Artificial IntelligenceMcGraw-Hill, 1991 - 621 páginas A revision of an established text for undergraduate and postgraduate Artificial Intelligence courses, this text incorporates the latest research and methods. |
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Página 11
... Go ( Posswin ( X ) ) [ i.e. , win ] else if Pos- swin ( O ) is not 0 , then Go ( Posswin ( O ) ) [ i.e. , block win ] , else if Board [ 7 ] is blank , then Go ( 7 ) , else Go ( 3 ) . [ Here the program is trying to make a fork . ] If ...
... Go ( Posswin ( X ) ) [ i.e. , win ] else if Pos- swin ( O ) is not 0 , then Go ( Posswin ( O ) ) [ i.e. , block win ] , else if Board [ 7 ] is blank , then Go ( 7 ) , else Go ( 3 ) . [ Here the program is trying to make a fork . ] If ...
Página 325
... program had an inter- esting learning component which allowed its performance to improve with experience ... Go Go is a very difficult game to play by machine since the average branching factor of the game tree is very high . Brute force ...
... program had an inter- esting learning component which allowed its performance to improve with experience ... Go Go is a very difficult game to play by machine since the average branching factor of the game tree is very high . Brute force ...
Página 579
... book has been to say enough about the use of knowledge in problem- solving programs to enable you to build one . Go do it . And have fun . 22.2 Exercises 1. What do you think is the main 579 Conclusion Components of an AI Program.
... book has been to say enough about the use of knowledge in problem- solving programs to enable you to build one . Go do it . And have fun . 22.2 Exercises 1. What do you think is the main 579 Conclusion Components of an AI Program.
Contenido
What Is Artificial Intelligence? | 3 |
5 | 24 |
Heuristic Search Techniques | 63 |
Derechos de autor | |
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Términos y frases comunes
Abbott agents algorithm answer apply approach ARMEMPTY assertions attributes axioms backpropagation backtracking backward belief best-first search breadth-first search Caesar called Chapter chess clauses complete concept conceptual dependency consider constraints contains contradiction corresponding define depth-first depth-first search described discussed domain example fact function game tree goal grammar graph heuristic Horn clauses important inference inheritance input instance interpretation isa links John justification knowledge base knowledge representation labeled learning Marcus match minimax move MYCIN natural language node object ON(B ON(C operators output parsing particular path perceptron perform players possible preconditions predicate logic problem problem-solving procedure produce PROLOG represent result robot rules script Section semantic semantic net sentence shown in Figure simple slot solution solve specific step structure Suppose syntactic task techniques theorem things tree truth maintenance system understanding variables version space