time4.4.2 Alternative 2: Explicit Detection Feedback4.5 ConclusionReferences

Chapter 5 - Agent Reaction Time: How Fast Should an AI React?
5.1 Introduction5.2 Context Is Key5.3 What Does Cognitive Research Tell Us?5.3.1 Simple Reaction Time5.3.2 Recognition or Go/No-Go Time5.3.3 Complex Cognitive Task Reaction Time5.4 ConclusionReferences
Chapter 6 - Preventing Animation Twinning Using a Simple Blackboard
6.1 Introduction6.2 Animation Twinning6.3 Animation Blackboard6.4 Delay Times and Animation Considerations6.5 ConclusionReference
Section II - Architecture
Chapter 7 - Possibility Maps for Opportunistic AI and Believable Worlds
7.1 Introduction7.2 What Are Probability and Possibility Maps?7.3 Using Possibility Maps7.4 Updating Probability Maps7.5 Combining Possibility and Probability Maps7.6 Factorizing Game State7.7 Conclusion
Chapter 8 - Production Rules Implementation in 1849
8.1 Introduction8.2 Game Mechanics and Production Rules8.2.1 Game Mechanics8.2.2 Game Simulation8.3 Rule System8.3.1 Resources8.3.2 Resource Bins8.3.3 Conditions and Actions8.3.4 Rule Execution8.4 Performance8.4.1 Condition Checking Frequency8.4.2 Condition Definition Language8.4.3 Data Model8.5 Lessons from 18498.5.1 Benefits8.5.2 Lessons8.6 Related Work8.7 ConclusionReferences
Chapter 9 - Production Systems: New Techniques in AAA Games
9.1 Introduction9.1.1 Terminology9.1.2 Design Considerations9.2 What Decisions Is the System Trying to Make?9.3 Choice of Rules Representation9.4 Method of Rules Authoring9.4.1 Recorded Rule System9.4.2 More Typical Systems9.5 Choice of Matching System9.5.1 Greedy Matching/Selection9.6 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 RHS9.8.1 More Complex RHS9.9 Debugging and Tuning9.10 ConclusionReferences
Chapter 10 - Building a Risk-Free Environment to Enhance Prototyping: Hinted-Execution Behavior Trees
10.1 Introduction10.2 Explaining the Problem10.3 Behavior Trees10.3.1 Simple BT10.3.2 Tree Complexity10.4 Extending the Model10.4.1 Hint Concept10.4.2 HeBT Selectors10.4.3 Hints and Conditions10.5 Multilevel Architecture10.5.1 Behavior Controllers10.5.2 Exposing Hints to Higher Levels10.6 More Complex Example10.6.1 Prototyping New Ideas10.6.2 Base Behavior10.6.3 Prototype Idea10.6.4 Creating a High-Level Tree10.6.5 Analyzing the Results10.7 Other Applications10.7.1 Adaptation10.7.2 Group Behaviors10.8 ConclusionReferences
Chapter 11 - Smart Zones to Create the Ambience of Life
11.1 Introduction11.2 Designing an Ambience of Life11.2.1 What Is a Living Scene?11.2.2 Performing Living Scenes through Smart Zones11.3 Smart Zones in Practice11.3.1 Definition of Smart Zones by Game Designers11.3.2 Functioning of the Smart Zone Architecture11.3.2.1 Trigger Management11.3.2.2 Role Assignment11.3.2.3 Behavior Orchestration11.3.3 NPC Behaviors11.3.3.1 Role Interruption11.3.3.2 Management of Overlapping Zones11.4 Concrete Example11.4.1 Scenario and the Smart Zones11.4.2 Implementation in Unity3D11.5 ConclusionAcknowledgmentsReferences
Chapter 12 - Separation of Concerns Architecture for AI and Animation
12.1 Introduction12.2 Animation Graphs12.3 Complexity Explosion and the Problem of Scalability12.4 SoC12.5 Separating Gameplay and Animation12.6 Animation Behaviors12.7 Animation Controller12.8 Benefits of an SoC Animation Architecture12.8.1 Functional Testing12.8.2 System Refactoring12.8.3 Level of Detail12.9 ConclusionReferences
Chapter 13 - Optimizing Practical Planning for Game AI
13.1 Introduction13.1.1 Required Background13.2 How Can You Optimize?13.2.1 Measure It!13.2.2 Design Valuable Tests!13.2.3 Use Profilers!13.3 Practical Planning Data Structures13.3.1 Actions13.3.2 Plans13.3.3 States13.4 Practical Planning Algorithms13.4.1 Iterating over Subsets of State Predicates13.4.2 Recording Where the Action Parameters Occur in the Action Predicates13.5 ConclusionReferences
Section III - Movement and Pathfinding
Chapter 14 - JPS+: An Extreme A* Speed Optimization for Static Uniform Cost Grids
14.1 Introduction14.2 Pruning Strategy14.3 Forced Neighbors14.4 Jump Points14.4.1 Primary Jump Points14.4.2 Straight Jump Points14.4.3 Diagonal Jump Points14.5 Wall Distances14.6 Map Preprocess Implementation14.7 Runtime Implementation14.8 ConclusionReferences
Chapter 15 - Subgoal Graphs for Fast Optimal Pathfinding
15.1 Introduction15.2 Preliminaries15.3 Simple Subgoal Graphs15.3.1 Constructing Simple Subgoal Graphs15.3.2 Searching Using Simple Subgoal Graphs15.3.3 Identifying All Direct-H-Reachable Subgoals from a Given Cell15.4 Two-Level Subgoal Graphs15.4.1 Constructing Two-Level Subgoal Graphs15.4.2 Searching Using Two-Level Subgoal Graphs15.5 N-Level Graphs15.6 ConclusionAcknowledgmentsReferences
Chapter 16 - Theta* for Any-Angle Pathfinding
16.1 Introduction16.2 Problem Formalization16.3 A* Algorithm16.4 Theta* Algorithm16.5 Theta* Paths16.6 Analysis16.7 ConclusionAcknowledgmentReferences
Chapter 17 - Advanced Techniques for Robust, Efficient Crowds
17.1 Introduction17.2 Pathfinding’s Utopian Worldview17.3 Congestion Map Approach17.4 Augmenting Path Planning with Congestion Maps17.5 Path Smoothing17.6 Flow Fields with Congestion Maps and Theta17.7 Current Alternatives17.8 Benefits17.9 Drawbacks17.10 Performance Considerations17.11 Future Work17.12 ConclusionReferences
Chapter 18 - Context Steering: Behavior-Driven Steering at the Macro Scale
18.1 Introduction18.2 When Steering Behaviors Go Bad18.2.1 Flocks versus Groups18.2.2 Lack of Context18.3 Toward Why, Not How18.3.1 Context Maps18.4 Context Maps by Example18.4.1 Chase Behavior18.4.2 Avoid Behavior18.4.3 Combining and Parsing18.4.4 Subslot Movement18.5 Racing with Context18.5.1 Coordinate System18.5.2 Racing Line Behavior18.5.3 Avoid Behavior18.5.4 Drafting Behavior18.5.5 Processing Context Maps18.6 Advanced Techniques18.6.1 Post-Processing18.6.2 Optimizations18.7 ConclusionReferences
Chapter 19 - Guide to Anticipatory Collision Avoidance
19.1 Introduction19.2 Key Concepts19.2.1 Agent State19.2.2 Predicting Collisions (Time to Collision)19.2.3 Time Horizon19.3 Prototype Implementation19.3.1 Agent Forces19.3.2 Avoidance Force19.3.2.1 Avoidance Force Direction19.3.2.2 Avoidance Force Magnitude19.3.2.3 Corner Cases19.3.2.4 Code19.3.3 Runtime Performance19.3.4 Parameter Tuning19.4 Advanced Approaches19.4.1 Human Motion Simulation19.4.1.1 Personal Space19.4.1.2 Field of View19.4.1.3 Distance to Collision19.4.1.3 Randomized Perturbation19.4.2 Guaranteed Collision Avoidance19.4.3 Herd’Em!19.5 ConclusionReferences
