Artificial intelligence for games:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton [u.a.]
CRC Press
2009
|
| Ausgabe: | 2. ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXIII, 870 S. Ill., graph. Darst. |
| ISBN: | 9780123747310 |
Internformat
MARC
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| 020 | |a 9780123747310 |c hbk. |9 978-0-12-374731-0 | ||
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| 100 | 1 | |a Millington, Ian |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Artificial intelligence for games |c Ian Millington and John Funge |
| 250 | |a 2. ed. | ||
| 264 | 1 | |a Boca Raton [u.a.] |b CRC Press |c 2009 | |
| 300 | |a XXIII, 870 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
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| 689 | 1 | 0 | |a Computerspiel |0 (DE-588)4010457-6 |D s |
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| 689 | 1 | |5 DE-604 | |
| 700 | 1 | |a Funge, John David |d 1968- |e Verfasser |0 (DE-588)140001026 |4 aut | |
| 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=018988926&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-018988926 | |
Datensatz im Suchindex
| DE-BY-FWS_katkey | 488717 |
|---|---|
| _version_ | 1824555693678526464 |
| adam_text |
Contents
About the Authors
iv
Acknowledgments
xix
Preface
xxi
About the Website
xxiii
Part I
Al
and Games
1
Introduction
з
1.1
What Is
AI? 4
1.1.1
Academic
AI 5
1.1.2
Game
AI 7
1.2
MODEL OF Game
AI 8
1.2.1
Movement
9
1.2.2
Decision Making
10
1.2.3
Strategy
10
1.2.4
Infrastructure
11
1.2.5
Agent-Based
AI 11
1.2.6
In the Book
12
1.3
Algorithms, Data Structures, and
Representations
12
1.3.1
Algorithms
12
1.3.2
Representations
15
16
16
17
18
19
19
19
20
21
21
22
22
23
24
25
25
28
29
29
2.4
The
ai
Engine
31
2.4.1
Structure of an
AI
Engine
32
2.4.2
Toolchain Concerns
33
2.4.3
Putting It All Together
34
Part II
Techniques
37
Chapter
«Э
Movement
39
3.1
The Basics of Movement Algorithms
40
3.1.1
Two-Dimensiohal Movement
41
3.1.2
Statics
42
3.1.3
Kinematics
45
3.2
KINEMATIC MOVEMENT ALGORITHMS
49
3.2.1
Seek
49
1.4
On the Website
1.4.1
Programs
1.4.2
Libraries
1.5
Layout of the Book
Chapter
Zá
Game
Al
2.1
The
Complexity Fallacy
2.1.1
When Simple Things Look Good
2.1.2
When Complex Things Look Bad
2.1.3
The Perception Window
2.1.4
Changes of Behavior
2.2
The
Kind of
Al in
Games
2.2.1
Hacks
2.2.2
Heuristics
2.2.3
Algorithms
2.3
Speed and memory
2.3.1
Processor Issues
2.3.2
Memory Concerns
2.3.3
PC Constraints
2.3.4
Console Constraints
53
55
3.3
STEERING
BEHAVIORS
55
55
56
56
59
62
66
67
68
71
72
73
76
82
84
90
95
3.4
Combining Steering Behaviors
95
96
96
103
107
108
3.5
Predicting Physics
120
121
121
123
126
128
3.6
Jumping
134
135
138
143
3.7
Coordinated Movement
144
3.7.1
Fixed Formations
144
3.7.2
Scalable Formations
146
3.7.3
Emergent Formations
146
3.7.4
Two-Level Formation Steering
147
3.7.5
Implementation
151
3.2.2
Wandering
3.2.3
On the Website
STEERING BEHAVIORS
3.3.1
Steering Basics
3.3.2
Variable Matching
3.3.3
Seek and Flee
3.3.4
Arrive
3.3.5
Align
3.3.6
Velocity Matching
3.3.7
Delegated Behaviors
3.3.8
Pursue and Evade
3.3.9
Face
3.3.10
Looking Where You're Going
3.3.11
Wander
3.3.12
Path Following
3.3.13
Separation
3.3.14
Collision Avoidance
3.3.15
Obstacle and Wall Avoidance
3.3.16
Summary
