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Game AI Pro 2: Collected Wisdom of Game AI Professionals
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Game AI Pro 2: Collected Wisdom of Game AI Professionals
by Steven Rabin
Game AI Pro 2
Preface
Acknowledgments
Web Materials
General System Requirements
Updates
Comments and Suggestions
Editors
Contributors
Section I - General Wisdom
Chapter 1 - Game AI Appreciation, Revisited
1.1 Introduction
1.2 What Is Game AI?
1.3 Fuzzy Border
1.4 AI as the Nexus of Game Development
1.5 Frontiers of the Field
1.5.1 Pathfinding
1.5.2 Conversations
1.5.3 Dynamic Storylines
1.5.4 Player Modeling
1.5.5 Modeling and Displaying Emotion
1.5.6 Social Relationships
1.5.7 Scale
1.5.8 Content Explosion
1.5.9 The Unexplored
1.6 Conclusion
References
Chapter 2 - Combat Dialogue in FEAR: The Illusion of Communication
2.1 Introduction
2.2 From Barks to Dialogues
2.3 Conclusion
References
Chapter 3 - Dual-Utility Reasoning
3.1 Introduction
3.2 Dual-Utility Reasoning
3.3 Dual-Utility Reasoning in Zoo Tycoon 2
3.4 Conclusion
Reference
Chapter 4 - Vision Zones and Object Identification Certainty
4.1 Introduction
4.2 Vision Zones and Object Identification Certainty
4.2.1 Vision Sweet Spot
4.3 Incorporating Movement and Camouflage
4.4 Producing Appropriate Behavior from Identification Certainty
4.4.1 Alternative 1: Object Certainty with Time
4.4.2 Alternative 2: Explicit Detection Feedback
4.5 Conclusion
References
Chapter 5 - Agent Reaction Time: How Fast Should an AI React?
