Artificial IntelligenceMcGraw-Hill, 1991 - 621 páginas |
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Página 12
... constraints, called the atomic constraints, over any of the domains N,Z,Q,R as follows: Equality: u = v Inequality: u = v Lower Bound: u θ b Upper Bound: −u θ b ... Constraints In this section we first review 12 2. Constraint Databases.
... constraints, called the atomic constraints, over any of the domains N,Z,Q,R as follows: Equality: u = v Inequality: u = v Lower Bound: u θ b Upper Bound: −u θ b ... Constraints In this section we first review 12 2. Constraint Databases.
Página 22
... constraints work their way into the value of a solution — the solutions that satisfy more constraints are better. Thus, in the first class below, the objective is to maximize the number of satisfied constraints while in the second class ...
... constraints work their way into the value of a solution — the solutions that satisfy more constraints are better. Thus, in the first class below, the objective is to maximize the number of satisfied constraints while in the second class ...
Página 62
... Constraints. Considering an application point of view, we have regrouped [3] the different global constraints under the following 11 categories: − order constraints: minimum or maximum value according to some defined ordering relation ...
... Constraints. Considering an application point of view, we have regrouped [3] the different global constraints under the following 11 categories: − order constraints: minimum or maximum value according to some defined ordering relation ...
Contenido
5 | 24 |
Heuristic Search Techniques | 63 |
Knowledge Representation Issues | 105 |
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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 fact frame 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 operators output parsing particular path perceptron perform players possible preconditions predicate logic problem problem-solving procedure produce PROLOG properties 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