Combining Steering Behaviors
3.4.1
Blending and Arbitration
3.4.2
Weighted Blending
3.4.3
Priorities
3.4.4
Cooperative Arbitration
3.4.5
Steering Pipeline
Predicting Physics
3.5.1
Aiming and Shooting
3.5.2
Projectile Trajectory
3.5.3
The Firing Solution
3.5.4
Projectiles with Drag
3.5.5
Iterative Targeting
Jumping
3.6.1
Jump Points
3.6.2
Landing Pads
3.6.3
Hole Fillers
3.7.6
Extending to More than Two Levels
157
3.7.7
Slot Roles and Better Assignment
159
3.7.8
Slot Assignment
162
3.7.9
Dynamic Slots and Plays
166
3.7.10
Tactical Movement
168
3.8
MOTOR CONTROL
171
3.8.1
Output Filtering
172
3.8.2
Capability-Sensitive Steering
174
3.8.3
Common Actuation Properties
175
3.9
MOVEMENT IN THE THIRD DIMENSION
178
3.9.1
Rotation in Three Dimensions
178
3.9.2
Converting Steering Behaviors to Three Dimensions
180
3.9.3
Align
180
3.9.4
Align to Vector
181
3.9.5
Face
183
3.9.6
Look Where You're Going
186
3.9.7
Wander
186
3.9.8
Faking Rotation Axes
188
Exercises
192
Chapter
PATHFINDING
197
4.1
The Pathfinding Graph
198
198
199
202
203
203
4.2
Dijkstra
204
205
206
210
212
214
214
4.3
A*
215
216
216
220
223
The
Pathfinding Graph
4.1.1
Graphs
4.1.2
Weighted Graphs
4.1.3
Directed Weighted Graphs
4.1.4
Terminology
4.1.5
Representation
Dijkstra
4.2.1
The Problem
4.2.2
The Algorithm
4.2.3
Pseudo-Code
4.2.4
Data Structures and Interfaces
4.2.5
Performance of
Dijkstra
4.2.6
Weaknesses
A*
4.3.1
The Problem
4.3.2
The Algorithm
4.3.3
Pseudo-Code
4.3.4
Data Structures and Interfaces
4.3.5 Implementation Notes 228
4.3.6
Algorithm
Performance 228
4.3.7
Node Array A*
229
4.3.8
Choosing a Heuristic
231
4.4
World Representations
237
4.4.1
Tile Graphs
239
4.4.2
Dirichlet Domains
241
4.4.3
Points of Visibility
244
4.4.4
Navigation Meshes
246
4.4.5
Non-Translational Problems
251
4.4.6
Cost Functions
251
4.4.7
Path Smoothing
251
4.5
Improving on A*
255
4.6
Hierarchical Pathfinding
255
4.6.1
The Hierarchical Pathfinding Graph
256
4.6.2
Pathfinding on the Hierarchical Graph
259
4.6.3
Hierarchical Pathfinding on Exclusions
262
4.6.4
Strange Effects of Hierarchies on Pathfinding
263
4.6.5
Instanced Geometry
265
4.7
Other Ideas in Pathfinding
271
4.7.1
Open Goal Pathfinding
272
4.7.2
Dynamic Pathfinding
272
4.7.3
Other bunds of Information Reuse
273
4.7.4
Low Memory Algorithms
273
4.7.5
Interruptible Pathfinding
274
4.7.6
Pooling Planners
275
4.8
Continuous Time Pathfinding
276
4.8.1
The Problem
276
4.8.2
The Algorithm
277
4.8.3
Implementation Notes
281
4.8.4
Performance
281
4.8.5
Weaknesses
282
4.9
Movement Planning
282
4.9.1
Animations
282
4.9.2
Movement Planning
283
4.9.3
Example
286
4.9.4
Footfalls
287
Exercises
288
Chapter
Decision Making
293
5.1
Overview of Decision Making
293
5.2
Decision Trees
295
5.2.1
The Problem
295
5.2.2
The Algorithm
295
5.2.3
Pseudo-Code
300
5.2.4
On the Website
302
5.2.5
Knowledge Representation
303
5.2.6
Implementation Nodes
303
5.2.7
Performance of Decision Trees
304
5.2.8
Balancing the Tree
304
5.2.9
Beyond the Tree
305
5.2.10
Random Decision Trees
306
5.3
State Machines
309
5.3.1
The Problem
311
5.3.2
The Algorithm
311
5.3.3
Pseudo-Code
311
5.3.4
Data Structures and Interfaces
312
5.3.5
On the Website
315
5.3.6
Performance
316
5.3.7
Implementation Notes
316
5.3.8
Hard-Coded FSM
316
5.3.9
Hierarchical State Machines
318
5.3.10
