Connectionistic Problem Solving
Computational Aspects of Biological Learning
Published on 1. January 1990
Book
Paperback/Softback
X, 278 pages
978-0-8176-3450-6 (ISBN)
Description
1. 1 The problem and the approach The model developed here, which is actually more a collection of com ponents than a single monolithic structure, traces a path from relatively low-level neural/connectionistic structures and processes to relatively high-level animal/artificial intelligence behaviors. Incremental extension of this initial path permits increasingly sophisticated representation and processing strategies, and consequently increasingly sophisticated behavior. The initial chapters develop the basic components of the sys tem at the node and network level, with the general goal of efficient category learning and representation. The later chapters are more con cerned with the problems of assembling sequences of actions in order to achieve a given goal state. The model is referred to as connectionistic rather than neural, be cause, while the basic components are neuron-like, there is only limited commitment to physiological realism. Consequently the neuron-like ele ments are referred to as "nodes" rather than "neurons". The model is directed more at the behavioral level, and at that level, numerous con cepts from animal learning theory are directly applicable to connectionis tic modeling. An attempt to actually implement these behavioral theories in a computer simulation can be quite informative, as most are only partially specified, and the gaps may be apparent only when actual ly building a functioning system. In addition, a computer implementa tion provides an improved capability to explore the strengths and limita tions of the different approaches as well as their various interactions.
More details
Edition
Softcover reprint of the original 1st ed. 1990
Language
English
Place of publication
Boston
United States
Target group
College/higher education
Professional and scholarly
Research
Product notice
Paperback (trade)
Unsewn / adhesive bound
Illustrations
X, 278 p.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 16 mm
Weight
441 gr
ISBN-13
978-0-8176-3450-6 (9780817634506)
DOI
10.1007/978-1-4684-6770-3
Schweitzer Classification
Other editions
Additional editions
E-Book
12/2012
Birkhäuser
€53.49
Available for download
Previous edition
Steven E. Hampson | George Adelman
Connectionistic Problem Solving
Computational Aspects of Biological Learning
Book
12/1989
Birkhäuser Verlag GmbH
€55.71
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Content
1 Introduction.- 1.1 The problem and the approach.- 1.2 Adaptive problem solving.- 1.3 Starting with simple models.- 1.4 Sequential vs. parallel processing.- 1.5 Learning.- 1.6 Overview of the model.- 2 Node Structure and Training.- 2.1 Introduction.- 2.2 Node structure.- 2.3 Node training.- 2.4 Input order.- 2.5 Alternative LTU models.- 2.6 Continuous or multivalued valued features.- 2.7 Representing multivalued features.- 2.8 Excitation, inhibition and facilitation.- 3 Improving on Perceptron Training.- 3.1 Introduction.- 3.2 Perceptron training time complexity.- 3.3 Output-specific feature associability.- 3.4 Origin placement.- 3.5 Short-term weight modification.- 4 Learning and Using Specific Instances.- 4.1 Introduction.- 4.2 Focusing.- 4.3 Generalization vs. specific instance learning.- 4.4 Use of specific instances.- 5 Operator and Network Structure.- 5.1 Introduction.- 5.2 Network interconnections and association types.- 5.3 Multilayer networks.- 5.4 Recurrent connections.- 5.5 Forms of category representation.- 5.6 Space requirements.- 5.7 Goals as undistinguished features.- 6 Operator Training.- 6.1 Introduction.- 6.2 Operator training (disjunctive representation).- 6.3 OT extensions.- 6.4 OT results.- 6.5 Shared memory focusing.- 6.6 Behavioral results.- 6.7 Input-specific feature associability.- 7 Learned Evaluation and Sequential Credit Assignment.- 7.1 Introduction.- 7.2 Single action.- 7.3 Sequential action.- 7.4 Biological evaluation.- 7.5 The S-R model.- 7.6 Variations on the theme.- 7.7 Instrumental and classical conditioning.- 7.8 Results.- 8 Stimulus-Stimulus Associations and Parallel Search.- 8.1 Introduction.- 8.2 The S-S model.- 8.3 Backward search.- 8.4 Eco-world: an example domain.- 8.5 Forward search.- 9 Stimulus-Stimulus Discussion.- 9.1 Introduction.- 9.2 Biological relevance.- 9.3 A simple experiment.- 9.4 Drive and reward.- 9.5 Automatization of behavior.- 9.6 Parallel vs. sequential search.- 10 Stimulus-Goal Associations.- 10.1 Introduction.- 10.2 The S-G model.- 10.3 S-G discussion.- 10.4 General goal setting.- 11 Summary and Conclusions.- 12 Further Reading and Notes.- 13 Bibliography.- 14 Symbols and Abbreviations.- 15 Name Index.- 16 Subject Index.