Artificial intelligence for games:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam [u.a.]
Elsevier [u.a.]
2006
|
| Schriftenreihe: | The Morgan Kaufmann series in interactive 3D technology
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | XXXV, 856 S. Ill., graph. Darst. CD-ROM (12 cm) |
| ISBN: | 0124977820 9780124977822 9780123736611 0123736617 |
Internformat
MARC
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| 100 | 1 | |a Millington, Ian |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Artificial intelligence for games |c Ian Millington |
| 264 | 1 | |a Amsterdam [u.a.] |b Elsevier [u.a.] |c 2006 | |
| 300 | |a XXXV, 856 S. |b Ill., graph. Darst. |e CD-ROM (12 cm) | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a The Morgan Kaufmann series in interactive 3D technology | |
| 500 | |a Includes bibliographical references and index | ||
| 650 | 4 | |a Künstliche Intelligenz | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Computer animation | |
| 650 | 4 | |a Computer games |x Programming | |
| 650 | 0 | 7 | |a Computerspiel |0 (DE-588)4010457-6 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Künstliche Intelligenz |0 (DE-588)4033447-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Programmierung |0 (DE-588)4076370-5 |2 gnd |9 rswk-swf |
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| 856 | 4 | 2 | |m Digitalisierung UB Passau |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015683907&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-015683907 | ||
Datensatz im Suchindex
| _version_ | 1804136565900836864 |
|---|---|
| adam_text | Contents
About the Author
vi
List of Figures
xxi
Acknowledgments
xxix
Preface
xxxi
About the CD-ROM
xxxiii
Part I
AI
and Games
1
Chapter
1
Introduction
1.1
What Is
AI?
1.1.1
Academic
AI
1.1.2
Game
AI
1.2
My
Γ
^Iodel
of Game
AI
1.2.1
Movement
1.2.2
Decision Making
1.2.3
Strategy
1.2.4
Infrastructure
1.2.5
Agent-Based
AI
1.2.6
In the Book
1.3
Algorithms, Data Structures, and
Represen tat
ions
1.3.1
Algorithms
1.3.2
Representations
4
5
7
9
10
10
11
11
12
12
13
13
16
1.4
ON THE CD
1.4.1
Programs
1.4.2
Libraries
1.5
Layout of the Book
Chapter
Á*
Game
AI
2.1
The
2.1.1
2.1.2
2.1.3
2.1.4
Complexity Fallacy
When Simple Things Look Good
When Complex Things Look Bad
The Perception Window
Changes of Behavior
2.2
The
2.2.1
2.2.2
2.2.3
Kind of
AI in
Games
Hacks
Heuristics
Algorithms
17
17
18
19
21
21
21
22
23
23
24
24
25
27
2.3
Speed and Memory
27
2.3.1
Processor Issues
28
2.3.2
Memory Concerns
31
2.3.3
PC Constraints
32
2.3.4
Console Constraints
32
2.4
The
AI
Engine
35
2.4.1
Structure of an
AI
Engine
35
2.4.2
Toolchain Concerns
37
2.4.3
Putting It All Together
38
Part II
Techniques
39
Chapter
Movement
41
3.1
The Basics of Movement Algorithms
42
3.1.1
Two-Dimensional Movement
43
3.1.2
Statics
44
3.1.3
Kinematics
47
3.2
Kinematic Movement Algorithms
51
3.2.1
Seek
52
3.2.2
Wandering
55
3.2.3
On the CD
57
3.3
Steering Behaviors
57
3.3.1
Steering Basics
58
3.3.2
Variable Matching
58
3.3.3
Seek and Flee
59
3.3.4
Arrive
62
3.3.5
Align
66
3.3.6
Velocity Matching
69
3.3.7
Delegated Behaviors
70
3.3.8
Pursue and Evade
71
3.3.9
Face
74
3.3.10
Looking Where You re Going
75
3.3.11
Wander
76
3.3.12
Path Following
79
3.3.13
Separation
85
3.3.14
Collision Avoidance
88
3.3.15
Obstacle and Wall Avoidance
94
3.3.16
Summary
99
3.4
Combining Steering Behaviors
100
3.4.1
Blending and Arbitration
100
3.4.2
Weighted Blending
101
3.4.3
Priorities
107
3.4.4
Cooperative Arbitration 111
3.4.5
Steering Pipeline
113
3.5
Predicting Physics
126
3.5.1
Aiming and Shooting
126
3.5.2
Projectile Trajectory
127
3.5.3
The Firing Solution
129
3.5.4
Projectiles with Drag
132
3.5.5
Iterative Targeting
134
3.6
Jumping
140
3.6.1
Jump Points
141
3.6.2
Landing Pads
145
3.6.3
Hole Fillers
149
3.7
Coordinated Movement
151
3.7.1
Fixed Formations
151
3.7.2
Scalable Formations
153
3.7.3
Emergent Formations
153
3.7.4
Two-Level Formation Steering
155
3.7.5
Implementation
158
3.7.6
Extending to More than Two Levels
165
3.7.7
Slot Roles and Better Assignment
167
3.7.8
Slot Assignment
169
3.7.9
Dynamic Slots and Plays
174
3.7.10
Tactical Movement
177
3.8
