Fourth International Symposium on Neural Networks (ISNN) June 3-7, 2007, Nanjing, China Online...
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Transcript of Fourth International Symposium on Neural Networks (ISNN) June 3-7, 2007, Nanjing, China Online...
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
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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
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Online dynamic curve fitting
Consider dynamic adjustment of the fit function described by a linear combination of the selected base functions:
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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
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Differential Based Voting:
Value
Input
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Vni
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Upper Curve
Neutral Curve
Lower Curve
Input data
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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:
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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
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sion
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Value
Data
samples
FinalValue
To all the processingelements in each layer
Bidirectionalsignal channel
DPN
Data PE
Information PE
Communication Channel
Bidirectional signal channel
IPN
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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