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Playing Games with
Computational Intelligence
• 理論
• 離散數學、計算理論…
• 硬體
• 邏輯電路設計、計算機結構…
• 軟體---程式設計
– 系統軟體
• 作業系統、資料庫系統、編譯程式…
– 應用軟體
• 多媒體應用、網際網路應用、人工智慧應用…
Artificial Intelligence
Automatic Theorem Proving
Heuristic Search---Computer Game Playing
Machine Learning
Computer Vision
Natural Language Processing
Computer Game Playing
• Offering a diverse range of engaging problems
and applications
• For the first few decades
– Beating expert human players at some of the most
challenging board games
• Over the last decade
– Investigating the application of AI and CI to video
Artificial Intelligence vs.
Computational Intelligence
• Artificial Intelligence(AI)
– Deals with the development of machine
intelligence by any means
• Computational Intelligence(CI)
– Deals with algorithms and architectures that
enables intelligent behavior to emerge via
statistical processes
Game Tree Search
• Conventional techniques
– Mini-max search with alpha-beta pruning
– Two features
• A good evaluation function
• A low or modest branching factor
• Lead only to modest levels of play and
offered no threat to expert human player
for Computer GO
Monte Carlo algorithms
• Rely on random sampling and simulated
• Playing random moves until the end of the
• The win/lose statistics are then used to
estimate the value of that position
Monte Carlo Tree Search
• Selectively building up a tree of explored
• Use the Upper Confidence Bounds for Trees
method for the selection policy
• Have made truly astonishing progress in the
world of Computer GO
• More CPU leads to more simulated play which
leads to higher quality actual play
General Game Playing
• A way to make games a true challenge for
machine learning
• Operate in two phases
– First—the game rules are given to each player
– Second– play commences and continue until the
end of the game
• Use a logic based game description language
• Not appropriate for video games
Video Games
• As an application of computational
• As a test-bed for computational intelligence
• Hand-programmed with a relatively small
number of parameters adapted using
evolutionary algorithms
• Still leaves much room for improvement
• Playing Games with Computation Intelligence
– Simon M. Lucas
• Monte Carlo GO
– B. Brugmann
• Bandit based Monte Carlo planning
– L. Krocsis and C. Szepesvari
• General game playing: Overview of the AAAI
– M.R. Genesereth, N. Love, and B. Pell