#Best kasparov chess computer pro#

We can see that the winning board has the maximum utility, so our utility rule isnвЂ™t totally useless, although it would certainly benefit from a вЂњdefensiveвЂќ component, which would consider how close O is to a win.

If we have scenarios where we have a row, column, or diagonal with 2 XвЂ™s and 1 blank square, our utility should be 0.2 times the number of scenarios where this occurs.вЂќ вЂњFor each row, column, and diagonal, if we have 3 XвЂ™s in-a-row, we have won the game and should assign a utility of 1.0. For each possible move, utility is calculated using the below utility rule. We can see that itвЂ™s XвЂ™s turn, and there are only 3 possible moves, and hence, 3 child nodes. The figure below displays a tic-tac-toe board midway through the game with a very simple (probably not optimal) utility rule.

LetвЂ™s take tic-tac-toe, for example, which can have relatively simple utility measures. all might be factors to consider in calculating the utility of a particular move. For instance, number of blank spaces on the board, the location of the opponentвЂ™s current pieces, the location of our current pieces, how close we are to a winning formation, etc. It can incorporate a large variety of factors and weigh them as the programmer sees fit. How utility is calculated is entirely up to the programmer. Utility can be thought of as a way to вЂњscoreвЂќ each possible move based on its potential to result in a win. As a matter of fact, chess has an average branching factor of ~35. Often times, in chess for instance, the number of possible moves can be much, much greater, causing our game tree to become complicated in a hurry. The above schematic is oversimplified in the sense that an opponent only has 3 possible moves any given turn. If we continue this on long enough, we can quite literally map out the future of the game. Now we can say that, from the perspective of the Level 2 nodes, the nodes at Level 1 are the вЂњparentвЂќ nodes and the nodes at Level 2 are the вЂњchildrenвЂќ. Say these вЂњchildвЂќ nodes branch off into вЂњgrandchildвЂќ nodes (Level 2). In computer science, a tree refers to a nested data structure in which we start with one вЂњrootвЂќ node (Level 0), and branching from this root node, we can have any number of вЂњchildвЂќ nodes (Level 1). This article will take a brief look at how a computer decides its next move using the Minimax Algorithm, but first we need to define a few things: Game Tree This general strategy is encased in a widely-used algorithm in gaming theory, called the Minimax Algorithm. This article, however, will focus on one general decision-making strategy used by computers across a variety of strategic games, including chess, checkers, mancala, tic-tac-toe, you name it. Textbooks have been written on the computer chess problem alone, and many strategies varying in complexity have been put to the test.

#Best kasparov chess computer pro#

The expansive timeframe over which the chess computer pro blem has been pondered lends credence to the complexity of the solution. 1997 chess match between world champion Garry Kasparov and IBM computer вЂњDeep BlueвЂќ