Combining Decision Trees and State Machines
331
5.4
Behavior Trees
334
5.4.1
Implementing Behavior Trees
340
5.4.2
Pseudo-Code
340
5.4.3
Decorators
345
5.4.4
Concurrency and Timing
351
5.4.5
Adding Data to Behavior Trees
361
5.4.6
Reusing Trees
365
5.4.7
Limitations of Behavior Trees
370
5.5
Fuzzy Logic
371
5.5.1
A Warning
371
5.5.2
Introduction to Fuzzy Logic
371
5.5.3
Fuzzy Logic Decision Making
381
5.5.4
Fuzzy State Machines
390
5.6
Markov Systems
395
5.6.1
Markov Processes
396
5.6.2
Markov State Machine
398
401
402
404
406
408
413
418
425
427
428
433
433
434
441
441
443
445
455
459
459
459
460
461
462
464
465
467
468
468
470
474
479
480
480
484
485
487
5.7
Goal-Oriented Behavior
5.7.1
Goal-Oriented Behavior
5.7.2
Simple Selection
5.7.3
Overall Utility
5.7.4
Timing
5.7.5
Overall Utility GOAP
5.7.6
GOAP with IDA*
5.7.7
Smelly GOB
5.8
Rule-Based Systems
5.8.1
The Problem
5.8.2
The Algorithm
5.8.3
Pseudo-Code
5.8.4
Data Structures and Interfaces
5.8.5
Implementation Notes
5.8.6
Rule Arbitration
5.8.7
Unification
5.8.8
Rete
5.8.9
Extensions
5.8.10
Where Next
5.9
Blackboard Architectures
5.9.1
The Problem
5.9.2
The Algorithm
5.9.3
Pseudo-Code
5.9.4
Data Structures and Interfaces
5.9.5
Performance
5.9.6
Other Things Are Blackboard Systems
5.10
Scripting
5.10.1
Language Facilities
5.10.2
Embedding
5.10.3
Choosing a Language
5.10.4
A Language Selection
5.10.5
Rolling Your Own
5.10.6
Scripting Languages and Other
AI
5.11
Action Execution
5.11.1
Types of Action
5.11.2
The Algorithm
5.11.3
Pseudo-Code
5.11.4
Data Structures and Interfaces
5.11.5 Implementation
Notes
489
5.11.6 Performance 490
5.11.7
Putting
It All Together
490
Chapter
Ö
Tactical and strategic
ai
493
6.1
Waypoint Tactics
494
6.1.1
Tactical Locations
494
6.1.2
Using Tactical Locations
502
6.1.3
Generating the Tactical Properties of a Waypoint
507
6.1.4
Automatically Generating the Waypoints
512
6.1.5
The Condensation Algorithm
513
6.2
Tactical Analyses
518
6.2.1
Representing the Game Level
518
6.2.2
Simple Influence Maps
519
6.2.3
Terrain Analysis
525
6.2.4
Learning with Tactical Analyses
527
6.2.5
A Structure for Tactical Analyses
528
6.2.6
Map Flooding
533
6.2.7
Convolution Filters
538
6.2.8
Cellular Automata
549
6.3
Tactical Pathfinding
553
6.3.1
The Cost Function
553
6.3.2
Tactic Weights and Concern Blending
555
6.3.3
Modifying the Pathfinding Heuristic
557
6.3.4
Tactical Graphs for Pathfinding
557
6.3.5
Using Tactical Waypoints
558
6.4
Coordinated Action
559
6.4.1
Multi-Tier
AI
559
6.4.2
Emergent Cooperation
565
6.4.3
Scripting Group Actions
568
6.4.4
Military Tactics
573
Exercises
576
Chapter
Learning
579
7.1
Learning Basics
579
7.1.1
Online or Offline Learning
579
7.1.2
Intra-
Behavior Learning
580
7.1.3
Inter-Behavior Learning
581
7.1.4
A Warning
581
7.1.5
Over-Learning
582
7.1.6
The Zoo of Learning Algorithms
582
7.1.7
The Balance of Effort
582
7.2
PARAMETER MODIFICATION
583
7.2.1
The Parameter Landscape
583
7.2.2
Hill Climbing
585
7.2.3
Extensions to Basic Hill Climbing
588
7.2.4
Annealing
591
7.3
Action Prediction
596
7.3.1
Left or Right
596
7.3.2
Raw Probability
596
7.3.3
String Matching
597
7.3.4
N-Grams
597
7.3.5
Window Size
601
7.3.6
Hierarchical N-Grams
602
7.3.7
Application in Combat
605
7.4
DECISION LEARNING
606
7.4.1
Structure of Decision Learning
606
7.4.2
What Should You Learn?
