Accession Number:

ADA183782

Title:

Multilayer Networks of Self-Interested Adaptive Units.

Descriptive Note:

Final rept. Sep 83-Sep 86,

Corporate Author:

MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER AND INFORMATION SCIENCE

Personal Author(s):

• Barto,Andrew G.

Report Date:

1987-07-01

Pagination or Media Count:

149.0

Abstract:

This report describes research directed toward refining and evaluating learning methods for multilayer networks of neuron-like adaptive units. We define a learning rule called the Associative Reward-Penalty, or A sub R-P, rule that has strong ties to both the theory of adaptive pattern classification and stochastic learning automata. We state a convergence result that has been proven for a single A sub R-P units can reliably learn nonlinear associative mappings. The behavior of these networks is discussed in terms of the collective behavior of stochastic learning automata in team decision problems. A number of methods for learning in multilayer networks are compared, including the A sub R-P method and the error back-propagation method. These methods, or variants of them, outperform the other methods applied to the test problem, with error back-propagation showing a significant speed advantage over the other methods. The A sub R-P and error back-propagation are compared and contrasted in terms of their respective approaches to gradient following.

Descriptors:

• *ADAPTIVE SYSTEMS
• *LEARNING
• *COMPUTERIZED SIMULATION
• *MATHEMATICAL ANALYSIS
• ADAPTIVE SYSTEMS
• AUTOMATA
• BEHAVIOR
• CLASSIFICATION
• CONVERGENCE
• DECISION MAKING
• LAYERS
• LEARNING
• NETWORKS
• PATTERNS
• STOCHASTIC PROCESSES
• TEAMS(PERSONNEL)
• VARIATIONS
• ARTIFICIAL INTELLIGENCE
• LEARNING CURVES
• ALGORITHMS
• ERRORS
• NETWORK ANALYSIS(MANAGEMENT)

Subject Categories:

• Computer Systems
• Bionics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE