The min-max algorithm is a backtracking decision-making process used in artificial intelligence for two-player games, where players aim to optimize their own outcomes. It involves a maximizing player and a minimizing player, and can be enhanced through alpha-beta pruning to improve efficiency by eliminating non-impactful branches. While it ensures optimal decisions, the algorithm faces computational complexity and depth limitations, especially in uncertain environments.

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MINMAX ALGORITHM in machine learning.pptx

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  MINMAX ALGORITHMBYNAME: SURYAKUMARANMROLL.NO: 24CSER025UNIT.NO: 2SUBJECT: FOUNDATION OF ARTIFICIAL INTELLIGENCE
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  WHAT IS MINMAX? The MinMax algorithm, also known as minimax, isa backtracking algorithm used in decisionmaking, game theory and artificial intelligence(AI). It is used to find the optimal move for a player,assuming that the opponent is also playing optimally. The algorithm is mostly employed for game play,such as chess, checkers, tic-tac-toe, go, and othertwo-player games
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  WORKING OF MIN-MAXPROCESS IN AI The Min-Max algorithm is a decision-making process used inartificial intelligence for two-player games. It involves twoplayers: the maximizer and the minimizer, each aiming tooptimize their own outcomes.Maximizing Player (Max): Aims to maximize their score or utility value. Chooses the move that leads to the highest possible utilityvalue, assuming the opponent will play optimally.
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  Cond…Minimizing Player (Min):Aims to minimize the maximizes score or utilityvalue. Selects the move that results in the lowest possibleutility value for the maximize, assuming theopponent will play optimally.
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  EXAMPLE OF MIN-MAXIN ACTION Consider a simplified version of a game where eachplayer can choose between two moves at each turn.Here’s a basic game tree:Max/ Min Min/ / +1 -10 +1
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  Cond.. At theleaf nodes, the utility values are +1, -1, 0, and+1. The minimizing player will choose the minimumvalues from the child nodes: -1 (left subtree) and 0(right subtree). The maximizing player will then choose themaximum value between -1 and 0, which is 0.
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  ALPHA-BETA PRUNING OPTIMIZATIONINMINI-MAX ALGORITHM Alpha-beta pruning enhances the Min-Max algorithmby eliminating branches that do not affect the finaldecision. Alpha (α): The best value that the maximizing playercan guarantee so far. Beta (β): The best value that the minimizing playercan guarantee so far.During the search: If alpha geq beta, prune the remaining branches.
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  STRENGTHS OF THEMIN-MAX ALGORITHMOptimal Decision Making: The Min-Max algorithm ensures optimaldecision making by considering allpossible moves and their outcomes. Itprovides a strategic advantage bypredicting the opponent’s best responsesand choosing moves that maximize theplayer’s benefit.
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  Cond..Simplicity and Clarity:The Min-Max algorithm is conceptuallysimple and easy to understand. Itsstraightforward approach of evaluatingand propagating utility values through agame tree makes it an accessible andwidely taught algorithm in AI.
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  WEAKNESSES OF THEMIN-MAXALGORITHMComputational Complexity: The primary drawback of the Min-Maxalgorithm is its computational complexity.As the depth and branching factor of thegame tree increase, the number of nodesto be evaluated grows exponentially. Thismakes it computationally expensive andimpractical for games with deep andcomplex trees, like Go.
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  Cond…Depth Limitations: Tomanage computational demands, theMin-Max algorithm often limits the depthof the game tree. However, this can leadto suboptimal decisions if critical moves liebeyond the chosen depth. Balancing depthand computational feasibility is asignificant challenge.
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  Cond…Handling of UncertainEnvironments: The Min-Max algorithm assumesdeterministic outcomes for each move,which may not be realistic in uncertain orprobabilistic environments. Real-worldscenarios often involve uncertainty andincomplete information, requiringmodifications to the basic Min-Maxapproach.
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  CONCLUSION In summary,the minimax algorithm helpsthe AI make optimal decisions byconsidering the best and worst possibleoutcomes for each move, assuming bothplayers play perfectly.
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