5.1 Introduction
5.2 Context Is Key
5.3 What Does Cognitive Research Tell Us?
5.3.1 Simple Reaction Time
5.3.2 Recognition or Go/No-Go Time
5.3.3 Complex Cognitive Task Reaction Time
5.4 Conclusion
References
Chapter 6 - Preventing Animation Twinning Using a Simple Blackboard
6.1 Introduction
6.2 Animation Twinning
6.3 Animation Blackboard
6.4 Delay Times and Animation Considerations
6.5 Conclusion
Reference
Section II - Architecture
Chapter 7 - Possibility Maps for Opportunistic AI and Believable Worlds
7.1 Introduction
7.2 What Are Probability and Possibility Maps?
7.3 Using Possibility Maps
7.4 Updating Probability Maps
7.5 Combining Possibility and Probability Maps
7.6 Factorizing Game State
7.7 Conclusion
Chapter 8 - Production Rules Implementation in 1849
8.1 Introduction
8.2 Game Mechanics and Production Rules
8.2.1 Game Mechanics
8.2.2 Game Simulation
8.3 Rule System
8.3.1 Resources
8.3.2 Resource Bins
8.3.3 Conditions and Actions
8.3.4 Rule Execution
8.4 Performance
8.4.1 Condition Checking Frequency
8.4.2 Condition Definition Language
8.4.3 Data Model
8.5 Lessons from 1849
8.5.1 Benefits
8.5.2 Lessons
8.6 Related Work
8.7 Conclusion
References
Chapter 9 - Production Systems: New Techniques in AAA Games
9.1 Introduction
9.1.1 Terminology
9.1.2 Design Considerations
9.2 What Decisions Is the System Trying to Make?
9.3 Choice of Rules Representation
9.4 Method of Rules Authoring
9.4.1 Recorded Rule System
9.4.2 More Typical Systems
9.5 Choice of Matching System
9.5.1 Greedy Matching/Selection
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
9.8.1 More Complex RHS
9.9 Debugging and Tuning
9.10 Conclusion
References
Chapter 10 - Building a Risk-Free Environment to Enhance Prototyping: Hinted-Execution Behavior Trees
10.1 Introduction
10.2 Explaining the Problem
10.3 Behavior Trees
10.3.1 Simple BT
10.3.2 Tree Complexity
10.4 Extending the Model
10.4.1 Hint Concept
10.4.2 HeBT Selectors
10.4.3 Hints and Conditions
10.5 Multilevel Architecture
10.5.1 Behavior Controllers
10.5.2 Exposing Hints to Higher Levels
10.6 More Complex Example
10.6.1 Prototyping New Ideas
10.6.2 Base Behavior
10.6.3 Prototype Idea
10.6.4 Creating a High-Level Tree
10.6.5 Analyzing the Results
10.7 Other Applications
10.7.1 Adaptation
10.7.2 Group Behaviors
10.8 Conclusion
References
Chapter 11 - Smart Zones to Create the Ambience of Life
11.1 Introduction
11.2 Designing an Ambience of Life
11.2.1 What Is a Living Scene?
11.2.2 Performing Living Scenes through Smart Zones
11.3 Smart Zones in Practice
11.3.1 Definition of Smart Zones by Game Designers
11.3.2 Functioning of the Smart Zone Architecture
11.3.2.1 Trigger Management
11.3.2.2 Role Assignment
11.3.2.3 Behavior Orchestration
11.3.3 NPC Behaviors
11.3.3.1 Role Interruption
11.3.3.2 Management of Overlapping Zones
11.4 Concrete Example
11.4.1 Scenario and the Smart Zones
11.4.2 Implementation in Unity3D
11.5 Conclusion
Acknowledgments
References
Chapter 12 - Separation of Concerns Architecture for AI and Animation
12.1 Introduction
12.2 Animation Graphs
12.3 Complexity Explosion and the Problem of Scalability
12.4 SoC
12.5 Separating Gameplay and Animation
12.6 Animation Behaviors
12.7 Animation Controller
12.8 Benefits of an SoC Animation Architecture
12.8.1 Functional Testing
12.8.2 System Refactoring
12.8.3 Level of Detail
12.9 Conclusion
References
Chapter 13 - Optimizing Practical Planning for Game AI