Chapter 20 - Hierarchical Architecture for Group Navigation Behaviors
20.1 Introduction20.2 Group Navigation20.2.1 Flocks20.2.2 Formations20.2.3 Social Groups20.3 Navigation Pipeline Architecture20.3.1 Navigation Behaviors20.3.2 Navigation Pipeline20.4 Group to Members Relationship Model20.4.1 Leader20.4.2 Virtual Group Entity20.4.3 Hierarchical Entity Architecture20.5 Pathfinding20.6 Emergent Group Structure20.6.1 Boids and Derivatives20.6.2 “Local” Formations20.6.3 Implementing an Emergent Group20.7 Choreographed Formations20.7.1 Formation Design20.7.2 Slots Assignment20.7.3 “Blind” Formation Following20.7.4 Autonomous Formation Following20.8 Group Collision Avoidance20.8.1 Velocity Correction20.8.2 Formation Adaptation20.9 ConclusionReferences
Chapter 21 - Dynamic Obstacle Navigation in Fuse
21.1 Introduction21.2 Fuse Traversal Setups21.3 Climb Mesh Generation21.3.1 Path Following on Climb Meshes21.3.2 Caveats of the Climb Path Generation21.4 Parsing Climb Paths21.4.1 Generating Virtual Controller Input21.5 ConclusionReferences
Section IV - Applied Search Techniques
Chapter 22 - Introduction to Search for Games
22.1 Introduction22.2 Illustrative Example22.3 Basic Search Requirements22.3.1 Efficient State Representation22.4 Alternate Uses of Search22.5 Bottlenecks for Search22.6 Conclusion
Chapter 23 - Personality Reinforced Search for Mobile Strategy Games
23.1 Introduction23.2 Turn-Based Strategy War Games Based on “Euro” Board Games23.3 Evolution and the Triune Brain23.4 Personality First23.5 Neocortex23.6 State Evaluation23.7 Limbic Brain23.8 Evolution and the Genetic Algorithm23.9 AI Chromosomes23.10 ConclusionReferences
Chapter 24 - Interest Search: A Faster Minimax
24.1 Introduction24.2 Background24.3 Rethinking Selective Search24.4 Selection by Variation: Interest Search24.5 Quantifying the Interest Search Idea24.6 Classifying the Moves in Terms of Interest24.7 Does This Work?24.8 Performance of Interest Search for Treebeard Chess24.9 Applying Interest Search to Japanese Chess (Shogi)24.10 Dynamic Calculation of Score and Interest24.11 Impact of Interest Search on Shogi24.12 Analysis24.13 Other Games24.14 ConclusionReferences
Chapter 25 - Monte Carlo Tree Search and Related Algorithms for Games
25.1 Introduction25.2 Background25.3 Algorithm 1: Online UCB125.3.1 Applying to Games25.4 Algorithm 2: Regret Matching25.4.1 Applying to Games25.5 Algorithm 3: Offline UCB125.6 Algorithm 4: UCT25.6.1 Important Implementation Details25.6.2 UCT Enhancements and Variations25.6.3 Applying to Games25.7 ConclusionReferences
Chapter 26 - Rolling Your Own Finite-Domain Constraint Solver
26.1 Introduction26.2 Simple Example26.3 Algorithm 1: Brute Force26.4 Algorithm 2: Backward Checking26.5 Algorithm 3: Forward Checking26.6 Detecting Inconsistencies26.7 Algorithm 4: Forward Checking with Backtracking and Undo26.8 Gory Implementation Details26.9 Extensions and Optimizations26.9.1 Finite-Domain Representation26.9.2 Constraint Arcs and the Work Queue26.9.3 Randomized Solutions26.9.4 Variable Ordering26.10 ConclusionReferences
Section V - Tactics, Strategy, and Spatial Awareness
