Neural Network in Backgammon: Analysis of 170,000 Moves and Player Mistakes

⚡ The gist in 5 seconds - A breakdown of a neural network engine for backgammon that selects moves based on training has been published. - The engine was tested on 170,000 moves made by real players — error patterns were identified. - The material is available on Habr; the key challenges are the head rule and the ban on full blocking. ### 🔍 What was found The article on Habr examines how a neural network selects moves in backgammon. The author describes the engine's architecture and explains why the head rule (the inability to place two checkers from a single cell) and the ban on full blocking (when a checker cannot be completely blocked) pose a serious challenge for the move generator. The move selection mechanics are based on position analysis: the neural network was trained on 170,000 moves made by real players. As a result, it was possible not only to reproduce the decision-making logic but also to identify typical human mistakes — for example, underestimating risk when building blocks or suboptimal use of the "head" in the early stages of a game. ### 💡 Why it matters The material shows how AI is applied not to classic chess or Go, but to board games with less formalized strategy. This is useful both for game algorithm developers and for players themselves: analyzing real games helps identify weak points in one's tactics. The article is published in open access, so anyone can study the approach and, if desired, adapt it for other games with similar mechanics.