Motor Control
180
3.8.1
Output Filtering
181
3.8.2
Capability-Sensitive Steering
183
3.8.3
Common Actuation Properties
184
3.9
Movement in the Third Dimension
187
3.9.1
Rotation in Three Dimensions
188
3.9.2
Converting Steering Behaviors to Three Dimensions
189
3.9.3
Align
190
3.9.4
Align to Vector
191
3.9.5
Face
192
3.9.6
Look Where You re Going
195
3.9.7
Wander
195
3.9.8
Faking Rotation Axes
198
Chapter
Pathfinding
2ОЗ
4.1
The Pathfinding Graph
204
4.1.1
Graphs
205
4.1.2
Weighted Graphs
206
4.1.3
Directed Weighted Graphs
208
4.1.4
Terminology
209
4.1.5
Representation
210
4.2
Dijkstra
211
4.2.1
The Problem
211
4.2.2
The Algorithm
212
4.2.3
Pseudo-Code
216
4.2.4
Data Structures and Interfaces
219
4.2.5
Performance of
Dijkstra
221
4.2.6
Weaknesses
221
4.3
A*
223
4.3.1
The Problem
223
4.3.2
The Algorithm
223
4.3.3
Pseudo-Code
227
4.3.4
Data Structures and Interfaces
231
4.3.5
Implementation Notes
235
4.3.6
Algorithm Performance
236
4.3.7
Node Array A*
236
4.3.8
Choosing a Heuristic
239
4.4
World Representations
246
4.4.1
Tile Graphs
248
4.4.2
Dirichlet Domains
251
4.4.3
Points of Visibility
253
4.4.4
Polygonal Meshes
255
4.4.5
Non-Translational Problems
260
4.4.6
Cost Functions
261
4.4.7
Path Smoothing
262
4.5
Improving on A*
264
4.6
Hierarchical Pathfinding
265
4.6.1
The Hierarchical Pathfinding Graph
265
4.6.2
Pathfinding on the Hierarchical Graph
269
4.6.3
Hierarchical Pathfinding on Exclusions
272
4.6.4
Strange Effects of Hierarchies on Pathfinding
272
4.6.5
Instanced Geometry
275
4.7
Other Ideas in Pathfinding
281
4.7.1
Open Goal Pathfinding
282
4.7.2
Dynamic Pathfinding
282
4.7.3
Other Kinds of Information Reuse
283
4.7.4
Low Memory Algorithms
283
4.7.5
Interruptible Pathfinding
285
4.7.6
Pooling Planners
285
4.8
Continuous Time Pathfinding
286
4.8.1
The Problem
286
4.8.2
The Algorithm
288
4.8.3
Implementation Notes
292
4.8.4
Performance
292
4.8.5
Weaknesses
293
4.9
Movement Planning
293
4.9.1
Animations
293
4.9.2
Movement Planning
295
4.9.3
Example
297
4.9.4
Footfalls
298
Chapter
Decision Making
зоі
5.1
Overview of Decision Making
301
5.2
Decision Trees
303
5.2.1
The Problem
303
5.2.2
The Algorithm
304
5.2.3
Pseudo-Code
309
5.2.4
On the CD
311
5.2.5
Knowledge Representation
311
5.2.6
Implementation Nodes
312
5.2.7
Performance of Decision Trees
312
5.2.8
Balancing the Tree
312
5.2.9
Beyond the Tree
314
5.2.10
Random Decision Trees
315
5.3
State Machines
318
5.3.1
The Problem
320
5.3.2
The Algorithm
320
5.3.3
Pseudo-Code
320
5.3.4
Data Structures and Interfaces
321
5.3.5
On the CD
324
5.3.6
Performance
325
5.3.7
Implementation Notes
325
5.3.8
Hard-Coded FSM
325
5.3.9
Hierarchical State Machines
327
5.3.10
Combining Decision Trees and State Machines
341
5.4
Fuzzy Logic
344
5.4.1
Introduction to Fuzzy Logic
344
5.4.2
Fuzzy Logic Decision Making
354
5.4.3
Fuzzy State Machines
364
5.5
Markov Systems
369
5.5.1
Markov Processes
370
5.5.2
Markov State Machine
373
5.6
Goal-Oriented Behavior
376
377
378
380
383
388
394
401
Goal-Oriented Behavior
5.6.1
Goal-Oriented Behavior
5.6.2
Simple Selection
5.6.3
Overall Utility
5.6.4
Timing
5.6.5
Overall Utility GOAP
5.6.6
GOAP with IDA*
5.6.7
Smelly GOB
Rule-Based Systems
5.7.1
The Problem
5.7.2
The Algorithm
5.7.3
Pseudo-Code
5.7.4
Data Structures and Interfaces
5.7.5
Implementation Notes
5.7.6
Rule Arbitration
5.7.7
Unification
5.7
Rule-Based Systems
403
404
409
410
411
416
418
420
5.7.8
Rete
422
5.7.9
Extensions
433
5.7.10
Where Next
436
5.8
Blackboard Architectures
436
5.8.1
The Problem
437
5.8.2
The Algorithm
437
5.8.3
Pseudo-Code
439
5.8.4
Data Structures and Interfaces
440
5.8.5
Performance
442
5.8.6
Other Things Are Blackboard Systems
443
5.9
Scripting
444
5.9.1
Language Facilities
445
5.9.2
Embedding
446
5.9.3
Choosing a Language
447
5.9.4
A Language Selection
449
5.9.5
Rolling Your Own
453
5.9.6
Scripting Languages and Other
AI
459
5.10
Action Execution
459
5.10.1
Types of Action
460
5.10.2
The Algorithm
464
5.10.3
Pseudo-Code
465
5.10.4
Data Structures and Interfaces
467
5.10.5
Implementation Notes
469
5.10.6
Performance
470
5.10.7
Putting It All Together
470
Chapter
Tactical and Strategic
AI
473
6.1
Waypoint Tactics
473
6.1.1
