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. |
Dentro del libro
Resultados 1-3 de 24
Página 143
... substitution ) dead ( Marcus , now ) ↑ ( 10 , substitution ) died ( Marcus , t1 ) A gt ( now , t1 ) ↑ ( 5 , substitution ) Pompeian ( Marcus ) ^ gt ( now , 79 ) ↑ ( 2 ) gt ( now , 79 ) ↑ ( 8 , substitute equals ) gt ( 1991,79 ) ...
... substitution ) dead ( Marcus , now ) ↑ ( 10 , substitution ) died ( Marcus , t1 ) A gt ( now , t1 ) ↑ ( 5 , substitution ) Pompeian ( Marcus ) ^ gt ( now , 79 ) ↑ ( 2 ) gt ( now , 79 ) ↑ ( 8 , substitute equals ) gt ( 1991,79 ) ...
Página 151
... substitution for the entire literal , not separate ones for each piece of it . To do this , we must take each substitution that we find and apply it to the remainder of the literals before we continue trying to unify them . For example ...
... substitution for the entire literal , not separate ones for each piece of it . To do this , we must take each substitution that we find and apply it to the remainder of the literals before we continue trying to unify them . For example ...
Página 152
... substitution that causes two literals to match . Usually , if there is one such substitution there are many . For example , the literals hate ( x , y ) hate ( Marcus , z ) could be unified with any of the following substitutions ...
... substitution that causes two literals to match . Usually , if there is one such substitution there are many . For example , the literals hate ( x , y ) hate ( Marcus , z ) could be unified with any of the following substitutions ...
Contenido
What Is Artificial Intelligence? | 3 |
5 | 24 |
Heuristic Search Techniques | 63 |
Derechos de autor | |
Otras 25 secciones no mostradas
Otras ediciones - Ver todas
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