New Book

[bozinovs@delusion.cs.umass.edu]

Dear Connectionists,

Happy New Year to everybody!

At the end of the year I have a pleasure to announce a new book in
the field.

Advertisment:
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New Book!   New Book!   New Book!   New Book!   New Book!   New Book!
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                        CONSEQUENCE DRIVEN SYSTEMS
                        CONSEQUENCE DRIVEN SYSTEMS 
                        CONSEQUENCE DRIVEN SYSTEMS

                          by  Stevo Bozinovski

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

Among its special features, the book:
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** provides a unified theory of response-sensitive teaching and learning 
 
** as a result of that theory describes a generic architecture of a 
neuro-genetic agent capable of performing in 1) consequence sensitive 
teaching, 2) reinforcement learning, and 3) self-reinforcement learning 
paradigms 

** describes the Crossbar Adaptive Array (CAA) architecture, an 1981
neural network developed within the Adaptive Networks Group, as an 
example of a neuro-genetic agent

** explains how the CAA architecture was the first neural network that 
solved a delayed reinforcement learning task, the Dungeons-and-Dragons
task, in 1981

** explains how the 1981 learning method (shown on the cover of the 
book) is actually the well known, 1989 rediscovered,  Q-learning method  

** introduces the Benefit-Cost CAA (B-C CAA), as extension of the 1981 
Benefit-only CAA architecture 

** introduces at-subgoal-go-back algorithm as modification of the 
1981 at-goal-go-back CAA algorithm

** introduces a new type of neuron, denoted as Provoking Adaptive Unit,
for dealing with tasks of Distributed Consequence Programming

** illustrates the usage of those neurons as routers in a 
routing-in-networks-with-faults task

** uses parallel programming technique in describing the algorithms
throughout the book
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Ordering information
ISBN 9989-684-06-5, Gocmar Press, 1995 
price: $15, paperback

For further information contact the author: bozinovs at cs.umass.edu

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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: A STRUCTURAL THEORY 

2.1. The 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
3.3. Teachers
3.3.1. Toward a theory of teaching systems
3.3.2. Teaching strategies
3.4. Curriculums
3.4.1. Curriculum grammars and 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 learningh NG agents
4.2. Associative Search Network (ASN)
4.2.1. Basic ASN
4.2.2. Reionforcement 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 the 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 architecture as an optimization architecture
5.9.2. Complemetarity 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. Introducing 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 and other notes

7. SUMMARY

8. REFERENCES

9. INDEX
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