13.1 Introduction
13.1.1 Required Background
13.2 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 Structures
13.3.1 Actions
13.3.2 Plans
13.3.3 States
13.4 Practical Planning Algorithms
13.4.1 Iterating over Subsets of State Predicates
13.4.2 Recording Where the Action Parameters Occur in the Action Predicates
13.5 Conclusion
References
Section III - Movement and Pathfinding
Chapter 14 - JPS+: An Extreme A* Speed Optimization for Static Uniform Cost Grids
14.1 Introduction
14.2 Pruning Strategy
14.3 Forced Neighbors
14.4 Jump Points
14.4.1 Primary Jump Points
14.4.2 Straight Jump Points
14.4.3 Diagonal Jump Points
14.5 Wall Distances
14.6 Map Preprocess Implementation
14.7 Runtime Implementation
14.8 Conclusion
References
Chapter 15 - Subgoal Graphs for Fast Optimal Pathfinding
15.1 Introduction
15.2 Preliminaries
15.3 Simple Subgoal Graphs
15.3.1 Constructing Simple Subgoal Graphs
15.3.2 Searching Using Simple Subgoal Graphs
15.3.3 Identifying All Direct-H-Reachable Subgoals from a Given Cell
15.4 Two-Level Subgoal Graphs
15.4.1 Constructing Two-Level Subgoal Graphs
15.4.2 Searching Using Two-Level Subgoal Graphs
15.5 N-Level Graphs
15.6 Conclusion
Acknowledgments
References
Chapter 16 - Theta* for Any-Angle Pathfinding
16.1 Introduction
16.2 Problem Formalization
16.3 A* Algorithm
16.4 Theta* Algorithm
16.5 Theta* Paths
16.6 Analysis
16.7 Conclusion
Acknowledgment
References
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
18.1 Introduction
18.2 When Steering Behaviors Go Bad
18.2.1 Flocks versus Groups
18.2.2 Lack of Context
18.3 Toward Why, Not How
18.3.1 Context Maps
18.4 Context Maps by Example
18.4.1 Chase Behavior
18.4.2 Avoid Behavior
18.4.3 Combining and Parsing
18.4.4 Subslot Movement
18.5 Racing with Context
18.5.1 Coordinate System
18.5.2 Racing Line Behavior
18.5.3 Avoid Behavior
18.5.4 Drafting Behavior
18.5.5 Processing Context Maps
18.6 Advanced Techniques
18.6.1 Post-Processing
18.6.2 Optimizations
18.7 Conclusion
References
Chapter 19 - Guide to Anticipatory Collision Avoidance
19.1 Introduction
19.2 Key Concepts
19.2.1 Agent State
19.2.2 Predicting Collisions (Time to Collision)
19.2.3 Time Horizon
19.3 Prototype Implementation
19.3.1 Agent Forces
19.3.2 Avoidance Force
19.3.2.1 Avoidance Force Direction
19.3.2.2 Avoidance Force Magnitude
19.3.2.3 Corner Cases
19.3.2.4 Code
19.3.3 Runtime Performance
19.3.4 Parameter Tuning
19.4 Advanced Approaches
19.4.1 Human Motion Simulation
19.4.1.1 Personal Space
19.4.1.2 Field of View
19.4.1.3 Distance to Collision
19.4.1.3 Randomized Perturbation
19.4.2 Guaranteed Collision Avoidance
19.4.3 Herd’Em!
19.5 Conclusion
References
Chapter 20 - Hierarchical Architecture for Group Navigation Behaviors
20.1 Introduction
20.2 Group Navigation
20.2.1 Flocks
20.2.2 Formations
20.2.3 Social Groups
20.3 Navigation Pipeline Architecture
20.3.1 Navigation Behaviors
20.3.2 Navigation Pipeline
20.4 Group to Members Relationship Model
20.4.1 Leader
20.4.2 Virtual Group Entity
20.4.3 Hierarchical Entity Architecture
20.5 Pathfinding
20.6 Emergent Group Structure
20.6.1 Boids and Derivatives
20.6.2 “Local” Formations
20.6.3 Implementing an Emergent Group
20.7 Choreographed Formations
20.7.1 Formation Design
20.7.2 Slots Assignment
20.7.3 “Blind” Formation Following
20.7.4 Autonomous Formation Following
20.8 Group Collision Avoidance
20.8.1 Velocity Correction
20.8.2 Formation Adaptation
20.9 Conclusion
References
