Expectimax
Expectimax is a decision-making algorithm used in artificial intelligence and game theory for environments with uncertainty, such as games with random elements or probabilistic outcomes. It extends the minimax algorithm by incorporating chance nodes to model stochastic events, calculating expected values to determine optimal moves. This approach is commonly applied in games like backgammon or poker, where dice rolls or card draws introduce randomness.
Developers should learn Expectimax when building AI agents for games or decision systems involving randomness, as it provides a robust framework for handling uncertainty and optimizing strategies. It is particularly useful in scenarios like adversarial games with chance elements, simulation-based planning, or any application requiring probabilistic reasoning to make informed decisions under risk.