Chapter 27 - Looking for Trouble: Making NPCs Search Realistically
27.1 Introduction27.2 Types of Searching27.2.1 Cautious Search27.2.2 Aggressive Search27.3 Triggering a Search27.3.1 Initial Stimulus-Based Trigger27.3.2 Losing a Target27.4 Phases of Searching27.4.1 Phase 127.4.1.1 Cautious27.4.1.2 Aggressive27.4.2 Phase 227.4.2.1 Generation of Search Spots27.4.2.2 Performing the Search27.4.2.3 Selecting the Best Search Spot27.4.3 Improving Phase 2 Search: Gap Detection27.4.4 Ending the Search27.5 ConclusionReferences
Chapter 28 - Modeling Perception and Awareness in Tom Clancy’s Splinter Cell Blacklist
28.1 Introduction28.1.1 Fairness28.1.2 Consistency28.1.3 Good Feedback28.1.4 Intelligence28.2 Visual Perception28.3 Environmental Awareness28.3.1 Connectivity28.3.2 Changed Objects28.4 Auditory Perception28.4.1 Calculating Audio Distance28.4.2 Auditory Detection Fairness28.5 Social/Contextual Awareness28.5.1 Social Awareness: Conversation28.5.2 Contextual Awareness: Unreachable Area28.5.3 Disappearing NPC Problem28.6 ConclusionReferences
Chapter 29 - Escaping the Grid: Infinite-Resolution Influence Mapping
29.1 Introduction29.2 Influence Mapping29.3 Limitations29.4 Point-Based Influence29.5 Making Queries Fast29.6 Temporal Influence Propagation29.7 Handling Obstacles and Nontrivial Topologies29.8 Optimization Queries29.9 Traveling to the Third Dimension29.10 Example Implementation29.11 Suitability Considerations29.11.1 Influence Source Density29.11.2 Query Point Density29.11.3 Update Frequency29.11.4 Need for Precise or Extremely Accurate Results29.12 ConclusionReferences
Chapter 30 - Modular Tactical Influence Maps
30.1 Introduction30.2 Influence Map Overview30.3 Propagation30.4 Architecture30.4.1 Base Map Structure30.4.2 Types of Base Maps30.4.3 Templates30.4.4 Working Maps30.5 Population of Map Data30.6 Information Retrieval30.6.1 Values at a Point30.6.2 Combinations of Maps30.6.3 Special Functions30.7 Usage30.7.1 Information30.7.2 Targeting30.7.3 Positioning30.8 Examples30.8.1 Location for Area of Effect Attack30.8.2 Movement to Safer Spot30.8.3 Nearest Battlefront Location30.9 ConclusionReferences
Chapter 31 - Spatial Reasoning for Strategic Decision Making
31.1 Introduction31.2 Spatial Partitioning31.2.1 Region Generation31.2.2 Static vs. Dynamic Regions31.3 Working with Regions31.3.1 Picking Places to Scout or Explore31.3.2 Picking Places to Attack31.3.3 Picking Unit Positions31.3.4 Path Planning31.3.5 Distance Estimates31.3.6 Region Path Caching31.3.7 Recognizing Cul-de-Sacs and Chokepoints31.4 Influence Maps31.4.1 Influence Map Basics31.4.2 Propagation Calculations31.4.3 Force Estimates over Regions31.4.4 Illusion of Intelligence31.4.5 Border Calculations31.5 Spatial Characteristics31.5.1 Scent of Death31.5.2 High-Traffic Areas31.5.3 Avenues of Approach31.5.4 Flanking Attacks31.5.5 Attackable Obstacles31.5.6 Counterattacks and Consolidation31.6 Conclusion and Future ThoughtsReferences
Chapter 32 - Extending the Spatial Coverage of a Voxel-Based Navigation Mesh