607
7.4.3
Four Techniques
607
7.5
Naive
Bayes
Classifiers
608
7.5.1
Implementation Notes
612
7.6
Decision Tree learning
613
7.6.1
ID3
613
7.6.2
ID3 with Continuous Attributes
622
7.6.3
Incremental Decision Tree Learning
626
7.7
Reinforcement Learning
631
7.7.1
The Problem
631
7.7.2
The Algorithm
632
7.7.3
Pseudo-Code
635
7.7.4
Data Structures and Interfaces
636
7.7.5
Implementation Notes
637
7.7.6
Performance
637
7.7.7
Tailoring Parameters
638
7.7.8
Weaknesses and Realistic Applications
641
7.7.9
Other Ideas in Reinforcement Learning
644
7.8
Artificial Neural Networks
646
7.8.1
Overview
647
7.8.2
The Problem
649
650
654
655
657
658
658
EXERCISES
662
7.8.3
The Algorithm
7.8.4
Pseudo-Code
7.8.5
Data Structures and Interfaces
7.8.6
Implementation Caveats
7.8.7
Performance
7.8.8
Other Approaches
Chapter
8
Board Games
667
8.1
Game Theory
668
8.1.1
Types of Games
668
8.1.2
The Game Tree
669
8.2
MlNlMAXING
671
8.2.1
The Static Evaluation Function
672
8.2.2
Minimaxing
674
8.2.3
The Minimaxing Algorithm
675
8.2.4
Negamaxing
678
8.2.5 AB
Pruning
681
8.2.6
The
AB
Search Window
684
8.2.7
Negascout
686
8.3
Transposition Tables and Memory
689
8.3.1
Hashing Game States
689
8.3.2
What to Store in the Table
692
8.3.3
Hash Table Implementation
693
8.3.4
Replacement Strategies
694
8.3.5
A Complete Transposition Table
695
8.3.6
Transposition Table Issues
696
8.3.7
Using Opponent's Thinking Time
696
8.4
memory-Enhanced Test algorithms
697
8.4.1
Implementing Test
697
8.4.2
The MTD Algorithm
699
8.4.3
Pseudo-Code
700
8.5
Opening Books and Other Set Plays
701
8.5.1
Implementing an Opening Book
702
8.5.2
Learning for Opening Books
702
8.5.3
Set Play Books
703
8.6
Further Optimizations
703
8.6.1
Iterative Deepening
704
8.6.2
Variable Depth Approaches
705
8.7
Turn-Based Strategy Games
706
8.7.1
Impossible Tree Size
706
8.7.2
Real-Time
AI in a
Turn-Based Game
708
Exercises
708
Part III
Supporting Technologies
711
Chapter
713
714
714
722
724
726
728
731
732
732
732
733
734
740
743
Exercises
744
Execution Management
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
745
10.1
Communication
745
10.2
Getting Knowledge Efficiently
746
10.2.1
Polling
746
10.2.2
Events
747
10.2.3
Determining What Approach to Use
748
10.3
Event Managers
748
10.3.1
Implementation
750
10.3.2
Event Casting
753
10.3.3
Inter-Agent Communication
755
10.4
Polling Stations
756
10.4.1
Pseudo-Code
756
10.4.2
Performance
757
10.4.3
Implementation Notes
757
10.4.4
Abstract Polling
758
10.5
SENSE MANAGEMENT
759
10.5.1
Faking It
760
10.5.2
What Do We Know?
