Before revision:  This paper studies the machine-learning algorithms for developing the game AIs , and provides their structural interpretations. Specifically, chess-playing Deep Blue is a calibrated value function, whereas shogi-playing Bonanza represents an estimated value function via Rust's (1987) nested fixed-point method.
After revision:  This paper studies the machine-learning algorithms to develop AIs and the econometrics of dynamic structural models through the case studies of three famous game AIs. Chess-playing Deep Blue is a calibrated value function, whereas shogi-playing Bonanza represents an estimated value function via Rust's (1987) nested fixed-point method.