32.1 Introduction32.2 Overview of Voxel-Based Navmesh Generation32.2.1 Voxel-Based Navmesh Goals32.2.2 Enter Voxelization32.2.4 Triangulating the Polygons32.2.5 Recap32.3 Extending the Navmesh’s Spatial Coverage32.3.1 Reintroducing the Discarded Space32.3.2 Using the Metadata32.4 Identifying Other Spaces32.4.1 Prone Navigation32.4.2 Swim Navigation32.4.3 Sidestep Navigation32.4.4 Multiple Generation Parameter Passes and Hierarchy32.5 Playing with the Heuristic32.6 ConclusionReferences
Section VI - Character Behavior
Chapter 33 - Infected AI in The Last of Us
33.1 Introduction33.2 The Infected33.2.1 Senses33.2.2 Distractions33.3 AI System33.3.1 Philosophy33.3.2 Data-Driven Design33.3.3 Implementation33.3.4 Debugging33.4 Skills and Behaviors33.4.1 Search Skill33.4.2 Chase Skill33.4.3 Follow Skill33.4.4 Ambush Skill33.4.5 Throw Skill33.4.6 On-Fire Skill33.4.7 Wander Skill33.4.8 Sleep Skill33.5 ConclusionReferences
Chapter 34 - Human Enemy AI in The Last of Us
34.1 Introduction34.2 Building Blocks34.3 AI Perception34.4 Cover and Posts34.5 Skills, States, and Behaviors34.6 Stealth34.7 Lethality34.8 Flanking34.9 Polish34.10 Conclusion
Chapter 35 - Ellie: Buddy AI in The Last of Us
35.1 Introduction35.2 Starting from Scratch35.2.1 The Plan35.2.2 Approach35.3 Ambient Following35.3.1 Follow Positions35.3.2 Moving35.3.3 Dodging35.3.4 Teleportation35.4 Taking Cover35.4.1 Runtime Cover Generation35.4.2 Cover Share35.5 Combat Utility35.5.1 Throwing35.5.2 Grapples35.5.3 Gifting35.6 Armed Combat35.6.1 Shooting35.6.2 Balancing35.6.3 Cheating35.7 Finishing Touches35.7.1 Vocalizations35.7.2 Callouts35.7.3 Ambience35.8 Conclusion
Chapter 36 - Realizing NPCs: Animation and Behavior Control for Believable Characters
36.1 Introduction36.2 Character Movement36.2.1 Movement Models36.2.2 Decoupling Extracted Motion36.2.3 Motion Correction36.2.4 Correcting Displacement Direction and Orientation36.2.4.1 Looping Animations36.2.4.2 Transition and One-Off Animations36.2.5 Correcting Speed36.3 Interrupting and Blending Movement36.3.1 Pose Matching36.3.2 Pose-Only and Per-Bone Blending36.4 Combining Actions36.4.1 Animation Masking36.4.2 Animation Mirroring36.4.3 Animation Layering36.5 Tracking36.5.1 Additive Aiming Poses36.5.2 Additive Aiming Animations36.5.3 Inverse Kinematics36.6 Behaviors36.6.1 Creating Variety36.6.1.1 Contextual One-Off Animations36.6.1.2 Micro Behaviors36.6.1.3 Using Additives with Idles36.6.2 Behavior Distribution36.6.2.1 Action Tokens36.6.2.2 Blackboards36.6.2.3 Action Ranking36.6.2.4 On-Screen Realization36.7 Conclusion
Chapter 37 - Using Queues to Model a Merchant’s Inventory
37.1 Introduction37.2 M/M/1 Queues37.3 Modeling a Merchant’s Inventory37.3.1 Computational Considerations37.4 ConclusionReferences
Chapter 38 - Psychologically Plausible Methods for Character Behavior Design
38.1 Introduction38.2 Perception and Abstractions38.3 Developmental Psychology and the Teleological Stance38.4 Attribution Theory38.5 Problem of Characters as Tokens38.6 Practical Application of Psychology in Character Behavior Design38.7 ConclusionReferences
Section VII - Analytics, Content Generation, and Experience Management
Chapter 39 - Analytics-Based AI Techniques for a Better Gaming Experience
