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Chapter5-advsearch.md

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Chapter 5, adverserial search

  • Multi-Agent enviroments
  • Game must be evaluable by some function
  • Agents try to maximize there score while minimizing oponents score
  • Search till game ends

Minmax search

Perfect for two player games like chess

  • Minmax algorithm
    • Generate complete game tree using deapth-first search
    • Evaluate game states using some utility function
    • From root max chooses the move that leads to the highes value

===

  • Alpha-beta pruning

    • Layerd on top of minmax
    • Searches with deepth first to give alpha and beta some values
    • Any part of the tree that gives worse values gets pruned
    • Alpha = best value of an already explored path for the maximizer
    • Beta = best value of an already explored path for the minimizer
  • When to prune?

  • A maximzer node will prune if its current value is higher then beta

  • A minimizer will prune if its current value is lower than alpha