By Yoav Shoham
Man made Intelligence options in Prolog
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Additional info for Artificial Intelligence Techniques in Prolog
For clarity, the procedures we discuss do not return the actual pruned tree, but only the value guaranteed to the first player. 5. 1 Minimax search The minimax search procedure is derived directly from the definition of the problem. 7). The search starts with the leaf nodes and propagates values back to the internal nodes. Each mm-node is assigned the minimum among the values of its successors (reflecting the assumption t h a t the second player will choose the move t h a t is the worst for the first player), and each rara-node is assigned the maximum among the values of its successors (hence the names for the two node types).
8. Game-tree search 45 A second modification does affect the input form. The above program accepts the entire game tree as input. However, in all interesting applications the game tree is very large and its exhaustive creation prior to the search is not feasible. An alternative is for the program itself to create as much of the game tree as computational resources allow, and to search that portion of the tree. 7. A third modification, which is aimed at speeding up the execution of the minimax search, is discussed in the next section.
6 shows an example of breadth-first search (the numbers by the nodes denote the order of discovery) and the induced layers. 1; don't be misled by the different layout. 6: Example of breadth-first search Breadth-first search can also be implemented to run in time t h a t is linear in the total number of arcs; however, unlike t h a t of depth-first search, the space complexity of its optimum implementation is also linear in the total number of arcs in the graph, rather t h a n in the length of the longest p a t h .
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