Chapter 21 - Dynamic Obstacle Navigation in Fuse
21.1 Introduction
21.2 Fuse Traversal Setups
21.3 Climb Mesh Generation
21.3.1 Path Following on Climb Meshes
21.3.2 Caveats of the Climb Path Generation
21.4 Parsing Climb Paths
21.4.1 Generating Virtual Controller Input
21.5 Conclusion
References
Section IV - Applied Search Techniques
Chapter 22 - Introduction to Search for Games
22.1 Introduction
22.2 Illustrative Example
22.3 Basic Search Requirements
22.3.1 Efficient State Representation
22.4 Alternate Uses of Search
22.5 Bottlenecks for Search
22.6 Conclusion
Chapter 23 - Personality Reinforced Search for Mobile Strategy Games
23.1 Introduction
23.2 Turn-Based Strategy War Games Based on “Euro” Board Games
23.3 Evolution and the Triune Brain
23.4 Personality First
23.5 Neocortex
23.6 State Evaluation
23.7 Limbic Brain
23.8 Evolution and the Genetic Algorithm
23.9 AI Chromosomes
23.10 Conclusion
References
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
25.1 Introduction
25.2 Background
25.3 Algorithm 1: Online UCB1
25.3.1 Applying to Games
25.4 Algorithm 2: Regret Matching
25.4.1 Applying to Games
25.5 Algorithm 3: Offline UCB1
25.6 Algorithm 4: UCT
25.6.1 Important Implementation Details
25.6.2 UCT Enhancements and Variations
25.6.3 Applying to Games
25.7 Conclusion
References
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
26.9.1 Finite-Domain Representation
26.9.2 Constraint Arcs and the Work Queue
26.9.3 Randomized Solutions
26.9.4 Variable Ordering
26.10 Conclusion
References
Section V - Tactics, Strategy, and Spatial Awareness
Chapter 27 - Looking for Trouble: Making NPCs Search Realistically
27.1 Introduction
27.2 Types of Searching
27.2.1 Cautious Search
27.2.2 Aggressive Search
27.3 Triggering a Search
27.3.1 Initial Stimulus-Based Trigger
27.3.2 Losing a Target
27.4 Phases of Searching
27.4.1 Phase 1
27.4.1.1 Cautious
27.4.1.2 Aggressive
27.4.2 Phase 2
27.4.2.1 Generation of Search Spots
27.4.2.2 Performing the Search
27.4.2.3 Selecting the Best Search Spot
27.4.3 Improving Phase 2 Search: Gap Detection
27.4.4 Ending the Search
27.5 Conclusion
References
Chapter 28 - Modeling Perception and Awareness in Tom Clancy’s Splinter Cell Blacklist
28.1 Introduction
28.1.1 Fairness
28.1.2 Consistency
28.1.3 Good Feedback
28.1.4 Intelligence
28.2 Visual Perception
28.3 Environmental Awareness
28.3.1 Connectivity
28.3.2 Changed Objects
28.4 Auditory Perception
28.4.1 Calculating Audio Distance
28.4.2 Auditory Detection Fairness
28.5 Social/Contextual Awareness
28.5.1 Social Awareness: Conversation
28.5.2 Contextual Awareness: Unreachable Area
28.5.3 Disappearing NPC Problem
28.6 Conclusion
References
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
29.11.1 Influence Source Density
29.11.2 Query Point Density
29.11.3 Update Frequency
29.11.4 Need for Precise or Extremely Accurate Results
29.12 Conclusion
References
Chapter 30 - Modular Tactical Influence Maps
30.1 Introduction
30.2 Influence Map Overview
30.3 Propagation
30.4 Architecture
30.4.1 Base Map Structure
30.4.2 Types of Base Maps
30.4.3 Templates
30.4.4 Working Maps
30.5 Population of Map Data
30.6 Information Retrieval
30.6.1 Values at a Point
30.6.2 Combinations of Maps
30.6.3 Special Functions
30.7 Usage
30.7.1 Information
30.7.2 Targeting
30.7.3 Positioning
30.8 Examples
30.8.1 Location for Area of Effect Attack
30.8.2 Movement to Safer Spot
30.8.3 Nearest Battlefront Location