760
10.5.3
Sensory Modalities
761
10.5.4
Region Sense Manager
767
10.5.5
Finite Element Model Sense Manager
775
Exercises
783
Chapter
11
Tools and Content Creation
785
11.0.1
Toolchains Limit
AI
786
11.0.2
Where
AI
Knowledge Comes from
786
11.1
KNOWLEDGE FOR PATHFINDING AND WAYPOINT TACTICS
786
11.1.1
Manually Creating Region Data
787
11.1.2
Automatic Graph Creation
789
11.1.3
Geometric Analysis
790
11.1.4
Data Mining
793
11.2
KNOWLEDGE FOR MOVEMENT
795
11.2.1
Obstacles
795
11.2.2
High-Level Staging
797
11.3
Knowledge for Decision making
798
11.3.1
Object Types
798
11.3.2
Concrete Actions
798
11.4
The Toolchain
799
11.4.1
Data-Driven Editors
799
11.4.2
AI
Design Tools
800
11.4.3
Remote Debugging
801
11.4.4
Plug-Ins
802
Exercises
802
Chapter
12
DESIGNING GAME
Al
807
12.1
The Design
807
12.1.1
Example
808
12.1.2
Evaluating the Behaviors
809
12.1.3
Selecting Techniques
811
12.1.4
The Scope of One Game
813
12.2
SHOOTERS
814
12.2.1
Movement and Firing
814
12.2.2
Decision Making
816
12.2.3
Perception
817
12.2.4
Pathfinding and Tactical
AI
818
12.2.5
Shooter-Like Games
818
12.3
Driving
820
12.3.1
Movement
821
12.3.2
Pathfinding and Tactical
AI
822
12.3.3
Driving-Like Games
823
12.4
Real-Time Strategy
823
12.4.1
Pathfinding
824
12.4.2
Group Movement
825
12.4.3
Tactical and Strategic
AI
825
12.4.4
Decision Making
826
12.5
Sports
827
12.5.1
Physics Prediction
827
12.5.2
Playbooks and Content Creation
828
12.6
Turn-based Strategy Games
828
12.6.1
Timing
829
12.6.2
Helping the Player
830
Chapter
13
ai-Based Game Genres 83i
13.1
Teaching Characters
831
13.1.1
Representing Actions
832
13.1.2
Representing the World
832
13.1.3
Learning Mechanism
833
13.1.4
Predictable
Mental
Models
and Pathological
States
835
13.2
Flocking and Herding Games
836
13.2.1
Making the Creatures
836
13.2.2
Tuning Steering for Interactivity
837
13.2.3
Steering Behavior Stability
838
13.2.4
Ecosystem Design
838
Appendix
REFERENCES
841
A.1 Books, Periodicals, and Papers
841
A.2 Games
842
Index
847 |
| any_adam_object | 1 |
| author | Millington, Ian Funge, John David 1968- |
| author_GND | (DE-588)140001026 |
| author_facet | Millington, Ian Funge, John David 1968- |
| author_role | aut aut |
| author_sort | Millington, Ian |
| author_variant | i m im j d f jd jdf |
| building | Verbundindex |
| bvnumber | BV036098497 |
| classification_rvk | ST 300 ST 324 SU 500 |
| classification_tum | DAT 700f DAT 758f |
| ctrlnum | (OCoLC)634245831 (DE-599)BVBBV036098497 |
| 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 |
| edition | 2. ed. |
| format | Book |
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| id | DE-604.BV036098497 |
| illustrated | Illustrated |
| indexdate | 2025-02-20T07:11:43Z |
| institution | BVB |
| isbn | 9780123747310 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-018988926 |
| oclc_num | 634245831 |
| open_access_boolean | |
| owner | DE-523 DE-739 DE-Aug4 DE-703 DE-91G DE-BY-TUM DE-355 DE-BY-UBR DE-706 DE-861 DE-20 DE-384 DE-859 |
| owner_facet | DE-523 DE-739 DE-Aug4 DE-703 DE-91G DE-BY-TUM DE-355 DE-BY-UBR DE-706 DE-861 DE-20 DE-384 DE-859 |
| physical | XXIII, 870 S. Ill., graph. Darst. |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | CRC Press |
| record_format | marc |
| spellingShingle | Millington, Ian Funge, John David 1968- Artificial intelligence for games Künstliche Intelligenz (DE-588)4033447-8 gnd Computerspiel (DE-588)4010457-6 gnd Programmierung (DE-588)4076370-5 gnd |
| subject_GND | (DE-588)4033447-8 (DE-588)4010457-6 (DE-588)4076370-5 |
| title | Artificial intelligence for games |
| title_auth | Artificial intelligence for games |
| title_exact_search | Artificial intelligence for games |
| title_full | Artificial intelligence for games Ian Millington and John Funge |
| title_fullStr | Artificial intelligence for games Ian Millington and John Funge |
| title_full_unstemmed | Artificial intelligence for games Ian Millington and John Funge |
| title_short | Artificial intelligence for games |
| title_sort | artificial intelligence for games |
| topic | Künstliche Intelligenz (DE-588)4033447-8 gnd Computerspiel (DE-588)4010457-6 gnd Programmierung (DE-588)4076370-5 gnd |
| topic_facet | Künstliche Intelligenz Computerspiel Programmierung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018988926&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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