Intelligence Semantics

Download Artificial Intelligence Techniques in Prolog by Yoav Shoham PDF

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By Yoav Shoham

Synthetic Intelligence thoughts in Prolog

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For example, in a route-finding application, the metric distance between the current location and the destination might serve as a reasonable estimation function (even though in fact we might have to make detours to avoid obstacles). 7. 9: Length of the p a t h is straightforward. The following program assumes an application-dependent predicate h(Node, Hval). % A* search astar( Start, GoalPred, Sol ) :- best_first_search( Start, astarJun, GoalPred, Sol ). astar_fun( [Node | Path], Fval ) :length( Path, Gval ), h( Node, Hval ), Fval is Gval+Hval.

And-or trees can also be viewed as ordered, and searched in various ways. ) Search of general graphs is slightly complicated by the need to avoid repeated exploration of the same node. 1. Again, the numbers by the nodes indicate the order of exploration. Depth-first search can be implemented very efficiently. The time complexity of the search can be kept linear in the total number of arcs in the graph, and the space complexity linear in the length of the longest p a t h in the graph. The efficient implementation uses a stack, or a last-in first-out d a t a structure: The program keeps a stack of the p a t h uncovered so far, and extends it by pushing new elements onto it.

This is an unrealistic assumption in all but the most trivial applications. Rewrite the programs so that they dynamically decide for each node whether to expand it, or instead to apply a given heuristic function to it and not expand the search further along that path. Examples of criteria for not expanding a node: (a) it is deeper than some given threshold, (b) its heuristic value is extremely high or low. 8 The implementation of α-β search given in the text scans the tree from left to right. Other search orders are possible, sometimes yielding greater pruning.

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