30.9 Conclusion
References
Chapter 31 - Spatial Reasoning for Strategic Decision Making
31.1 Introduction
31.2 Spatial Partitioning
31.2.1 Region Generation
31.2.2 Static vs. Dynamic Regions
31.3 Working with Regions
31.3.1 Picking Places to Scout or Explore
31.3.2 Picking Places to Attack
31.3.3 Picking Unit Positions
31.3.4 Path Planning
31.3.5 Distance Estimates
31.3.6 Region Path Caching
31.3.7 Recognizing Cul-de-Sacs and Chokepoints
31.4 Influence Maps
31.4.1 Influence Map Basics
31.4.2 Propagation Calculations
31.4.3 Force Estimates over Regions
31.4.4 Illusion of Intelligence
31.4.5 Border Calculations
31.5 Spatial Characteristics
31.5.1 Scent of Death
31.5.2 High-Traffic Areas
31.5.3 Avenues of Approach
31.5.4 Flanking Attacks
31.5.5 Attackable Obstacles
31.5.6 Counterattacks and Consolidation
31.6 Conclusion and Future Thoughts
References
Chapter 32 - Extending the Spatial Coverage of a Voxel-Based Navigation Mesh
32.1 Introduction
32.2 Overview of Voxel-Based Navmesh Generation
32.2.1 Voxel-Based Navmesh Goals
32.2.2 Enter Voxelization
32.2.4 Triangulating the Polygons
32.2.5 Recap
32.3 Extending the Navmesh’s Spatial Coverage
32.3.1 Reintroducing the Discarded Space
32.3.2 Using the Metadata
32.4 Identifying Other Spaces
32.4.1 Prone Navigation
32.4.2 Swim Navigation
32.4.3 Sidestep Navigation
32.4.4 Multiple Generation Parameter Passes and Hierarchy
32.5 Playing with the Heuristic
32.6 Conclusion
References
Section VI - Character Behavior
Chapter 33 - Infected AI in The Last of Us
33.1 Introduction
33.2 The Infected
33.2.1 Senses
33.2.2 Distractions
33.3 AI System
33.3.1 Philosophy
33.3.2 Data-Driven Design
33.3.3 Implementation
33.3.4 Debugging
33.4 Skills and Behaviors
33.4.1 Search Skill
33.4.2 Chase Skill
33.4.3 Follow Skill
33.4.4 Ambush Skill
33.4.5 Throw Skill
33.4.6 On-Fire Skill
33.4.7 Wander Skill
33.4.8 Sleep Skill
33.5 Conclusion
References
Chapter 34 - Human Enemy AI in The Last of Us
34.1 Introduction
34.2 Building Blocks
34.3 AI Perception
34.4 Cover and Posts
34.5 Skills, States, and Behaviors
34.6 Stealth
34.7 Lethality
34.8 Flanking
34.9 Polish
34.10 Conclusion
Chapter 35 - Ellie: Buddy AI in The Last of Us
35.1 Introduction
35.2 Starting from Scratch
35.2.1 The Plan
35.2.2 Approach
35.3 Ambient Following
35.3.1 Follow Positions
35.3.2 Moving
35.3.3 Dodging
35.3.4 Teleportation
35.4 Taking Cover
35.4.1 Runtime Cover Generation
35.4.2 Cover Share
35.5 Combat Utility
35.5.1 Throwing
35.5.2 Grapples
35.5.3 Gifting
35.6 Armed Combat
35.6.1 Shooting
35.6.2 Balancing
35.6.3 Cheating
35.7 Finishing Touches
35.7.1 Vocalizations
35.7.2 Callouts
35.7.3 Ambience
35.8 Conclusion
Chapter 36 - Realizing NPCs: Animation and Behavior Control for Believable Characters
36.1 Introduction
36.2 Character Movement
36.2.1 Movement Models
36.2.2 Decoupling Extracted Motion
36.2.3 Motion Correction
36.2.4 Correcting Displacement Direction and Orientation
36.2.4.1 Looping Animations
36.2.4.2 Transition and One-Off Animations
36.2.5 Correcting Speed
36.3 Interrupting and Blending Movement
36.3.1 Pose Matching
36.3.2 Pose-Only and Per-Bone Blending
36.4 Combining Actions
36.4.1 Animation Masking
36.4.2 Animation Mirroring
36.4.3 Animation Layering
36.5 Tracking
36.5.1 Additive Aiming Poses
36.5.2 Additive Aiming Animations
36.5.3 Inverse Kinematics
36.6 Behaviors
36.6.1 Creating Variety
36.6.1.1 Contextual One-Off Animations
36.6.1.2 Micro Behaviors
