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Minimax (sometimes Minmax, MM[ 1 ] or saddle point[ 2 ]) is a decision rule used in artificial intelligence, decision theory, game theory, statistics, and philosophy for minimizing the possible loss for a worst case (max imum loss) scenario. When dealing with gains, it is referred to as "maximin" – to maximize the minimum gain.
The interrogator is limited to using the responses to written questions to make the determination. [ 1 ] The Turing test, originally called the imitation game by Alan Turing in 1950, [ 2 ] is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human.
v. t. e. Game theory is the study of mathematical models of strategic interactions. [ 1 ] It has applications in many fields of social science, and is used extensively in economics, logic, systems science and computer science. [ 2 ] Initially, game theory addressed two-person zero-sum games, in which a participant's gains or losses are exactly ...
Artificial intelligence has been an integral part of video games since their inception in 1948, first seen in the game Nim. [1] AI in video games is a distinct subfield and differs from academic AI. It serves to improve the game-player experience rather than machine learning or decision making.
Appearance. Artificial intelligence and machine learning techniques are used in video games for a wide variety of applications such as non-player character (NPC) control and procedural content generation (PCG). Machine learning is a subset of artificial intelligence that uses historical data to build predictive and analytical models.
Artificial intelligence (AI), in its broadest sense, is intelligence exhibited by machines, particularly computer systems.It is a field of research in computer science that develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize their chances of achieving defined goals. [1]
Alpha–beta pruning is a search algorithm that seeks to decrease the number of nodes that are evaluated by the minimax algorithm in its search tree. It is an adversarial search algorithm used commonly for machine playing of two-player combinatorial games (Tic-tac-toe, Chess, Connect 4, etc.). It stops evaluating a move when at least one ...
A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative artificial intelligence. [1][2] The concept was initially developed by Ian Goodfellow and his colleagues in June 2014. [3] In a GAN, two neural networks contest with each other in the form of a zero-sum game ...