Fourth International Symposium on Neural Networks (ISNN)
June 3-7, 2007, Nanjing, China
Online Dynamic Value System for Machine Learning
Haibo He, Stevens Institute of Technology
Janusz A. Starzyk, Ohio University

Outline

Introduction;

Online curve fitting principles ;

Network architecture and operation;

Simulation analysis;

Conclusion and future research;
2/22

From traditional AI to the embodied intelligence:
Rat Neurons can fly F- 22 jet
Intelligent machine
Picture source: www.space.com
State
S t
Reward r t r t

1
S t

1
Environment
Action a t
 Make value judgments according to received information;
 Develop sensory-motor coordination to actively interaction with environment;
 Develop internal value system and apply it to decision making;
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Different applications will have different definition of value signal, but we define the value signal as an expected reward or desired objective for machine’s action.
Motivation: Goal-driven learning
To provide a mechanism for the intelligent machines to be able to dynamically estimate the value function in reinforcement learning
(specify “good” from “bad”), therefore guiding the machines to adjust its actions to achieve the goal.
Source: Biologically inspired robot at CWRU http://biorobots.cwru.edu/
4/22

(SOLAR)
Characteristics:
* Self-organization
* Sparse and local interconnections
* Dynamically reconfigurable
* Online data-driven learning
System clock Remote neurons
Nearest neighbour neuron
Other Neurons
II: information index
ID: information deficiency
5/22

Supervisor is not always available in the learning environment
–
Uncertain (no prior knowledge) external environment
Supervisor is not always necessary in the learning environment
– How learning happens in a one-year old baby
Source:
Sociable humanoid robots: Kismet at MIT Artificial Intelligence Lab
6/22


Unstructured environment/uncertain information

Limited availability of information;

Information ambiguity and redundancy;

High dimensionality of the data set;

Time variability of the information;
7/22

Outline

Introduction;
Online Curve Fitting Principles;

Network architecture and operation;

Simulation analysis;

Conclusion and future research;
8/22

Consider dynamic adjustment of the fit function described by a linear combination of the selected base functions:
Y
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 a a

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...
a q
1
2






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
 T 


1  T
Y
Y
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
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 i n
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1 i n
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

1 i
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1 i
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...
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1 i
2 i qi
Storage requirements:

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
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2
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a q
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
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
 
* A i n

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
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 q ( q
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 q
2
9/22

Value
A
Value A
Upper Curve
Neutral Curve
Lower Curve
B
B
Data dimension
Data dimension
Three curve fitting:
 Neutral Curve: a least square fit (LSF) fits to all the data samples in the space
 Upper Curve: only fits to the data points which are above the neutral curve.
 Lower Curve: only fits to the data points which are below the neutral curve
10/22

Differential Based Voting: d
1 i
 v ni
 v ui d
2 i d i

 d v ni
1 i

 v li d
2 i
2
Value
Vui
Vni
Vli
Upper Curve
Neutral Curve d1i d2i
Input data w i

1 d i v vote
 i k 

1
 v ni w i i k 

1 w i

Lower Curve
Input
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Value
V_true
Vni
New received point
Upper Curve-after modification
Upper Curve-before the new point is received
Neutral Curve-after modification
Pseudo code:
Lower Curve-keep unchanged
Neutral Curve-before the new point is received
Data dimension
{New data sample comes;
Modify the neutral curve;
Difference = v ni
 v true
If (Difference >= 0)
{ Modify the lower curve;
Keep the upper curve unchanged;} else
{ Modify the upper curve;
Keep the lower curve unchanged;} end end}
12/22

Outline

Introduction;

Online Curve Fitting Principles;
Network architecture and operation;

Simulation analysis;

Conclusion and future research;
13/22

A pipelined dynamic architecture:
To all the processing elements in each layer
Value
C h a n n e l
Data samples
Bidirectional signal channel
Vn1
W1
Vn2
W2
C h a n n e l
Vni
Wn v
1 w
1


  w i v i

1
  w i

1
Data PE
Information PE
C h a n n e l v l w l

  w i v i l


  w i l

 v l

1
 w l

1
IPN a n n e l
C h
Communication Channel
Bidirectional signal channel
DPN
Final
Value
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Value
Input 1
Input 2
Processing
Element
Fitted value To Differential Voting
Transform function output
To another PE’s input
Input space transform function
Curve fitting
Fitted value
Transform function output
15/22

Outline

Introduction;

Online Curve Fitting Principles;

Network architecture and operation;
Simulation analysis;

Conclusion and future research;
16/22

Financial data analysis bank prime loan rate prediction
Data sets are available from: www.forecasts.org
Input:
 Monthly bank prime loan rate;
 Discount rate;
 Federal funds rate;
 Ten-year treasury constant maturity rate;
“market is unpredictable”
 Random Walk Hypothesis;
 Efficient Market Hypothesis;
Output:
 Next month’s bank prime loan rate
Training period:
 January 1995 to December 2000
Testing period:
 February 2001 to September 2002
17/22

Bank prime loan rate prediction by value system
(February 2001 to September 2002)
18/22

Performance comparision
0.6
0.5
0.4
MSE error 0.3
0.2
0.1
0
Learning accuracy Prediction accuracy
Hybrid iterative evolutionary fuzzy neural network in [8]
Genetic fuzzy neural learning algorithm in [9]
Proposed value system
19/22

Outline

Introduction;

Online Curve Fitting Principles;

Network architecture and operation;

Simulation analysis;
Conclusion and future research;
20/22

Conclusion and future research
 Provide a mechanism for the intelligent machines to be able to dynamically estimate the value function;
 Dynamic online data driven learning;
 No backpropagation required;
 Three curve fitting method;
 General framework for different implementations
21/22

Future research
 Dynamically self-reconfigurable;
 Investigate different input transformation and base functions;
 Hardware implementation;
 Facilitate goal-driven learning;
 Integration with reinforcement learning within a realistic environment;
A promising future?
Ray Kurzweil predicted:
We achieve one Human Brain capability for $1,000 around the year 2023, for one cent around the year 2037;
We achieve one Human Race capability for $1,000 around the year 2049, for one cent around the year 2059.
---from
“The Law of Accelerating Returns” by Ray Kurzweil
Source: www.kurzweilai.net
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