:rocket:With iterative deepening, we can improve our chess program's search process by searching deeper moves as the iterations progress.
:hourglass:Search extensions allow us to prioritize moves that lead to checkmate or pawn promotion, leading to more efficient and effective search results.
:chart_with_upwards_trend:By continuously testing and making improvements, our chess AI program shows consistent progress and performs well against previous versions.
What is iterative deepening?
—Iterative deepening is a technique where we progressively search deeper moves as the iterations progress, ensuring that the program always has the best move from the previous iteration.
How do search extensions improve the program?
—Search extensions prioritize moves that lead to checkmate or pawn promotion, allowing the program to focus on more relevant and potentially game-changing moves.
How does the program compare to previous versions?
—The program shows consistent progress and performs well against previous versions, demonstrating the effectiveness of the improvements implemented.
00:00Introduction to the video and the focus on chess AI development journey.
02:56Improving the search process with iterative deepening and testing the program against previous versions.
06:12Exploring the concept of search extensions to prioritize moves leading to checkmate or pawn promotion.
10:06Comparing the program's performance against previous versions and discussing consistent progress.
