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eBook Connectionistic Problem Solving: Computational Aspects of Biological Learning ePub

eBook Connectionistic Problem Solving: Computational Aspects of Biological Learning ePub

by Steven E. Hampson

  • ISBN: 0817634509
  • Category: Computer Science
  • Subcategory: Computers
  • Author: Steven E. Hampson
  • Language: English
  • Publisher: Birkhäuser; Softcover reprint of the original 1st ed. 1990 edition (January 1, 1990)
  • Pages: 278
  • ePub book: 1180 kb
  • Fb2 book: 1831 kb
  • Other: lrf mbr docx txt
  • Rating: 4.5
  • Votes: 383

Description

Learning and Using Specific Instances.

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 ic structures and processes to relatively high-level animal/artificial intelligence. Learning and Using Specific Instances.

Computational Aspects of Biological Learning. Extension artificial intelligence behavior intelligence learning memory modeling problem solving proving simulation. Authors and affiliations.

oceedings{isticPS, title {Connectionistic Problem Solving: Computational Aspects of. .Explore Further: Topics Discussed in This Paper. International Standard Book Number. Publications citing this paper

oceedings{isticPS, title {Connectionistic Problem Solving: Computational Aspects of Biological Learning, . 00, 276 pp, ISBN: 0-8176-345}, author {Douglas A. Baxter}, year {1992} }. Douglas A. Baxter. Publications citing this paper. Showing 1-4 of 4 citations.

Atsisiųskite, kad galėtumėte skaityti neprisijungę, paryškinti, pažymėti elementus ar užsirašyti pastabas skaitydami knygą „Connectionistic Problem Solving: Computational Aspects of Biological Learning. 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 ic structures and processes to relatively high-level animal/artificial intelligence behaviors.

Connectionistic Problem Solving: Computational Aspects of Biological Learning: Steven E. Hampson . The problem and the approach. Adaptive problem solving. Starting with simple models. Sequential vs. parallel processing. Hampson Birkhäuser, 1990. Overview of th. More).

The first computational paradigm was concerned with practically obtained psycho-learning . Predictors of Hands-On Learning: Students’ Problem Approach Attitude, Problem Solving Confidence, and Problem Solving Style Relevant to Parental Monitoring.

The first computational paradigm was concerned with practically obtained psycho-learning behavioral results after three animals’ neural modeling. These are namely: Pavlov’s, and Thorndike’s experimental work. In addition, the third model is concerned with optimal solution of reconstruction problem reached by a mouse’s movement inside Figure 8 maze. 25006 4 352 Downloads 5 257 Views Citations.

This article addresses an interesting comparative analytical study. The presented study considers two concepts of diverse algorithmic biological behavioral learning approach. Those concepts for computational intelligence are tightly related to neural and non-neural Systems. Respectively, the first algorithmic intelligent approach concerned with observed obtained practical results after three neural animal systems’ activities. Namely, they are Pavlov’s, and Thorndike’s experimental work

Connectionistic Problem-Solving : Computational Aspects of Biological Learning.

Connectionistic Problem-Solving : Computational Aspects of Biological Learning. by Steven E. 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 ic structures and processes to relatively high-level animal/artificial intelligence behaviors.

Additionally, considering effect of noisy environment on learning convergence, an interesting analogy between both proposed biological systems is introduced. artificial neural network modeling, animal learning, ant colony system, traveling salesman problem and computational biology.

Connectionistic problem solving: computational aspects of biological learning. recognition problems if the PN model could be trained by the BP method

Connectionistic problem solving: computational aspects of biological learning. recognition problems if the PN model could be trained by the BP method. In this paper, we propose a BP method for multilayer pulsed neural networks. The proposed method uses the duality of PN model, in which the desired output of hidden layer neuron is calculated from output layer neurons’ weights and output.

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.