Pattern Recognition in Computer Games: Exploring Strategies and Decision-Making

 
Pattern Recognition in
Computer Games
 
Chumphol Bunkhumpornpat
Department of Computer Science
Faculty of Science
Chiang Mai University
 
2
 
204453: Pattern Recognition
 
Google AI beats top human players
at strategy game 
StarCraft II
 
AlphaStar was built by Google’s AI firm
DeepMind.
a machine capable of learning or understanding
any task that humans can
AlphaStar has 1,026 actions to choose from at
any moment.
DeepMind limited the speed of AlphaStar’s
reflexes to that of experienced human players.
AlphaStar placed within the top 0.5% of all
players on the European server.
 
204453: Pattern Recognition
 
3
 
StarCraft II players battle each other in
a futuristic warzone.
 
204453: Pattern Recognition
 
4
 
Introduction
 
Computer games are an unique application
area for pattern recognition.
Challenging synthetic opponents computer
should recognize the behavior of a human
player.
The purpose of pattern recognition is to
abstract relevant information from the game
world and to construct concepts and to
deduce patterns from this information.
 
204453: Pattern Recognition
 
5
 
Relations between the world,
pattern recognition, and decision-making
 
204453: Pattern Recognition
 
6
 
Pattern Recognition in Computer Games
 
204453: Pattern Recognition
 
7
 
PERSPECTIVE VIEWS OF PATTERN
RECOGNITION IN COMPUTER GAMES
 
Decision Making Levels
Stance Towards Players
Game Graphs
 
204453: Pattern Recognition
 
8
 
Decision Making Levels
 
204453: Pattern Recognition
 
9
 
Strategical
 
Long Period of Time
Large Amount of Data
Inhabitants
Items
Events
High Cost of a Wrong Decision
Speculative: What-If Scenarios
Offline: Background
 
204453: Pattern Recognition
 
10
 
Operational
 
Concrete
Atomatory Entities
Reactive
Short-Term
Real-Time
Online: 
in-the-Field
Irrevocable Problems
 
204453: Pattern Recognition
 
11
 
Tactical
 
connects between strategical and operational
considers a group of entities and their
cooperation
made more frequently than strategical,
pattern recognition has less time to use
The quality cannot be as high as on upper
level.
 
204453: Pattern Recognition
 
12
 
Stance Towards Players
 
204453: Pattern Recognition
 
13
 
Enemy
 
requires 
modus operandi
 of the player
provides challenge
 
204453: Pattern Recognition
 
14
 
Ally
 
accounts human perspective but not decision
making system
Synthetic Reconnaissance Officer
It reports on enemy movement.
It suggests effective counteractions.
 
204453: Pattern Recognition
 
15
 
Neutral
 
Autonomous Camera Director
controls camera movement in sports games
dictates by television practice
Referee
allows the play continue
interprets causality between offence and
subsequent events
Interface should adapt dynamically to the
needs of a player.
 
204453: Pattern Recognition
 
16
 
Game Graph
 
A story progresses linearly.
A game provides an illusion of 
free will
.
Nodes
Game States
Direct Arches
Actions
The game properties can be analyzed through
graph concepts (e.g., repetitiveness
corresponds to cycles in the graph).
 
 
204453: Pattern Recognition
 
17
 
Outdegree
 
The number of direct arches leaving a node
The greater the outdegree means more
freedom the player has.
 
204453: Pattern Recognition
 
18
 
Indegree
 
The number of direct arches entering the
node
Uniqueness of a response  can be measured as
the indegree.
 
 
204453: Pattern Recognition
 
19
 
A linear (a story) allows no diversion
 
204453: Pattern Recognition
 
20
 
node 
S
i
 has an outdegree of 2
 
204453: Pattern Recognition
 
21
 
node 
S
n
 has an indegree of 3.
 
204453: Pattern Recognition
 
22
 
Each action has now a unique response
 
204453: Pattern Recognition
 
23
 
References
 
T. Kaukoranta, J. Smed, and H. Hakonen, Role of
Pattern Recognition in Computer Games,
Proceedings of the 2
nd
 International Conference on
Application and Development of Computer Games,
pp. 189--94, Hong Kong SAR, China, 2003.
https://www.nature.com/articles/
d41586-019-03298-6
 
204453: Pattern Recognition
 
24
Slide Note
Embed
Share

Computer games provide a unique platform for pattern recognition, where synthetic opponents challenge the computer to analyze human player behavior. This process involves abstracting information from the game world, constructing concepts, and deducing patterns. The intersection of pattern recognition, real-time strategy, decision-making levels, and strategic/tactical considerations in games like StarCraft II offers a fascinating insight into AI capabilities and human-machine interactions.


