Chess is a two player game, with white piece to start the game. It involves many complex rules with different types of pieces. It consists of 63 squares (8 by 8) making the number of legal moves in an average position between 15 and 25.
Chess is almost completely analytical and it uses the left hand side of the brain.
GO is a two player game, with black piece to start the game. It involves two simple rules with only one type of piece. It consists of 361 intersections (19 by 19) making the number of legal moves in an average position around 250.
The game GO uses both sides of the brain (left & right) as it requires analytical thinking as well as pattern recognition.
Chess is a complex game with different types of pieces with a lot of analytical thinking where as in GO, each player has only one type of piece which can be placed on the board. The game Go, involves analytical thinking but in addition human use pattern recognition to find the best possible move. Professional Chess players almost always make move with a plan where as in GO, players often play by instinct.
Even thought chess involves complex rules, it has only 15 to 25 numbers of legal moves in an average position compare to 250 in the game of GO. This makes the game GO more challenging to program.
Artificial Intelligence (AI) in Chess has been developed from many years ago. There have been many researches and designs and some excellent algorithms. In 1996 a program called “Deep Blue” by the IBM Company managed to defeat the world champion Garry Kasparov. This proofs that computers can now perform chess playing better than humans due to good algorithm use.
There are many different algorithms used in Chess such as Iterative Deeping, Quiescent Search, Analysis Function etc. The most used and expert algorithm is the Mini-max search which is based on Adversarial Search. “Competitive environments, in which the agents' goals are in conflict, give rise to adversarial search problems”. After the game “Opening” where initial movements have been made, players need to come up with a plan. This stage is called the “Middle Game” where alpha-beta pruning is used in order to avoid ineffectual depth search.
Although, the game GO is mostly implemented using the same Mini-max tree algorithm as in Chess, it doesn't produce such a competitive computer player. One of the reasons that expert computer player cannot be achieved is the wide range of movements during the game. Another reason is that the computer needs to perform similar to human brain in order to achieve the same result.
There are number of solutions and algorithms still being researched in this area. One method is the Monte-Carlo method which selects the best move from a huge list of randomly simulated movements. Another solution is neural network and genetic algorithm which are part of Machine Learning method which allow the computer to come up with strategies and plans based on the programmers. Knowledge based systems in other hand is also a very good method in order for the computer to make decision similar to the human brain. This system records all the previous games played by professional human players and learns good moves and bad moves from past experiences.
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