Tactical Locations
474
6.1.2
Using Tactical Locations
483
6.1.3
Generating the Tactical Properties of a Waypoint
488
6.1.4
Automatically Generating the Waypoints
494
6.1.5
The Condensation Algorithm
495
6.2
Tactical Analyses
499
6.2.1
Representing the Game Level
500
6.2.2
Simple Influence Maps
500
6.2.3
Terrain Analysis
508
6.2.4
Learning with Tactical Analyses
510
6.2.5
A Structure for Tactical Analyses
512
6.2.6
Map Flooding
517
6.2.7
Convolution Filters
521
6.2.8
Cellular Automata
533
6.3
Tactical Pathfinding
538
6.3.1
The Cost Function
538
6.3.2
Tactic Weights and Concern Blending
538
6.3.3
Modifying the Pathfinding Heuristic
541
6.3.4
Tactical Graphs for Pathfinding
542
6.3.5
Using Tactical Waypoints
542
6.4
Coordinated Action
543
6.4.1
Multi-Tier
AI
544
6.4.2
Emergent Cooperation
551
6.4.3
Scripting Group Actions
554
6.4.4
Military Tactics
559
Chapter
LEARN
ING
7.1
Learning Basics
7.1.1
Online or Offline Learning
7.1.2
Intra-Behavior Learning
7.1.3
Inter-Behavior Learning
7.1.4
A Warning
7.1.5
Over-learning
7.1.6
The Zoo of Learning Algorithms
7.1.7
The Balance of Effort
563
563
564
564
565
565
566
566
567
7.2
Parameter Modification
567
7.2.1
The Parameter Landscape
567
7.2.2
Hill Climbing
569
7.2.3
Extensions to Basic Hill Climbing
572
7.2.4
Annealing
576
7.3
Action Prediction
580
7.3.1
Left or Right
581
7.3.2
Raw Probability
581
7.3.3
String Matching
582
7.3.4
N-Grams
582
7.3.5
Window Size
586
7.3.6
Hierarchical N-Grams
588
7.3.7
Application in Combat
591
7.4
Decision Learning
591
7.4.1
Structure of Decision Learning
592
7.4.2
What Should You Learn?
592
7.4.3
Three Techniques
593
7.5
Décision
Tree Learning
7.5.1
ID3
7.5.2
ID3 with Continuous Attributes
7.5.3
Incremental Decision Tree Learning
7.6
Reinforcement Learning
7.6.1
The Problem
7.6.2
The Algorithm
7.6.3
Pseudo-Code
7.6.4
Data Structures and Interfaces
7.6.5
Implementation Notes
7.6.6
Performance
7.6.7
Tailoring Parameters
7.6.8
Weaknesses and Realistic Applications
7.6.9
Other Ideas in Reinforcement Learning
7.7
Artificial Neural Networks
7.7.1
Overview
7.7.2
The Problem
7.7.3
The Algorithm
7.7.4
Pseudo-Code
7.7.5
Data Structures and Interfaces
7.7.6
Implementation Caveats
7.7.7
Performance
7.7.8
Other Approaches
Chapter
8
Board Games
8.1
Game Theory
8.1.1
Types of Games
8.1.2
The Game Tree
8.2
Minimaxing
8.2.1
The Static Evaluation Function
8.2.2
Minimaxing
8.2.3
The Minimaxing Algorithm
8.2.4
Negamaxing
8.2.5 AB
Pruning
8.2.6
The
AB
Search Window
8.2.7
Negascout
8.3
Transposition Tables and Memory
8.3.1
Hashing Game States
8.3.2
What to Store in the Table
8.3.3
Hash Table Implementation
8.3.4
Replacement
Strategies
676
8.3.5
A
Complete
Transposition Table
676
8.3.6
Transposition Table Issues
677
8.3.7
Using Opponent s Thinking Time
678
8.4
Memory-Enhanced Test Algorithms
678
8.4.1
Implementing Test
678
8.4.2
The MTD Algorithm
681
8.4.3
Pseudo-Code
682
8.5
Opening Books and Other Set Plays
683
8.5.1
Implementing an Opening Book
684
8.5.2
Learning for Opening Books
684
8.5.3
Set Play Books
685
8.6
Further Optimizations
685
8.6.1
Iterative Deepening
686
8.6.2
Variable Depth Approaches
687
8.7
Turn-Based Strategy Games
688
8.7.1
Impossible Tree Size
689
8.7.2
Real-Time
AI in a
Turn-Based Game
690
PART III
Supporting Technologies
69 1
Chapter
Execution Management
693
694
694
702
705
707
709
712
712
713
713
714
715
721
724
9.1
Scheduling
9.1.1
The Scheduler
9.1.2
Interruptible Processes
9.1.3
Load-Balancing Scheduler
9.1.4
Hierarchical Scheduling
9.1.5
Priority Scheduling
9.2
Anytime Algorithms
9.3
Level of Detail
9.3.1
Graphics Level of Detail
9.3.2
AI LOD
9.3.3
Scheduling
LOD
9.3.4
Behavioral
LOD
9.3.5
Group
LOD
9.3.6
In Summary
Chapter
10
World Interfacing
727
10.1
Communication
727
10.2
Getting Knowledge Efficiently
728
10.2.1
Polling
728
10.2.2
Events
729
10.2.3
Determining What Approach to Use
730
10.3
Event Managers
730
10.3.1
Implementation
733
10.3.2
Event Casting
736
10.3.3
Inter-Agent Communication
738
10.4
Polling Stations
739
10.4.1
Pseudo-Code
739
10.4.2
Performance
740
10.4.3
Implementation Notes
740
10.4.4
Abstract Polling
741
10.5
Sense Management
742
10.5.1
Faking It
743
10.5.2
What Do I Know?
