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 155
... show how resolution can be used to prove new things about him . Let's first consider the set of statements introduced ... shows a resolution proof of the statement hate ( Marcus , Caesar ) Of course , many more resolvents could have been ...
... show how resolution can be used to prove new things about him . Let's first consider the set of statements introduced ... shows a resolution proof of the statement hate ( Marcus , Caesar ) Of course , many more resolvents could have been ...
Página 162
... shows how the resolution process finds the statement for which we are looking . The answer to the question can then be derived from the chain of unifications that lead back to the starting clause . We can eliminate the necessity for ...
... shows how the resolution process finds the statement for which we are looking . The answer to the question can then be derived from the chain of unifications that lead back to the starting clause . We can eliminate the necessity for ...
Página 216
... shows such a justification added to the network and a consistent labeling for that network , which shows Suspect Abbott labeled IN . That Abbot is the primary suspect represents an initial state of the murder investi- gation ...
... shows such a justification added to the network and a consistent labeling for that network , which shows Suspect Abbott labeled IN . That Abbot is the primary suspect represents an initial state of the murder investi- gation ...
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