AI algorithm in game playing

AI algorithm in game playing

• 1.
  Topic : Game playingin AI • PRESENTED BY • VICKY TYAGI • MCA 3RD SEM • 2001560001
• 2.
  Overview of presentation •Algorithm in game playing • Popular algorithm in game playing • Minimax algorithm • Alpha beta pruning algorithm • Most suitable algorithm
• 3.
  Algorithm in gameplaying • Any game works on a particular algorithm • Most strategy based game uses minimax algorithm or alpha- beta pruning algorithm • Games like chess or tic-tac-toe uses these algorithm • The algorithm works as every action possibly took by player and then compare the action with the computer move and then take action • These games uses particular set of action and follow particular set of rules
• 4.
  Minimax algorithm • Here,there are two things i.e minimum and maximum value • Maximum is a move done by user that aims at maximizing the chance of winning • Minimize is a move done by AI that aims at minimizing the chance of losing • Let’s have a example for better understanding
• 5.
  Example for Minimaxalgorithm • As shown in fig 1 given the algorithm have tree to traverse from root A to the terminal nodes • The algorithm assumes max as player(human) and min as AI(machine) • The algorithm is much similar to DFS as the action is taken after traversing last node comparison.
• 6.
  Fig 1: Agame tree for Minimax algorithm
• 7.
  Advantages and disadvantages •The minimax algorithm is not suitable for large game tree. • The minimax algorithm is easy to implement • The time complexity of minimax algorithm is O(b^d). • Every move in game by AI(computer) is taken after several comparisons.
• 8.
  Alpha Beta pruningalgorithm • As soon as the optimum solution is found the algorithm immediately neglects any other solution possible. • Generally alpha is considered for max node and beta is considered for min node. • The aim of algorithm is to have solution in minimum number of moves
• 9.
  Fig 2: Agame tree for Alpha Beta Pruning algorithm