743
10.5.3
Sensory Modalities
744
10.5.4
Region Sense Manager
750
10.5.5
Finite Element Model Sense Manager
758
Chapter
11
Tools and Content Creation
769
11.0.1
Toolchains Limit
AI
770
11.0.2
Where
AI
Knowledge Comes from
770
11.1
Knowledge for Pathfinding and Waypoint Tactics
770
11.1.1
Manually Creating Region Data
771
11.1.2
Automatic Graph Creation
774
11.1.3
Geometric Analysis
774
11.1.4
Data Mining
778
11.2
Knowledge for Movement
780
11.2.1
Obstacles
780
11.2.2
High-Level Staging
782
11.3
Knowledge for Decision Making
783
11.3.1
Object Types
783
11.3.2
Concrete Actions
783
11.4
The Toolchain
784
11.4.1
Data-Driven
Editors 784
11.4.2 AI
Design Tools
785
11.4.3
Remote Debugging
786
11.4.4
Plug-Ins
787
Part IV
Designing Game
AI 789
Chapter
12
Designing Game
AI 791
12.1
The Design
791
12.1.1
Example
792
12.1.2
Evaluating the Behaviors
793
12.1.3
Selecting Techniques
796
12.1.4
The Scope of One Game
798
12.2
Shooters
798
12.2.1
Movement and Firing
799
12.2.2
Decision Making
801
12.2.3
Perception
802
12.2.4
Pathfinding and Tactical
AI 802
12.2.5
Shooter-Like Games
803
12.3
Driving
805
12.3.1
Movement
805
12.3.2
Pathfinding and Tactical
AI 808
12.3.3
Driving-Like Games
808
12.4
Real-Time Strategy
809
12.4.1
Pathfinding
810
12.4.2
Group Movement
810
12.4.3
Tactical and Strategic
AI 811
12.4.4
Decision Making
811
12.5
Sports
812
12.5.1
Physics Prediction
813
12.5.2
Playbooks and Content Creation
814
12.6
Turn-Based Strategy Games
814
12.6.1
Timing
815
12.6.2
Helping the Player
816
CHAPTER
AI-BASED Game Genres
817
13.1
Teaching Characters
817
13.1.1
Representing Actions
818
13.1.2
Representing the World
818
13.1.3
Learning Mechanism
819
13.1.4
Predictable Mental Models and Pathological States
821
13.2
Flocking and Herding Games
823
13.2.1
Making the Creatures
823
13.2.2
Tuning Steering for Interactivity
823
13.2.3
Steering Behavior Stability
825
13.2.4
Ecosystem Design
825
Appendix
References
829
A.I Books, Periodicals, and Papers
829
A.2 Games
830
Index
835
|
| adam_txt |
Contents
About the Author
vi
List of Figures
xxi
Acknowledgments
xxix
Preface
xxxi
About the CD-ROM
xxxiii
Part I
AI
and Games
1
Chapter
1
Introduction
1.1
What Is
AI?
