By Ian Pratt (auth.)

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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"'). Means-ends analysis was employed in an early and influential robot planning program called STRIPS. STRIPS uses PAD-rules to represent knowledge about actions , and lists of formulae to represent situations and goals, much as described here. ) Notice that the first action to be thought of, a, occurs, in general, neither at the beginning of the final plan (as with forward chaining), nor at the end (as with backward chaining), but somewhere in the middle.

However, it is easy to increase the size of the problem to the point where this is no longer the case. Note in addition that the badness-measure just defined is specific to the present problem. It is common for heuristics to rely on features specific to the problem at hand in this way. 5 Finding optimal plans We have introduced best-first search as a procedure for efficiently finding some plan to achieve Bob's goals. Note that this is not the same as a search-procedure for somehow finding an efficient plan to achieve Bob's goals .

The links in the next tier correspond to possible moves for black, those in the tier after that, possible moves for white, and so on. For interesting games, the search-trees consisting of all possible moves from the start to the finish (containing all possible ways in which the game may develop) are huge; and it is quite impractical to try to generate the whole tree until a goal state (win for the computer) is encountered . So, rather than marking some situations as goal situations, as in the planning problems considered above, we will imagine that each situation has a numerical goodness value or utility from the white player's point of view.

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