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Arcade
Computing optimal strategies in standard
games and puzzles
Motivated by the desire to automate models of thinking
Optimization in decision problems under
uncertainity
Cryptographic problems that arise in multi-party
protocols
Motivated by the desire to model competitive nature of cryptographic protocols
Modeling Interaction between two mistrustful agents and an eavesdropper
agent
Coordination of processes in distributed
processing systems
Competition for resources and cooperation for solving a problem.
Randomness
Secrecy
Limited Resources
Limited Information
Computer games can be approached from different directions.
Point of view of a programmer - Interested
in creating impressive arcade games with multimedia emphasis
Point of a view of a theoretician - Interested
in developing the best strategy
Point of view of AI - Interested in making
agents paly the games
AI is going
to be more important in games
A great environment
for developing human-level AI
Academic
AI has something to offer the game industry
Applications of AI to Games
Synthetic Characters
Sports games,
FPS, RTS, …
Dynamic Game Control
Modify play
calling based on experience (coaching)
Directing
characters for plot and story
Providing
play-by-play, camera control, …
Development of Games
Automatic
testing
Cognitive Architectures
ACT-R (John Anderson, Carnegie Mellon)
Detailed
models of behavior and learning
10 milliseconds-10 seconds.
Used by cognitive
psychologists
Issues on
performance and scalabiltiy
EPIC (David Kieras, University of Michigan)
Detailed
models of human-computer interaction (no learning)
Emphasizes models of perception and action
50 milliseconds-10 seconds
Used by cognitive
psychologist
Soar (John Laird, University of Michigan)
Models of
human behavior and learning
50 milliseconds-1 hour
Emphasis more on complex behavior and learning
Used by AI
researchers and cognitive psychologist