1.1.1
Academic
AI
1.1.2
Game
AI
1.2
My
Γ
^Iodel
of Game
AI
1.2.1
Movement
1.2.2
Decision Making
1.2.3
Strategy
1.2.4
Infrastructure
1.2.5
Agent-Based
AI
1.2.6
In the Book
1.3
Algorithms, Data Structures, and
Represen tat
ions
1.3.1
Algorithms
1.3.2
Representations
4
5
7
9
10
10
11
11
12
12
13
13
16
1.4
ON THE CD
1.4.1
Programs
1.4.2
Libraries
1.5
Layout of the Book
Chapter
Á*
Game
AI
2.1
The
2.1.1
2.1.2
2.1.3
2.1.4
Complexity Fallacy
When Simple Things Look Good
When Complex Things Look Bad
The Perception Window
Changes of Behavior
2.2
The
2.2.1
2.2.2
2.2.3
Kind of
AI in
Games
Hacks
Heuristics
Algorithms
17
17
18
19
21
21
21
22
23
23
24
24
25
27
2.3
Speed and Memory
27
2.3.1
Processor Issues
28
2.3.2
Memory Concerns
31
2.3.3
PC Constraints
32
2.3.4
Console Constraints
32
2.4
The
AI
Engine
35
2.4.1
Structure of an
AI
Engine
35
2.4.2
Toolchain Concerns
37
2.4.3
Putting It All Together
38
Part II
Techniques
39
Chapter
Movement
41
3.1
The Basics of Movement Algorithms
42
3.1.1
Two-Dimensional Movement
43
3.1.2
Statics
44
3.1.3
Kinematics
47
3.2
Kinematic Movement Algorithms
51
3.2.1
Seek
52
3.2.2
Wandering
55
3.2.3
On the CD
57
3.3
Steering Behaviors
57
3.3.1
Steering Basics
58
3.3.2
Variable Matching
58
3.3.3
Seek and Flee
59
3.3.4
Arrive
62
3.3.5
Align
66
3.3.6
Velocity Matching
69
3.3.7
Delegated Behaviors
70
3.3.8
Pursue and Evade
71
3.3.9
Face
74
3.3.10
Looking Where You're Going
75
3.3.11
Wander
76
3.3.12
Path Following
79
3.3.13
Separation
85
3.3.14
Collision Avoidance
88
3.3.15
Obstacle and Wall Avoidance
94
3.3.16
Summary
99
3.4
Combining Steering Behaviors
100
3.4.1
Blending and Arbitration
100
3.4.2
Weighted Blending
101
3.4.3
Priorities
107
3.4.4
Cooperative Arbitration 111
3.4.5
Steering Pipeline
113
3.5
Predicting Physics
126
3.5.1
Aiming and Shooting
126
3.5.2
Projectile Trajectory
127
3.5.3
The Firing Solution
129
3.5.4
Projectiles with Drag
132
3.5.5
Iterative Targeting
134
3.6
Jumping
140
3.6.1
Jump Points
141
3.6.2
Landing Pads
145
3.6.3
Hole Fillers
149
3.7
Coordinated Movement
151
3.7.1
Fixed Formations
151
3.7.2
Scalable Formations
153
3.7.3
Emergent Formations
153
3.7.4
Two-Level Formation Steering
155
3.7.5
Implementation
158
3.7.6
Extending to More than Two Levels
165
3.7.7
Slot Roles and Better Assignment
167
3.7.8
Slot Assignment
169
3.7.9
Dynamic Slots and Plays
174
3.7.10
Tactical Movement
177
3.8
Motor Control
180
3.8.1
Output Filtering
181
3.8.2
Capability-Sensitive Steering
183
3.8.3
Common Actuation Properties
184
3.9
Movement in the Third Dimension
187
3.9.1
Rotation in Three Dimensions
188
3.9.2
Converting Steering Behaviors to Three Dimensions
189
3.9.3
Align
190
3.9.4
Align to Vector
191
3.9.5
Face
192
3.9.6
Look Where You're Going
195
3.9.7
Wander
195
3.9.8
Faking Rotation Axes
198
Chapter
Pathfinding
2ОЗ
4.1
The Pathfinding Graph
204
4.1.1
Graphs
205
4.1.2
Weighted Graphs
206
4.1.3
Directed Weighted Graphs
208
4.1.4
Terminology
209
4.1.5
Representation
210
4.2
Dijkstra
211
4.2.1
The Problem
211
4.2.2
The Algorithm
212
4.2.3
Pseudo-Code
216
4.2.4
Data Structures and Interfaces
219
4.2.5
Performance of
Dijkstra
221
4.2.6
Weaknesses
221
4.3
A*
223
4.3.1
The Problem
223
4.3.2
The Algorithm
223
4.3.3
Pseudo-Code
227
4.3.4
Data Structures and Interfaces
231
4.3.5
Implementation Notes
235
4.3.6
Algorithm Performance
236
4.3.7
Node Array A*
236
4.3.8
Choosing a Heuristic
239
4.4
World Representations
246
4.4.1
Tile Graphs
248
4.4.2
Dirichlet Domains
251
4.4.3
Points of Visibility
253
4.4.4
Polygonal Meshes
255
4.4.5
Non-Translational Problems
260
4.4.6
Cost Functions
261
4.4.7
Path Smoothing
262
4.5
Improving on A*
264
4.6
Hierarchical Pathfinding
265
4.6.1
The Hierarchical Pathfinding Graph
265
4.6.2
Pathfinding on the Hierarchical Graph
269
4.6.3
Hierarchical Pathfinding on Exclusions
272
4.6.4
Strange Effects of Hierarchies on Pathfinding
272
4.6.5
Instanced Geometry
275
4.7
Other Ideas in Pathfinding
281
4.7.1
Open Goal Pathfinding
282
4.7.2
Dynamic Pathfinding
282
4.7.3
Other Kinds of Information Reuse
283
4.7.4
Low Memory Algorithms
283
4.7.5
