By Ian Pratt (auth.)
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Additional info for Artificial Intelligence
More generally, meansends analysis is the following procedure : o Take the initial situation 5, and the set of goals G . o Find an action a which, if it could be performed, would achieve some goal GinG. o Find (using means-ends analysis) a plan 11", which will achieve the preconditions of a. o Compute the situation I produced by executing 11" and then a and find (using means-ends analysis) a plan 11"' which will achieve the remaining goals in G (minus G) in situation 1. e, 11" followed by a followed by 11"').
In this section, we consider an alternative approach. We settled on the following knowledge representation scheme. 9) 30 ArtificiallnteJligence action-type ( ancestor-rule-2(X,Y,Z), (210) pre-conds([parent(X,Y),ancestor(Y,Z)]), . add-list([ancestor(X,Z)])) . g. ancestor (sue, dave) . 11) Instead of adopting the Bob-like approach of starting from the initial knowledgesituation and generating a search-tree of all possible inferences until a node is found containing the sentence ances tor (sue, dave), we shall adopt the reverse strategy of working back from the goal, by seeing what could have led up to it.
E. a sequence of moves) to a board position which is the best that can be hoped for (assuming optimal play by the opponent) . The first move in this sequence is then the chosen move to be executed by the game-playing program. Once this move is made, the program awaits its opponent's response, at which point the process begins over again from the new board position. More formally, the procedure can be described recursively as follows: Search and Planning 41 begin minimax (node) if node is at limit of search-tree then let utili ty be the utility of node return [node, util ity] else if node is a white node, then apply minimax to children of node return result which has maximum utility else if node is a black node, then apply minimax to children of node return result which has minimum utility end if end minimax One final technicality.
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