Cognitive Science: The Science of Intelligent Systems
Autor George F. Luger, Peder Johnson, Carl Stern, Jean E. Newman, Ronald Yeoen Limba Engleză Hardback – 5 iul 1994
Preț: 444.20 lei
Preț vechi: 576.89 lei
-23% Nou
Puncte Express: 666
Preț estimativ în valută:
85.02€ • 89.15$ • 70.25£
85.02€ • 89.15$ • 70.25£
Carte tipărită la comandă
Livrare economică 29 ianuarie-12 februarie 25
Preluare comenzi: 021 569.72.76
Specificații
ISBN-13: 9780124595705
ISBN-10: 0124595707
Pagini: 666
Dimensiuni: 152 x 229 x 35 mm
Greutate: 1.07 kg
Editura: ELSEVIER SCIENCE
ISBN-10: 0124595707
Pagini: 666
Dimensiuni: 152 x 229 x 35 mm
Greutate: 1.07 kg
Editura: ELSEVIER SCIENCE
Public țintă
AUDIENCE: Upper-division students in cognitive science.Cuprins
Introduction to Cognitive Science:
Intelligence and the Roots of Cognitive Science.
Vocabularies for Describing Intelligence.
Representation Schemes.
Constraining the Architecture of Minds.
Natural Intelligence: Brain Function.
Symbol Based Representation and Search:
Network and Structured Representation Schemes.
Logic Based Representation and Reasoning.
Search Strategies for Weak Method Problem Solving.
Using Knowledge and Strong Method Problem Solving.
Machine Learning:
Explicit Symbol Based Learning Models.
Connectionist Networks: History, The Perception, and Backpropagation.
Competitive, Reinforcement, and Attractor Learning Models.
Language:
Language Representation and Processing.
Pragmatics and Discourse.
Building Cognitive Representations in PROLOG:
PROLOG as Representation and Language.
Creating Meta-Interpreters in PROLOG.
Epilogue:
Cognitive Science: Problems and Promise.
References.
Index.
Intelligence and the Roots of Cognitive Science.
Vocabularies for Describing Intelligence.
Representation Schemes.
Constraining the Architecture of Minds.
Natural Intelligence: Brain Function.
Symbol Based Representation and Search:
Network and Structured Representation Schemes.
Logic Based Representation and Reasoning.
Search Strategies for Weak Method Problem Solving.
Using Knowledge and Strong Method Problem Solving.
Machine Learning:
Explicit Symbol Based Learning Models.
Connectionist Networks: History, The Perception, and Backpropagation.
Competitive, Reinforcement, and Attractor Learning Models.
Language:
Language Representation and Processing.
Pragmatics and Discourse.
Building Cognitive Representations in PROLOG:
PROLOG as Representation and Language.
Creating Meta-Interpreters in PROLOG.
Epilogue:
Cognitive Science: Problems and Promise.
References.
Index.