AI: AI & Searching


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AI: AI & Searching

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AI: AI & Searching

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