What is secondary search in artificial intelligence

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Secondary search, also known as a quiescence search or a tactical search, is a specialized search technique used in artificial intelligence and game playing to address issues related to unstable or tactical positions within a search tree. This technique is particularly relevant in games with complex tactical interactions, such as chess or other board games. The primary purpose of a secondary search is to ensure that the evaluation of a position takes into account critical tactical considerations and avoids making decisions based on unstable or temporary threats.

Here's how secondary search works:

Primary Search: During the main search (e.g., minimax, alpha-beta pruning), the AI algorithm explores the game tree to a certain depth, analyzing different moves and positions.

Unstable Positions: In some cases, the primary search might lead to positions where tactics are still in flux, and the evaluation could be misleading due to temporary threats or captures.

Secondary Search: When the primary search reaches a position with potential instability or tactical activity, a secondary search is triggered.

Quiescence Search: The secondary search, often referred to as a quiescence search, focuses only on "quiet" positions, which are states where no major tactical activities are ongoing. It examines forcing moves such as captures, checks, and material gains.

Depth-Limited Search: The quiescence search is typically limited to a certain depth or until the position stabilizes. It ensures that the analysis is focused on the most critical aspects of the position.

Evaluation: The evaluation function used during the quiescence search is designed to capture tactical threats, material imbalances, and other relevant factors that might affect the position's stability.

Cutoff and Return: Once the quiescence search is completed, the AI algorithm returns the evaluated score to the main search, which can then use this more accurate evaluation to make better-informed decisions.

The primary goal of secondary search is to address the horizon effect and prevent premature cutoffs based on unstable tactical situations. By focusing on critical moves and tactical considerations in the quiescence search, the AI algorithm can make more reliable and informed decisions that take into account potential threats and captures that might otherwise be overlooked.

Secondary search is particularly useful in scenarios where tactical interactions play a significant role and where the main search might not adequately capture the dynamic nature of the position. It helps ensure that the AI's evaluation of the game state is more accurate and comprehensive, leading to improved decision-making in complex and tactical game environments.