Uploaded on Oct 06, 2024 | 0 Views


Download Presentation

Please find below an Image/Link to download the presentation.

The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author. Download presentation by click this link. If you encounter any issues during the download, it is possible that the publisher has removed the file from their server.

E N D

Presentation Transcript


  1. Pattern Recognition in Computer Games Chumphol Bunkhumpornpat Department of Computer Science Faculty of Science Chiang Mai University

  2. 2 204453: Pattern Recognition

  3. Google AI beats top human players at strategy game StarCraft II AlphaStar was built by Google s AI firm DeepMind. a machine capable of learning or understanding any task that humans can AlphaStar has 1,026 actions to choose from at any moment. DeepMind limited the speed of AlphaStar s reflexes to that of experienced human players. AlphaStar placed within the top 0.5% of all players on the European server. 3 204453: Pattern Recognition

  4. StarCraft II players battle each other in a futuristic warzone. 4 204453: Pattern Recognition

  5. Introduction Computer games are an unique application area for pattern recognition. Challenging synthetic opponents computer should recognize the behavior of a human player. The purpose of pattern recognition is to abstract relevant information from the game world and to construct concepts and to deduce patterns from this information. 5 204453: Pattern Recognition

  6. Relations between the world, pattern recognition, and decision-making 6 204453: Pattern Recognition

  7. Pattern Recognition in Computer Games RTS: Real-Time Strategy Fighting Sports reacts to enemy's frequent moves remedies threats and strategizes reads the match 7 204453: Pattern Recognition

  8. Decision Making Levels Stance Towards Players Game Graphs PERSPECTIVE VIEWS OF PATTERN RECOGNITION IN COMPUTER GAMES 8 204453: Pattern Recognition

  9. Decision Making Levels Strategical Tactical Operational 9 204453: Pattern Recognition

  10. Strategical Long Period of Time Large Amount of Data Inhabitants Items Events High Cost of a Wrong Decision Speculative: What-If Scenarios Offline: Background 10 204453: Pattern Recognition

  11. Operational Concrete Atomatory Entities Reactive Short-Term Real-Time Online: in-the-Field Irrevocable Problems 11 204453: Pattern Recognition

  12. Tactical connects between strategical and operational considers a group of entities and their cooperation made more frequently than strategical, pattern recognition has less time to use The quality cannot be as high as on upper level. 12 204453: Pattern Recognition

  13. Stance Towards Players Enemy Ally Neutral 13 204453: Pattern Recognition

  14. Enemy requires modus operandi of the player provides challenge 14 204453: Pattern Recognition

  15. Ally accounts human perspective but not decision making system Synthetic Reconnaissance Officer It reports on enemy movement. It suggests effective counteractions. 15 204453: Pattern Recognition

  16. Neutral Autonomous Camera Director controls camera movement in sports games dictates by television practice Referee allows the play continue interprets causality between offence and subsequent events Interface should adapt dynamically to the needs of a player. 16 204453: Pattern Recognition

  17. Game Graph A story progresses linearly. A game provides an illusion of free will. Nodes Game States Direct Arches Actions The game properties can be analyzed through graph concepts (e.g., repetitiveness corresponds to cycles in the graph). 17 204453: Pattern Recognition

  18. Outdegree The number of direct arches leaving a node The greater the outdegree means more freedom the player has. 18 204453: Pattern Recognition

  19. Indegree The number of direct arches entering the node Uniqueness of a response can be measured as the indegree. 19 204453: Pattern Recognition

  20. A linear (a story) allows no diversion 20 204453: Pattern Recognition

  21. node Sihas an outdegree of 2 21 204453: Pattern Recognition

  22. node Snhas an indegree of 3. 22 204453: Pattern Recognition

  23. Each action has now a unique response 23 204453: Pattern Recognition

  24. References T. Kaukoranta, J. Smed, and H. Hakonen, Role of Pattern Recognition in Computer Games, Proceedings of the 2ndInternational Conference on Application and Development of Computer Games, pp. 189--94, Hong Kong SAR, China, 2003. https://www.nature.com/articles/ d41586-019-03298-6 24 204453: Pattern Recognition

More Related Content

giItT1WQy@!-/#giItT1WQy@!-/#giItT1WQy@!-/#giItT1WQy@!-/#