Interruptible Pathfinding
285
4.7.6
Pooling Planners
285
4.8
Continuous Time Pathfinding
286
4.8.1
The Problem
286
4.8.2
The Algorithm
288
4.8.3
Implementation Notes
292
4.8.4
Performance
292
4.8.5
Weaknesses
293
4.9
Movement Planning
293
4.9.1
Animations
293
4.9.2
Movement Planning
295
4.9.3
Example
297
4.9.4
Footfalls
298
Chapter
Decision Making
зоі
5.1
Overview of Decision Making
301
5.2
Decision Trees
303
5.2.1
The Problem
303
5.2.2
The Algorithm
304
5.2.3
Pseudo-Code
309
5.2.4
On the CD
311
5.2.5
Knowledge Representation
311
5.2.6
Implementation Nodes
312
5.2.7
Performance of Decision Trees
312
5.2.8
Balancing the Tree
312
5.2.9
Beyond the Tree
314
5.2.10
Random Decision Trees
315
5.3
State Machines
318
5.3.1
The Problem
320
5.3.2
The Algorithm
320
5.3.3
Pseudo-Code
320
5.3.4
Data Structures and Interfaces
321
5.3.5
On the CD
324
5.3.6
Performance
325
5.3.7
Implementation Notes
325
5.3.8
Hard-Coded FSM
325
5.3.9
Hierarchical State Machines
327
5.3.10
Combining Decision Trees and State Machines
341
5.4
Fuzzy Logic
344
5.4.1
Introduction to Fuzzy Logic
344
5.4.2
Fuzzy Logic Decision Making
354
5.4.3
Fuzzy State Machines
364
5.5
Markov Systems
369
5.5.1
Markov Processes
370
5.5.2
Markov State Machine
373
5.6
Goal-Oriented Behavior
376
377
378
380
383
388
394
401
Goal-Oriented Behavior
5.6.1
Goal-Oriented Behavior
5.6.2
Simple Selection
5.6.3
Overall Utility
5.6.4
Timing
5.6.5
Overall Utility GOAP
5.6.6
GOAP with IDA*
5.6.7
Smelly GOB
Rule-Based Systems
5.7.1
The Problem
5.7.2
The Algorithm
5.7.3
Pseudo-Code
5.7.4
Data Structures and Interfaces
5.7.5
Implementation Notes
5.7.6
Rule Arbitration
5.7.7
Unification
5.7
Rule-Based Systems
403
404
409
410
411
416
418
420
5.7.8
Rete
422
5.7.9
Extensions
433
5.7.10
Where Next
436
5.8
Blackboard Architectures
436
5.8.1
The Problem
437
5.8.2
The Algorithm
437
5.8.3
Pseudo-Code
439
5.8.4
Data Structures and Interfaces
440
5.8.5
Performance
442
5.8.6
Other Things Are Blackboard Systems
443
5.9
Scripting
444
5.9.1
Language Facilities
445
5.9.2
Embedding
446
5.9.3
Choosing a Language
447
5.9.4
A Language Selection
449
5.9.5
Rolling Your Own
453
5.9.6
Scripting Languages and Other
AI
459
5.10
Action Execution
459
5.10.1
Types of Action
460
5.10.2
The Algorithm
464
5.10.3
Pseudo-Code
465
5.10.4
Data Structures and Interfaces
467
5.10.5
Implementation Notes
469
5.10.6
Performance
470
5.10.7
Putting It All Together
470
Chapter
Tactical and Strategic
AI
473
6.1
Waypoint Tactics
473
6.1.1
Tactical Locations
474
6.1.2
Using Tactical Locations
483
6.1.3
Generating the Tactical Properties of a Waypoint
488
6.1.4
Automatically Generating the Waypoints
494
6.1.5
The Condensation Algorithm
495
6.2
Tactical Analyses
499
6.2.1
Representing the Game Level
500
6.2.2
Simple Influence Maps
500
6.2.3
Terrain Analysis
508
6.2.4
Learning with Tactical Analyses
510
6.2.5
A Structure for Tactical Analyses
512
6.2.6
Map Flooding
517
6.2.7
Convolution Filters
521
6.2.8
Cellular Automata
533
6.3
Tactical Pathfinding
538
6.3.1
The Cost Function
538
6.3.2
Tactic Weights and Concern Blending
538
6.3.3
Modifying the Pathfinding Heuristic
541
6.3.4
Tactical Graphs for Pathfinding
542
6.3.5
Using Tactical Waypoints
542
6.4
Coordinated Action
543
6.4.1
Multi-Tier
AI
544
6.4.2
Emergent Cooperation
551
6.4.3
Scripting Group Actions
554
6.4.4
Military Tactics
559
Chapter
LEARN
ING
7.1
Learning Basics
7.1.1
Online or Offline Learning
7.1.2
Intra-Behavior Learning
7.1.3
Inter-Behavior Learning
7.1.4
A Warning
7.1.5
Over-learning
7.1.6
The Zoo of Learning Algorithms
7.1.7
The Balance of Effort
563
563
564
564
565
565
566
566
567
7.2
Parameter Modification
567
7.2.1
The Parameter Landscape
567
7.2.2
Hill Climbing
569
7.2.3
Extensions to Basic Hill Climbing
572
7.2.4
Annealing
576
7.3
Action Prediction
580
7.3.1
Left or Right
581
7.3.2
Raw Probability
581
7.3.3
String Matching
582
7.3.4
N-Grams
582
7.3.5
Window Size
586
7.3.6
Hierarchical N-Grams
588
7.3.7
Application in Combat
591
7.4
Decision Learning
591
7.4.1
Structure of Decision Learning
592
7.4.2
What Should You Learn?
