State of the Art Game Programs

State of the Art Game Programs

While developing game playing applications the incremental approach can be taken. First stage is, human plays against human. In this stage program serve as a representation. The program checks for legal moves. In second stage, human plays against program in which legal and good moves are made by program. The program acts as coach in this stage. In third stage, program plays against program. The learning component of the program enhances by playing games.

In game playing good, evaluation function is crucial factor. A evaluation function should consider all the factors like number of pieces, values associated with each square on the board. Searching, looking ahead and exploring alternatives should be tried using evaluation function.

Game Playing Case Studies

• Checkers (Samuels, Chinook) : Chinook ended 40-year-reign of human world champion Marion Tinsley in 1994. It used an endgame database defining perfect play for all positions involving 8 or fewer pieces on the board, a total of 443,748,401,247 positions. Checkers is now solved!

• Chess: Deep Blue defeated human world champion Gary Kasparov in a six-game match in 1997. Deep Blue examined 200 million positions per second, used very sophisticated evaluation and undisclosed methods for extending some lines of search up to 40 ply. Current programs are even better, if less historic.

• Othello (Logistello): In 1997, Logistello defeated human champion by six games to none. Human champions refuse to compete against computers, which are too good.

• Go (Goemate, Go4++): Human champions are beginning to be challenged by machines, though the best humans still beat the best machines. In Go, b> 300, so most programs use pattern knowledge bases to suggest plausible moves, along with aggressive pruning.

• Backgammon (Tesauro's TD-gammon): Neural-net learning program TD Gammon is one of world's top 3 players.

Human against program - Incremental Addition to the "Smartness" of the program:

1. Play randomly (but legal, may involve a non-trivial amount of knowledge/computation).

2. Have a static value associated with each square on the board.

3. Have a dynamic value associated with each square on the board.

4. Have an evaluation function taking other factors into account (for example, no. of pieces).