ALERT Computer theory book ALERT - Title: CONSEQUENCE DRIVEN SYSTEMS

Ivan PopStefanija ivan at qei.quad-eng.com
Fri Jun 27 08:13:41 EST 1997


The following is a information on book which readers of this list
might find of interest.

                      CONSEQUENCE DRIVEN SYSTEMS                     
            - Teaching, Learning, and Self-Learning Agents -         

                          by Stevo Bozinovski                        

 *201 pages
 *79 figures
 *27 algorithm descriptions
 *8 tables
 *156 references

 Among its special features, the book
 -------------------------------------------
 **  provides  a  unified  theory  of  consequence  driven  systems
 including  closed-loop   teaching,   reinforcement   learning,   and 
 self-reinforcement learning
 ** describes a generic architecture of a neuro-genetic agent capable 
 of performing in all the mentioned paradigms, 1) consequence  driven 
 teaching  2)  external  reinforcement   based   learning,   and   3) 
 self-reinforcement based learning 
 ** describes the Crossbar  Adaptive  Array  (CAA)  architecture,  an 
 early  (1981)  connectionist  network  and  explains  how  the   CAA 
 architecture was the first neural  network  that  solved  a  delayed 
 reinforcement learning task
 ** explains how the 1981 CAA learning method (shown on the cover  of 
 the book) is actually the well known, 1989 rediscovered,  Q-learning 
 method
 ** explains how CAA uses its genotype and phenotype (behavioral) 
 environment during as optimization environments in Lamarckian sense
 ** introduces new types of neurons, denoted  as  Provoking  Adaptive 
 Units,  axon provoked neurons for distributed DP tasks 
 **  illustrates  the  usage  of  those  neurons  as  routers  in   a 
 routing-in-networks-with-faults task. 
 **  uses  the  parallel  programming  technique  in  describing  the 
 algorithms throughout the book 

 
 *******************************************************************
 Ordering information 
 ISBN 9989-684-06-5,  Gocmar Press                 
 price: USD $15, paperback
 send email to the publisher representative: 
 Ivan PopStefanija, Gocmar Press, USA 
 ivan at alex.ecs.umass.edu
 ********************************************************************

 CONTENTS:

 1. INTRODUCTION

 1.1. The framework
 1.2. Agents and architectures
 1.3. Neural architectures
 1.3.1 Greedy policy neural architectures
 1.3.2. Recurrent architectures
 1.3.3. Crossbar architectures
 1.3.4. Subsumption architecture adaptive arrays
 1.4. Problems. Emotional Graphs
 1.5. games. Emotional petri nets
 1.6. Parallel programming
 1.7. Bibliographical and other notes

 2. CONSEQUENCE LEARNING AGENTS

 2.1. Thhe agent-environment interface
 2.2. A taxonomy of learning paradigms
 2.3. Classes of consequence learning agents
 2.4. A generic consequence learning architecture
 2.5. Learning rules and routines 
 2.6. Bibliographical and other notes

 3. CONSEQUENCE DRIVEN TEACHING

 3.1. Class T agents
 3.2. Learners
 3.2.1. Multi layer perceptrons
 3.2.2. Greedy policy neural arrays
 3.3. Teachers
 3.3.1. Toward a theory of teaching systems
 3.3.2. Teaching strategies
 3.4. Curriculums
 3.4.1. Curriculum grammars amd languages
 3.4.2. Curriculum space approach
 3.5. Pattern classification teaching as integer programming
 3.6. Pattern classification teaching as Dynamic Programming
 3.7. Bibliographical and other notes

 4. EXTERNAL REINFORCEMENT LEARNING

 4.1. Reinforcement learning NG agents
 4.2. Associative Serach Network (ASN)
 4.2.1. Basic ASN
 4.2.2. Reinforcement predictive ASN
 4.3. Actor-Critic architecture
 4.4. Bibliographical and other notes

 5. SELF-REINFORCEMENT LEARNING

 5.1. Conceptual framework
 5.2. Self-reinforcement learning and the NG agents
 5.3. The Crossbar Adaptive Array architecture
 5.4. How it works
 5.4.1. Defining primary goals from thew genetic environment
 5.4.2. Secondary reinforcement mechanism
 5.4.3. The CAA learning method
 5.5. Example of a CAA architecture
 5.6. Solving problems with a CAA architecture
 5.6.1. Learning in emotional graphs: maze running
 5.6.2. Learning in loosely defined emotional graphs: Pole balancing
 5.7. Another example of a CAA architecture
 5.8. Using entropy in Markov decision Processes
 5.9. Issues on the genetic environment
 5.9.1. CAA as an optimization architecture
 5.9.2. Complelemtarity with the Genetic Algorithms
 5.9.3. Self-reinforcement: genetic environment approach
 5.10. Bibliographical and other notes

 6. CONSEQUENCE PROGRAMMING

 6.1. Dynamic Programming and markov decision Problems
 6.2. Inroducing cost in the CAA architecture
 6.3. Q-learning
 6.4. A taxonomy of the CAA-method based learning algorithms
 6.5. Producing optimal solution in a stochastic environment
 6.6. Distributed Consequence Programming: A neural theory
 6.6.1. Provoking units: axon provoked neurons
 6.6.2. An illustration: Routing in client-server networks with faults
 6.7. Bibliographical nad other notes

 7. SUMMARY

 8. REFERENCES

 9. INDEX
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        ********************************************
                                                    
                    Ordering Information            
                                                    
               Book: CONSEQUENCE DRIVEN SYSTEMS     
             Author:     Stevo Bozinovski           
               ISBN:       9989-684-06-5            
          Publisher:  GOCMAR Press, 1995             
                                                    
          Price:                    $15             
                                                    
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            GOCMAR Press USA                        
            c/o Ivan PopStefanija                   
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        ********************************************


Thank you

        Ivan PopStefanija
        e-mail: <ivan at alex.ecs.umass.edu>
        GOCMAR Press USA



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