39.1 Introduction39.2 Game Analytics Approaches to Modeling User Performance, Skills, and Behaviors39.2.1 Game Metrics39.2.2 Analysis Techniques39.2.2.1 Individual Analysis39.2.2.2 Communal Analysis39.3 Game Analytics and Adaptive Techniques39.3.1 Skill-Based Difficulty Adaptation39.3.2 Emotion-Based Adaptation39.3.3 Style-Based Adaptation39.4 Game Analytics and Recommendation Systems39.5 Game Analytics and Team Matching Techniques39.6 ConclusionReferences
Chapter 40 - Procedural Content Generation: An Overview
40.1 Introduction40.2 Technical Approaches to Content Generation40.2.1 Algorithms and Approaches40.2.1.1 Simulation Based40.2.1.2 Constructionist40.2.1.3 Grammars40.2.1.4 Optimization40.2.1.5 Constraint Driven40.2.2 Knowledge Representation40.2.2.1 Experiential Chunks40.2.2.2 Templates40.2.2.3 Components40.2.2.4 Subcomponents40.2.3 Mixing and Matching40.3 Understanding PCG’s Relationship to a Game40.3.1 PCG’s Mechanical Role40.3.1.1 Game Stage40.3.1.2 Interaction with the Generator40.3.1.3 Control over Player Experience40.3.2 Player Interaction with PCG40.3.2.1 PCG Relationship to Other Mechanics40.3.2.2 Reacting40.3.2.3 Strategizing40.3.2.4 Searching40.3.2.5 Practicing40.3.2.6 Community40.4 Choosing an Approach40.4.1 Getting Started40.4.2 Game Design Constraints40.4.3 Relationship with Art40.4.4 Engineering Constraints40.5 Tuning and Debugging a Content Generator40.6 Conclusion40.6.1 Tools and Frameworks40.6.2 Reading and CommunityReferences
Chapter 41 - Simulation Principles from Dwarf Fortress
41.1 Introduction41.2 Principle 1: Don’t Overplan Your Model41.3 Principle 2: Break Down and Understand the System41.4 Principle 3: Don’t Overcomplicate41.5 Principle 4: Base Your Model on Real-World Analogs41.6 ConclusionReferences
Chapter 42 - Techniques for AI-Driven Experience Management in Interactive Narratives
42.1 Introduction42.2 AI-Driven Experience Management42.2.1 Player in a Game Environment42.2.2 AI Manager Modifying Narrative42.2.3 Choosing between Stories42.3 AI-Driven Experience Management: Common Techniques42.3.1 Technique: Narrative Generation42.3.2 Technique: Play Style Modeling42.3.3 Technique: Goal Inference42.3.4 Technique: Emotional Modeling42.3.5 Technique: Objective Function Maximization42.3.6 Technique: Machine-Learned Narrative Selection42.4 Implementations42.4.1 PaSSAGE42.4.2 PAST42.4.3 PACE42.4.4 SCoReS42.5 Conclusion and Future WorkReferences

Content preview from Game AI Pro 2

Contributors

Tarn Adams is the creator of the cult classic Dwarf Fortress and cofounder of Bay 12 Games, along with his brother Zach. Tarn earned his PhD in mathematics from Stanford and BSc in mathematics from the University of Washington, Seattle, Washington.

Bobby Anguelov is a South African expat currently working as a senior programmer at Ubisoft, Montreal, Canada, primarily in the field of animation. Prior to joining Ubisoft, he worked as an AI/animation programmer at IO-Interactive. Outside of the game industry, he has worked in a variety of roles ranging from computer graphics lecturer to enterprise software consultant. Bobby holds an MS in computer science from the University of Pretoria, South Africa, with a focus on computational ...

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Game AI Pro 2

by Steven Rabin

Contributors

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