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

2/22

Outline

Introduction;

Online curve fitting principles;

Network architecture and operation;

Simulation analysis;

Conclusion and future research;

3/22

Introduction: Why value system is important?

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;

Environment

State Reward Action

Intelligent machine

tS tr

1tS

1tr

ta

From traditional AI to the embodied intelligence:

Rat Neurons can fly F- 22 jet

Picture source: www.space.com

4/22

Introduction: What is the value signal?

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/

5/22

Introduction: self-organizing learning array(SOLAR)

Characteristics:

* Self-organization

* Sparse and local interconnections

* Dynamically reconfigurable

* Online data-driven learning

Other Neurons

Nearest neighbour neuron

Remote neurons System clock

ID: information deficiency

II: information index

6/22

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

How can value system help here?

Source: Sociable humanoid robots: Kismet at MIT Artificial Intelligence Lab

7/22

The challenges

Unstructured environment/uncertain information

Limited availability of information;

Information ambiguity and redundancy;

High dimensionality of the data set;

Time variability of the information;

8/22

Introduction;

Online Curve Fitting Principles;

Network architecture and operation;

Simulation analysis;

Conclusion and future research;

Outline

9/22

n

iiqi

n

iii

n

iii

n

iqiqi

n

iqii

n

iqii

n

iqii

n

iii

n

iii

n

iqii

n

iii

n

iii

TT

qY

Y

Y

Y

a

a

a

1

12

11

1

112

11

12

122

121

11

121

111

12

1

......

............

...

...

...

Online dynamic curve fitting

Consider dynamic adjustment of the fit function described by a linear combination of the selected base functions:

qqaaaY ......2211

A

a

a

a

Y

q

q *...

... 2

1

21

Storage requirements: qqq

s 2

)1(

10/22

Three curve fitting versus single curve fitting

Value

Data dimension

A

B

Upper Curve

Neutral Curve

Lower Curve

Value

Data dimension

A

B

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

11/22

Decision integration

ii d

w1

k

ii

k

iini

vote

w

wvv

1

1

Differential Based Voting:

Value

Input

Vui

Vni

Vli

d1i

d2i

Upper Curve

Neutral Curve

Lower Curve

Input data

221

2

1

iii

linii

uinii

ddd

vvd

vvd

12/22

Upper Curve-before the new point is received

Lower Curve-keep unchanged

New received point Upper Curve-after modification

Neutral Curve-before the new point is received

Neutral Curve-after modificationVni

V_true

Data dimension

Value

Implementation of TCF

{New data sample comes;Modify the neutral curve;Difference = If (Difference >= 0)

{ Modify the lower curve; Keep the upper curve unchanged;}

else { Modify the upper curve;Keep the lower curve unchanged;}

endend}

Pseudo code:

trueni vv

13/22

Introduction;

Online Curve Fitting Principles;

Network architecture and operation;

Simulation analysis;

Conclusion and future research;

Outline

14/22

Value system architecture

Channel

Channel

Channel

Channel

Vn1

Vn2

Vni

Dat

adi

men

sion

s

Value

Data

samples

FinalValue

To all the processingelements in each layer

Bidirectionalsignal channel

DPN

Data PE

Information PE

Communication Channel

Bidirectional signal channel

IPN

11 iivwv

11 iww

1 lliil vvwv

1 llil www

W1

W2

Wn

A pipelined dynamic architecture:

15/22

Inside a value system

Input spacetransformfunction

Curvefitting

Transform function output

Value

Fitted value

Input 1

Input 2

ProcessingElement

To Differential Voting

To another PE’s input

Fitted value

Transformfunction output

16/22

Introduction;

Online Curve Fitting Principles;

Network architecture and operation;

Simulation analysis;

Conclusion and future research;

Outline

17/22

Simulation analysis

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;

Output: Next month’s bank prime loan rate

Training period: January 1995 to December 2000

Testing period: February 2001 to September 2002

“market is unpredictable” Random Walk Hypothesis;

Efficient Market Hypothesis;

18/22

Prediction results

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

19/22

Result comparison: MSE error

0

0.1

0.2

0.3

0.4

0.5

0.6

MSE error

Learning accuracy Prediction accuracy

Performance comparision

Hybrid iterative evolutionary fuzzyneural network in [8]

Genetic fuzzy neural learning algorithmin [9]

Proposed value system

20/22

Introduction;

Online Curve Fitting Principles;

Network architecture and operation;

Simulation analysis;

Conclusion and future research;

Outline

21/22

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

22/22

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 KurzweilSource: www.kurzweilai.net