Download e-book for iPad: Encyclopedia of Artificial Intelligence: A-N by Stuart C. Shapiro

By Stuart C. Shapiro

ISBN-10: 047162974X

ISBN-13: 9780471629740

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L. l. + A l\ ffi H\ A ,+ 1 \ l( -J*. bc L<2 2<3 3<4 <:> 8 AA8 8 v I ma }T ,# I={ White Goalstate:{positionsin which opponent's king hasbeencaptured} Rules:{legalmovesof chess} Figure 2. A forward searchgraph. Heuristicknowledge: ' Functionsthat evaluateboardpositionsfor likelihoodthey will leadto a win. Standardopeningmoves. Classicendgames. Other tacticaland strategicknowledge. Figure l. A state-space problemdescription. Heuristic knowledge is not necessaryfor trivial problems (such as playing tic-tac-toe) because these problems can be solved by exhaustively enumerating all the legal paths from an initial state and checking for one such path that reachesa goal state.

Figure 5 shows examples of such production rules taken from two different expert systems. The CF (certainty factor) in the MYCIN rule is an estimate of how strongly the evidencein the if part of the rule supports the conclusion in the then part. Many expert systems use such estimates, although others, such as Rl (XCON), do not. Often additional knowledge bases, frequently of slot-and-filler structures, are also used in conjunction with the production rules, but the primary program architecture is the production system.

When the procedure finds that the (possibly backed- branching factor analysis, seeRef. 4. For an evaluation of the up) value of a max position (Max's turn to move) does not overall effectiveness of the procedure, see Ref. 1. function Alphabeta (p: position; a: integer; B: integer): integer; var ffi, i, t, d: integer; begin determine the successorpositions Pt,' ' ' , Pa of position p; if d - 0 then Alphabeta :: f(p) else begin Figure 2. Game tree with minimax backed-upvalues. BETA (b) Figure 3. Depth-first minimax procedure' Figure 4.

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Encyclopedia of Artificial Intelligence: A-N by Stuart C. Shapiro

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