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CSE 4705 Artificial Intelligence
Jinbo Bi Department of Computer Science & Engineering
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Informed search strategies
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Random restart – a remedy to the drawbacks
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Local beam search
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Simulated annealing
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Simulated annealing (SA)
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Simulated annealing (SA)
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Simulated annealing algorithm
Example of simulated annealing Example codes
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Simulated annealing example
h = 1, t=1, T = T0/2 = 50 Because T ~= 0, so Next = a random move Δ E = E1 – E0 = 1 If Δ E < 0, Current = Next Else compute p = e (- Δ E/T) =0.98 Generate a random number r from a uniform [0,1] Assume r = 0.8 Then, Current = Next Initial state T0 = 100 Annealing schedule is T T/2
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Simulated annealing example
h = 1, t=1, T = T0/2 = 50 Because T ~= 0, so Next = a random move Δ E = E1 – E0 = 1 If Δ E < 0, Current = Next Else compute p = e (- Δ E/T) =0.98 Generate a random number r from a uniform [0,1] Assume r = 0.8 Then, Current = Next Initial state T0 = 100 Annealing schedule is T T/2
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Simulated annealing (variant)
Another annealing scheduling
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Genetic algorithms
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Genetic algorithms
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Representation: strings of genes
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Encoding of a chromosome
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Example: genetic algorithms for drive train
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Operations: crossover
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Operations: mutation
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The basic genetic algorithm
A short video explains GA in 3 minutes
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Genetic algorithm: 8-queens
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Game playing and adversarial search
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Games If possible, we can talk about minimax rule and search
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Motivation
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The simplest game environment
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Other properties of the simplest games
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More complicated games
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Formalizing the game setup
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How to play a game by searching
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Game trees
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Game trees
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Hexapawn: three possible first moves
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Hexapawn: three possible first moves
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MAX & MIN nodes: an egocentric view
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Evaluation functions: f(n)
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A partial game tree for Tic-Tac-Toe
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The Minimax rule
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The minimax rule Don’t play hope chess
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The minimax procedure
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2-ply Minimax example
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What if MIN does not play optimally?
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Comments on Minimax search
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Questions?
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