Chess-playing algorithms are a classic application of Reinforcement Learning (RL) in machine learning.

In RL, an agent (chess program) interacts with an environment (chessboard/game state).

It learns optimal strategies (policies) by trial and error, guided by reward signals (e.g., winning the game, capturing pieces).

Famous examples include DeepMind’s AlphaZero and earlier systems like IBM’s Deep Blue, which incorporated reinforcement principles along with heuristics.

Option B (Pattern density): Not a recognized ML paradigm.

Option C (Supervised learning): While supervised ML can be used to predict moves from labeled games, chess strategy learning is best modeled as reinforcement learning.

Option D (Clustering): Not applicable; clustering is unsupervised grouping of data.

Thus, chess-playing algorithms are best categorized as Reinforcement Learning → Option A.