Artificial IntelligenceMcGraw-Hill, 1991 - 621 páginas |
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Página xii
... Learning 17.1 What Is Learning ? 17.2 Rote Learning 17.3 Learning by Taking Advice 17.4 Learning in Problem Solving 17.5 Learning from Examples : Induction 445 447 447 448 450 452 457 17.6 Explanation - Based Learning 471 17.7 Discovery ...
... Learning 17.1 What Is Learning ? 17.2 Rote Learning 17.3 Learning by Taking Advice 17.4 Learning in Problem Solving 17.5 Learning from Examples : Induction 445 447 447 448 450 452 457 17.6 Explanation - Based Learning 471 17.7 Discovery ...
Página 448
... learning . However , many AI programs are able to improve their performance substantially through rote - learning techniques , and we will look at one example in depth , the checker - playing program of Samuel ... LEARNING Rote Learning.
... learning . However , many AI programs are able to improve their performance substantially through rote - learning techniques , and we will look at one example in depth , the checker - playing program of Samuel ... LEARNING Rote Learning.
Página 484
... Learning from examples - Initial state : collection of positive and negative examples - Final state : concept description - Search algorithms : candidate elimination , induction of decision trees • Learning in problem solving - Initial ...
... Learning from examples - Initial state : collection of positive and negative examples - Final state : concept description - Search algorithms : candidate elimination , induction of decision trees • Learning in problem solving - Initial ...
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