592
7.4.3
Three Techniques
593
7.5
Décision
Tree Learning
7.5.1
ID3
7.5.2
ID3 with Continuous Attributes
7.5.3
Incremental Decision Tree Learning
7.6
Reinforcement Learning
7.6.1
The Problem
7.6.2
The Algorithm
7.6.3
Pseudo-Code
7.6.4
Data Structures and Interfaces
7.6.5
Implementation Notes
7.6.6
Performance
7.6.7
Tailoring Parameters
7.6.8
Weaknesses and Realistic Applications
7.6.9
Other Ideas in Reinforcement Learning
7.7
Artificial Neural Networks
7.7.1
Overview
7.7.2
The Problem
7.7.3
The Algorithm
7.7.4
Pseudo-Code
7.7.5
Data Structures and Interfaces
7.7.6
Implementation Caveats
7.7.7
Performance
7.7.8
Other Approaches
Chapter
8
Board Games
8.1
Game Theory
8.1.1
Types of Games
8.1.2
The Game Tree
8.2
Minimaxing
8.2.1
The Static Evaluation Function
8.2.2
Minimaxing
8.2.3
The Minimaxing Algorithm
8.2.4
Negamaxing
8.2.5 AB
Pruning
8.2.6
The
AB
Search Window
8.2.7
Negascout
8.3
Transposition Tables and Memory
8.3.1
Hashing Game States
8.3.2
What to Store in the Table
8.3.3
Hash Table Implementation
8.3.4
Replacement
Strategies
676
8.3.5
A
Complete
Transposition Table
676
8.3.6
Transposition Table Issues
677
8.3.7
Using Opponent's Thinking Time
678
8.4
Memory-Enhanced Test Algorithms
678
8.4.1
Implementing Test
678
8.4.2
The MTD Algorithm
681
8.4.3
Pseudo-Code
682
8.5
Opening Books and Other Set Plays
683
8.5.1
Implementing an Opening Book
684
8.5.2
Learning for Opening Books
684
8.5.3
Set Play Books
685
8.6
Further Optimizations
685
8.6.1
Iterative Deepening
686
8.6.2
Variable Depth Approaches
687
8.7
Turn-Based Strategy Games
688
8.7.1
Impossible Tree Size
689
8.7.2
Real-Time
AI in a
Turn-Based Game
690
PART III
Supporting Technologies
69 1
Chapter
Execution Management
693
694
694
702
705
707
709
712
712
713
713
714
715
721
724
9.1
Scheduling
9.1.1
The Scheduler
9.1.2
Interruptible Processes
9.1.3
Load-Balancing Scheduler
9.1.4
Hierarchical Scheduling
9.1.5
Priority Scheduling
9.2
Anytime Algorithms
9.3
Level of Detail
9.3.1
Graphics Level of Detail
9.3.2
AI LOD
9.3.3
Scheduling
LOD
9.3.4
Behavioral
LOD
9.3.5
Group
LOD
9.3.6
In Summary
Chapter
10
World Interfacing
727
10.1
Communication
727
10.2
Getting Knowledge Efficiently
728
10.2.1
Polling
728
10.2.2
Events
729
10.2.3
Determining What Approach to Use
730
10.3
Event Managers
730
10.3.1
Implementation
733
10.3.2
Event Casting
736
10.3.3
Inter-Agent Communication
738
10.4
Polling Stations
739
10.4.1
Pseudo-Code
739
10.4.2
Performance
740
10.4.3
Implementation Notes
740
10.4.4
Abstract Polling
741
10.5
Sense Management
742
10.5.1
Faking It
743
10.5.2
What Do I Know?
