Skip to main navigation
Skip to search
Skip to main content
Maastricht University Home
Support & FAQ
Link opens in a new tab
Search content at Maastricht University
Home
Researchers
Publications
Activities
Press/Media
Prizes
Organisations
Dataset/Software
Projects
Learning state-action features for general game playing
Dennis J.N.J. Soemers
Department of Advanced Computing Sciences
Department of Advanced Computing Sciences
Research output
:
Thesis
›
Doctoral Thesis
›
Internal
1256
Downloads (Pure)
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Learning state-action features for general game playing'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
General Game Playing
100%
Computer Program
100%
State Action
100%
Action Features
100%
Learning State
100%
Board Games
50%
Game Design
50%
Intelligence Level
50%
Cultural Artifacts
50%
Game Development
50%
Artificial Intelligence Research
50%
Development Industry
50%
Design Industry
50%
Algorithm Program
50%
Playing Level
50%
Making Decisions
50%
Anthropological Studies
50%
Cultural Games
50%
Artifact Design
50%
Computer Science
Game Playing
100%
Game Design
100%
Independent Algorithm
100%
Intelligence Research
100%
Industry Development
100%
Learning State
100%
Cultural Artifact
100%
Artificial Intelligence
100%
INIS
algorithms
100%
levels
100%
learning
100%
computer programs
100%
humans
50%
design
50%
industry
50%
artificial intelligence
50%