hierarchical learning in ai - general problem

Mar 6, 2002 Jie Bao 1

Hierarchical Learning in AI

Notes 1

General Problem

Jie Bao

AI Lab, Iowa State University

[email protected]


Content of whole series Hierarchy and any problem in general information system HL in GA

Delicate structure in coding string Behavior Learning Sociobiology and ecology competition

HL in NN Modular learning Ensemble learning Hybrid learning

HL in MAS(MultiAgent System) Society entity: an agent in the hierarchy Society relationship: their languages


Why hierarchy? Nature system are mostly hierarchical

system “Divide and conquer” in Engineering The power and stableness from

cooperation of subsystems Easier to design and implement


How to hierarchy? System theory: summation( all parts) < whole system The biologically hierarchical system are formed by

self-organization Start from simple system Evolve from combine simple system to complex

system The structural hierarchy is usually the result of

evolutionary history


General Hierarchy Formation Direction: Order increasing Grow Multiplication Accelerate Interaction Higher level hierarchy appears! Differentiation Centralization


Three kinds of order-increasing system with Hierarchy

They are all order-increasing They are derived from the former one They all have hierarchical structure

Many theory and algorithm can be borrowed between the science of them !

General Hierarchy Formation

Organism Society Automata


General Hierarchy Formation

System is in the inner development of one level, for example, form ape to human. System search for favorable position on “niche” space, and the moving progress is called “Evolution”. In this progress, system tends to be more ordered because only more ordered system can have higher energy-consuming efficiency and win in the competition.

When the structure of system block in the way to higher order, hierarchy will be appeared. In this stage, a new high-level system “ bursts out”, such as society organized by human. Under the new organization, the system can have higher energy-consuming efficiency or energy-occupation ability, to further increase its order.


Hierarchical Structure(1)--Organism

Body System Organ Tissue Cell Cell organelle Protein , nucleic acid ……


Hierarchical Structure(2)--Society

Whole human world Nation State, Province City, County, Shire Community, Village Kin, Family Person


Hierarchical Structure(3)--Automata

Cyber space: internet Local area network Terminal (both software and hardware) Module ( eg. CPU, operating system) Sub-module (eg. ALU, disk system) Smaller module, (eg. Adder, a interrupt

service routine) Bit operation(eg. Gate )


Hierarchical Evolution(1)--Organism

Little molecule-> big molecule: 3.5 billion y Molecule -> cell (2 billion y?) Prokaryote -> Eukaryotic (? billion y ) Cell -> Multi-cellular (0.6 billion y) Multi-cellular -> society ( for human, 4

million y, for insects, 0.2 billion y?) Society -> Gaia Cell ( now )


Hierarchical Evolution(2)--Society

Vassals and tribes in China

2000BC 10,000

1600BC 3,000

1000BC 800

700BC 140

400BC 10

221BC 1


Hierarchical Evolution(3)--Automata

Operation system: Windows CP/L(?K) : late 1970s DOS(1M): more interrupts Windows 3.1(15M): GUI Windows 95(100M): multi-media IE, Plus, DirectX, ActiveX : Windows 98

(200M) Windows 2000(1G) : various fanciness


Hierarchy in AI: simulate the nature

AI system

Immune system

Molecule

Cell

Multi-cell

SocietyHierarchy

Evolution

Gene

neural network


The way to hierarchy(1): organism

Order increasing

Growth

Accelerate

interaction

Hierarchy

Differentiation

Centralization

Multiplication

Organism

The physical freedom of Molecule and cell are decreasing ( less entropy)

Growth of individual; the average body volume increase in evolution

Species are generated quicker and quicker

Isolated specie becomes “living fossil”

Differentiation of Tissue Organ

The evolution of neural system

Mitosis and amitosis


The way to in hierarchy(2): Society

Order increasing

Growth

Accelerate

interaction

Hierarchy

Differentiation

Centralization

Multiplication

Society

Organization degree are increasing

Growth of total social economy

The acceleration in social development

Open society are developed quicker than isolated society

From individual, group, tribe to nation and international society

More detailed social professional work

Government and international organization

Culture split, language pedigree ；colonizztion


The way to in hierarchy(3): OS

Order increasing

Growth

Accelerate

interaction

Hierarchy

Differentiation

Centralization

Multiplication

Process

and operating

system

Increase of OS size

Exponential growth of OS size

Message and signal between progress

The SDK of OS is composed by many application API

modules in OS are divided more detailedly

Central part controls all processes

The replication of progress and virus.


The way to hierarchy(4): WAN

Order increasing

Growth

Accelerate

interaction

Hierarchy

Differentiation

Centralization

Multiplication

Wide area network

From unrestricted to be controlled and managed

The extend of network size

Rapid almost exponent growth

Protocol and messages

WAN, LAN, terminal

More and more different kind of websites

Formation of Portal, manage center, service center

?


The way to hierarchy(5): NN

Order increasing

Growth

Accelerate

interaction

Hierarchy

Differentiation

Centralization

Multiplication

Neural Network

After training, input can converge to some attractors

(evolutionary neural network)

? Weights

Group network with complex behavior by neurons

From less structured to complex structured, such as layer

? ?


The way to hierarchy(6): GA

Order increasing

Growth

Accelerate

interaction

Hierarchy

Differentiation

Centralization

Multiplication

Genetic Algorithm

Schema theorem

Fitness From individuals to population

Selection , crossover,mutation and new population


The way to hierarchy(7): MAS

Order increasing

Growth

Accelerate

interaction

Hierarchy

Differentiation

Centralization

Multiplication

Multi-agent system

Converge to equilibrium point

Agent Language

Simple agent and complex agent society


Hierarchical AI System: Social Computational intelligence: neural network,

multi-agent system (MAS), evolution computation and artificial immune system.

The basic idea of computational intelligence is “social computation”, that’s, complex intelligence can be obtained self-organizingly by simple intelligence individuals under some simple social rules (including competition, cooperation and so on).


Hierarchical AI System: EcologicalSuch a “social computation” system can be regarded

as an hierarchical artificial ecology system, which has similar property and development to nature ecology system. So basic laws of computational intelligence can be regarded as “general ecology”.


Hierarchical AI System: Self-organization

Some general laws in computational intelligence, such as order-increasing; information interchange; hierarchy structure and development; progressive centralization; progressive Differentiation, are in fact general properties of a kind of self-organizing systems.


Hierarchical AI System: Self-organization(2)

Possible system

state space in early stage

Possible system state

space in early stage


Hierarchical AI System: Self-organization(3)

Order

limited growth, stable or slow development.

break through developing obstacle by hierarchy with higher order but less freedom degree. More efficient in energy using and can use more energy that lower-level system can’t utilize

abundant resource stage , exponential growth


Hierarchical AI System: interdisciplinary

Therefore, the development of hierarchical learning in computational intelligence, especially the hierarchical MAS, is closely related to the development of life sciences and social sciences. Neural Network (top-down) and MAS (bottom-up) are integrated methods to carry out the research in practice.


Hierarchical learning in Neural Network Ensemble learning Modular learning Hybrid learning


Hierarchical learning in GA Behavior evolution: hierarchical

structure in nonlinear coding (tree) Diversification of population Multi-level selection


Hierarchical learning in MAS Evolution of cooperation by Reinforcement

learning Hierarchal Markov game: game between groups Hierarchical entity: agent and their society Hierarchical relationship: language

hierarchical learning in ai - general problem

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