743
10.5.3
Sensory Modalities
744
10.5.4
Region Sense Manager
750
10.5.5
Finite Element Model Sense Manager
758
Chapter
11
Tools and Content Creation
769
11.0.1
Toolchains Limit
AI
770
11.0.2
Where
AI
Knowledge Comes from
770
11.1
Knowledge for Pathfinding and Waypoint Tactics
770
11.1.1
Manually Creating Region Data
771
11.1.2
Automatic Graph Creation
774
11.1.3
Geometric Analysis
774
11.1.4
Data Mining
778
11.2
Knowledge for Movement
780
11.2.1
Obstacles
780
11.2.2
High-Level Staging
782
11.3
Knowledge for Decision Making
783
11.3.1
Object Types
783
11.3.2
Concrete Actions
783
11.4
The Toolchain
784
11.4.1
Data-Driven
Editors 784
11.4.2 AI
Design Tools
785
11.4.3
Remote Debugging
786
11.4.4
Plug-Ins
787
Part IV
Designing Game
AI 789
Chapter
12
Designing Game
AI 791
12.1
The Design
791
12.1.1
Example
792
12.1.2
Evaluating the Behaviors
793
12.1.3
Selecting Techniques
796
12.1.4
The Scope of One Game
798
12.2
Shooters
798
12.2.1
Movement and Firing
799
12.2.2
Decision Making
801
12.2.3
Perception
802
12.2.4
Pathfinding and Tactical
AI 802
12.2.5
Shooter-Like Games
803
12.3
Driving
805
12.3.1
Movement
805
12.3.2
Pathfinding and Tactical
AI 808
12.3.3
Driving-Like Games
808
12.4
Real-Time Strategy
809
12.4.1
Pathfinding
810
12.4.2
Group Movement
810
12.4.3
Tactical and Strategic
AI 811
12.4.4
Decision Making
811
12.5
Sports
812
12.5.1
Physics Prediction
813
12.5.2
Playbooks and Content Creation
814
12.6
Turn-Based Strategy Games
814
12.6.1
Timing
815
12.6.2
Helping the Player
816
CHAPTER
AI-BASED Game Genres
817
13.1
Teaching Characters
817
13.1.1
Representing Actions
818
13.1.2
Representing the World
818
13.1.3
Learning Mechanism
819
13.1.4
Predictable Mental Models and Pathological States
821
13.2
Flocking and Herding Games
823
13.2.1
Making the Creatures
823
13.2.2
Tuning Steering for Interactivity
823
13.2.3
Steering Behavior Stability
825
13.2.4
Ecosystem Design
825
Appendix
References
829
A.I Books, Periodicals, and Papers
829
A.2 Games
830
Index
835 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Millington, Ian |
| author_facet | Millington, Ian |
| author_role | aut |
| author_sort | Millington, Ian |
| author_variant | i m im |
| building | Verbundindex |
| bvnumber | BV022476497 |
| callnumber-first | Q - Science |
| callnumber-label | QA76 |
| callnumber-raw | QA76.76.C672 |
| callnumber-search | QA76.76.C672 |
| callnumber-sort | QA 276.76 C672 |
| callnumber-subject | QA - Mathematics |
| classification_rvk | ST 324 SU 500 |
| ctrlnum | (OCoLC)254443361 (DE-599)BVBBV022476497 |
| dewey-full | 794.81526 |
| dewey-hundreds | 700 - The arts |
| dewey-ones | 794 - Indoor games of skill |
| dewey-raw | 794.81526 |
| dewey-search | 794.81526 |
| dewey-sort | 3794.81526 |
| dewey-tens | 790 - Recreational and performing arts |
| discipline | Sport Informatik |
| discipline_str_mv | Sport Informatik |
| format | Book |
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| id | DE-604.BV022476497 |
| illustrated | Illustrated |
| index_date | 2024-07-02T17:46:43Z |
| indexdate | 2024-07-09T20:58:26Z |
| institution | BVB |
| isbn | 0124977820 9780124977822 9780123736611 0123736617 |
| language | English |
| lccn | 2006299120 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-015683907 |
| oclc_num | 254443361 |
| open_access_boolean | |
| owner | DE-Aug4 DE-824 DE-523 DE-703 DE-B768 |
| owner_facet | DE-Aug4 DE-824 DE-523 DE-703 DE-B768 |
| physical | XXXV, 856 S. Ill., graph. Darst. CD-ROM (12 cm) |
| publishDate | 2006 |
| publishDateSearch | 2006 |
| publishDateSort | 2006 |
| publisher | Elsevier [u.a.] |
| record_format | marc |
| series2 | The Morgan Kaufmann series in interactive 3D technology |
| spelling | Millington, Ian Verfasser aut Artificial intelligence for games Ian Millington Amsterdam [u.a.] Elsevier [u.a.] 2006 XXXV, 856 S. Ill., graph. Darst. CD-ROM (12 cm) txt rdacontent n rdamedia nc rdacarrier The Morgan Kaufmann series in interactive 3D technology Includes bibliographical references and index Künstliche Intelligenz Artificial intelligence Computer animation Computer games Programming Computerspiel (DE-588)4010457-6 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Programmierung (DE-588)4076370-5 gnd rswk-swf Computerspiel (DE-588)4010457-6 s Programmierung (DE-588)4076370-5 s DE-604 Künstliche Intelligenz (DE-588)4033447-8 s Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015683907&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Millington, Ian Artificial intelligence for games Künstliche Intelligenz Artificial intelligence Computer animation Computer games Programming Computerspiel (DE-588)4010457-6 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Programmierung (DE-588)4076370-5 gnd |
| subject_GND | (DE-588)4010457-6 (DE-588)4033447-8 (DE-588)4076370-5 |
| title | Artificial intelligence for games |
| title_auth | Artificial intelligence for games |
| title_exact_search | Artificial intelligence for games |
| title_exact_search_txtP | Artificial intelligence for games |
| title_full | Artificial intelligence for games Ian Millington |
| title_fullStr | Artificial intelligence for games Ian Millington |
| title_full_unstemmed | Artificial intelligence for games Ian Millington |
| title_short | Artificial intelligence for games |
| title_sort | artificial intelligence for games |
| topic | Künstliche Intelligenz Artificial intelligence Computer animation Computer games Programming Computerspiel (DE-588)4010457-6 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Programmierung (DE-588)4076370-5 gnd |
| topic_facet | Künstliche Intelligenz Artificial intelligence Computer animation Computer games Programming Computerspiel Programmierung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015683907&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT millingtonian artificialintelligenceforgames |