Your SlideShare is downloading. ×
AI: AI & Searching
Upcoming SlideShare
Loading in...5

Thanks for flagging this SlideShare!

Oops! An error has occurred.

Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply

AI: AI & Searching


Published on

AI: AI & Searching

AI: AI & Searching

Published in: Technology

  • Be the first to comment

No Downloads
Total Views
On Slideshare
From Embeds
Number of Embeds
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

No notes for slide


  • 1. AI & Searching
  • 2. How to avoid repeated states during search?
    Three ways to deal with repeated state
    1- Do not return to the state you just came from.
    2- Do not create paths with cycles in them.
    3- Do not generate any state that was ever generated before
  • 3. Constraint satisfaction search
    A constraint satisfaction problem (or CSP) is a special kind of problem that satisfies some additional structural properties beyond the basic requirements for problems in general.
  • 4. Informed search method
    Best first search :
    When the nodes are ordered so that the one with the best evaluation is expanded first, the resulting strategy is called best-first search.
  • 5. Informed search
    Greedy search:
    The node whose state is judged to be closest to the goal state is always expanded first.
  • 6. Memory Bounded Search
    Iterative deepening A* search (IDA*)
    IDA* is a variant of the A* search algorithm which uses iterative deepening to keep the memory usage lower than in A*.
    It is an informed search based on the idea of the uninformed iterative deepening search.
    SMA* search is another is another example of Memory bounded search
  • 7. Iterative Improvement Algorithms
    The general idea is to start with a complete configuration and to make modifications to improve its quality interactively.
    Hill-climbing search
    Simulated annealing
  • 8. Games as Search Problem
    A game can be defined as a search problem with the following components: 
    The initial state, which includes the board position and an indication of whose move it is ,
    A set of operators, which define the legal moves that a player can make.
    A terminal test, which determines when the game is over. States where the game has ended are called terminal states.
    A utility function (also called a payoff function), which gives a numeric value for the outcome of a game.
  • 9. Perfect decision in two person games
    An algorithm for calculating mini-max decisions.
    It returns the operator that corresponding to the move that leads to the outcome with the best utility, under the assumption that the opponent plays to minimize utility.
    The function MINIMAX-VALUE goes through the whole game tree, all the way to the leaves,to determine the backed-up value of a state.
    The process of eliminating a branch of the search tree from consideration without examining it is called pruning the search tree.
    An example of particular technique will be alpha-beta pruning.
    When applied to a standard mini-max tree, it returns the same move as mini-max would, but prunes away branches that cannot possibly influence the final decision.
  • 11. Effectiveness of alpha-beta pruning algorithm
    The effectiveness of alpha-beta depends on the ordering in which the successors are examined.
    The alpha-beta search algorithm, It does the same computation as a normal mini-max, but prunes the search tree.
  • 12. Visit more self help tutorials
    Pick a tutorial of your choice and browse through it at your own pace.
    The tutorials section is free, self-guiding and will not involve any additional support.
    Visit us at