36.6.1.3 Using Additives with Idles
36.6.2 Behavior Distribution
36.6.2.1 Action Tokens
36.6.2.2 Blackboards
36.6.2.3 Action Ranking
36.6.2.4 On-Screen Realization
36.7 Conclusion
Chapter 37 - Using Queues to Model a Merchant’s Inventory
37.1 Introduction
37.2 M/M/1 Queues
37.3 Modeling a Merchant’s Inventory
37.3.1 Computational Considerations
37.4 Conclusion
References
Chapter 38 - Psychologically Plausible Methods for Character Behavior Design
38.1 Introduction
38.2 Perception and Abstractions
38.3 Developmental Psychology and the Teleological Stance
38.4 Attribution Theory
38.5 Problem of Characters as Tokens
38.6 Practical Application of Psychology in Character Behavior Design
38.7 Conclusion
References
Section VII - Analytics, Content Generation, and Experience Management
Chapter 39 - Analytics-Based AI Techniques for a Better Gaming Experience
39.1 Introduction
39.2 Game Analytics Approaches to Modeling User Performance, Skills, and Behaviors
39.2.1 Game Metrics
39.2.2 Analysis Techniques
39.2.2.1 Individual Analysis
39.2.2.2 Communal Analysis
39.3 Game Analytics and Adaptive Techniques
39.3.1 Skill-Based Difficulty Adaptation
39.3.2 Emotion-Based Adaptation
39.3.3 Style-Based Adaptation
39.4 Game Analytics and Recommendation Systems
39.5 Game Analytics and Team Matching Techniques
39.6 Conclusion
References
Chapter 40 - Procedural Content Generation: An Overview
40.1 Introduction
40.2 Technical Approaches to Content Generation
40.2.1 Algorithms and Approaches
40.2.1.1 Simulation Based
40.2.1.2 Constructionist
40.2.1.3 Grammars
40.2.1.4 Optimization
40.2.1.5 Constraint Driven
40.2.2 Knowledge Representation
40.2.2.1 Experiential Chunks
40.2.2.2 Templates
40.2.2.3 Components
40.2.2.4 Subcomponents
40.2.3 Mixing and Matching
40.3 Understanding PCG’s Relationship to a Game
40.3.1 PCG’s Mechanical Role
40.3.1.1 Game Stage
40.3.1.2 Interaction with the Generator
40.3.1.3 Control over Player Experience
40.3.2 Player Interaction with PCG
40.3.2.1 PCG Relationship to Other Mechanics
40.3.2.2 Reacting
40.3.2.3 Strategizing
40.3.2.4 Searching
40.3.2.5 Practicing
40.3.2.6 Community
40.4 Choosing an Approach
40.4.1 Getting Started
40.4.2 Game Design Constraints
40.4.3 Relationship with Art
40.4.4 Engineering Constraints
40.5 Tuning and Debugging a Content Generator
40.6 Conclusion
40.6.1 Tools and Frameworks
40.6.2 Reading and Community
References
Chapter 41 - Simulation Principles from Dwarf Fortress
41.1 Introduction
41.2 Principle 1: Don’t Overplan Your Model
41.3 Principle 2: Break Down and Understand the System
41.4 Principle 3: Don’t Overcomplicate
41.5 Principle 4: Base Your Model on Real-World Analogs
41.6 Conclusion
References
Chapter 42 - Techniques for AI-Driven Experience Management in Interactive Narratives
42.1 Introduction
42.2 AI-Driven Experience Management
42.2.1 Player in a Game Environment
42.2.2 AI Manager Modifying Narrative
42.2.3 Choosing between Stories
42.3 AI-Driven Experience Management: Common Techniques
42.3.1 Technique: Narrative Generation
42.3.2 Technique: Play Style Modeling
42.3.3 Technique: Goal Inference
42.3.4 Technique: Emotional Modeling
42.3.5 Technique: Objective Function Maximization
42.3.6 Technique: Machine-Learned Narrative Selection
42.4 Implementations
42.4.1 PaSSAGE
42.4.2 PAST
42.4.3 PACE
42.4.4 SCoReS
42.5 Conclusion and Future Work
References
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