Adversarial Search (Minimax with Alpha-Beta Pruning) - Intro to Artificial Intelligence

The lecture series follows NC State's CSC 411 - Intro to AI with Dr. Adam Gaweda. This lecture is our final lecture in classical AI search - adversarial search. In adversarial search the agent needs to factor in decisions made by another agent, one that will select actions that worsen the original agent's outcome. We do this through minimax by alternating which agent we are modeling. However, minimax alone relies on an exhaustive search which may be an issue when considering algorithm efficiency. To combat this issue we can include alpha-beta pruning to remove searches that would never be considered due to the adversarial agent's decision making process.

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