Hybrid Intelligent SystemsHybrid Intelligent Systemsfor Robotics

Prof Adrian HopgoodProf Adrian HopgoodDe Montfort University

OutlineBackground

Hi t– History– Spectrum of intelligence

Why hybrids?Multifaceted problems– Multifaceted problems

– Clarification and verification– Capability enhancement– Parameter settingg

Embedded hybridsC l iConclusion

Artificial Intelligenceg

A definition:– the science of mimicking human mental g

faculties in a computer

The beginnings:– Turing 1950– Dartmouth Conference 1956Dartmouth Conference 1956

1956…

2008...

Asimo video clipAsimo video cliphttp://asimo.honda.com/movie/qthttp://asimo.honda.com/movie/qt_download/intelligence_tech_300.

movmov

A spectrum of intelligenceg

expertiseexpertiseplanninginteractioncommon sense level of perceptioncoordination

understanding

coordinationreactionregulation

Spot the rabbit

Specialist end of the spectrumDENDRAL (1969)MYCIN (1976)SmartGov (2003)SmartGov (2003)ARBS / DARBS– ultrasonics (1993)– telecommunications (1994)( )– control of robot arms (2003)– plasma deposition (1998 2002)– plasma deposition (1998, 2002)– medical imaging (2006)

Why hybrids?y yMultifaceted problemsClarification and verificationCapability enhancementCapability enhancementParameter setting

Multifaceted problemsMultifaceted problems

Agents within agentsg g

Blackboard multiagent modelg

DARBS: Distributed Algorithmic & Rule-based Blackboard System

Rule-based agentsInference mode #1

Rule-based agentsInference mode #2

Rule-based agentsInference mode #3

The Blackboard

Proceduralagents

Genetic algorithmagents

Neural networkagentsg g g

Some early applicationsy

Plasma Processingg

Tuning a Langmuir probeg g

Multivariable control

Embedded hybridsEmbedded hybrids

emDARBS on SARNet

Tileworld (Pollack and Ringuett, 1990)

10987654321112345678

Agent =

Hole =

910

Obstacle =

Tile =

TileWorld on DARBS

Processor & agent numbers

3 50

4.00

g

3.00

3.50p

2.50

Spee

dup

1.50

2.00

1.001 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Number of agent KSs

1 Agent Processor 2 Agent Processors 3 Agent Processors4 Agent Processors 5 Agent Processors 6 Agent Processors7 Agent Processors 8 Agent Processors 9 Agent Processors10 Agent Processors 11 Agent Processors Starting point trend

Clarification and verificationClarification and verification

Ultrasonic scanningg

Raw imageg

Verified interpretation

Capability enhancementCapability enhancement

Lamarckian/Baldwinian inheritance

Steepest gradient descent search beforeSteepest gradient descent search before fitness evaluation and selectionSearch the Hamming neighbourhood of each candidate solution for a fitter oneeach candidate solution for a fitter oneThen either:– replace the candidate solution with the fitter

one (Lamarck), or( ),– keep the candidate solution but substitute the

neighbour’s fitness value (Baldwin)neighbour s fitness value (Baldwin)

Hamming distancegNumber of bit positions that are differentHamming distance of 1:– 1101001 and 1100001– 1101001 and 1111001

Hamming distance of 2:Hamming distance of 2:– 0101100 and 1001100– 0101100 and 0100000

For you:For you:– 1101001 and 1000001– 0101100 and 1101001

The Effect of Learning Strategy on Solution Quality of Schwefel Function (d=4)-50

The Effect of Learning Strategy on Solution Quality of Schwefel Function (d=4)

100%B20%L40%L60%L

-10060%L80%L100%L

-150

Fitn

ess

-200

Aver

age

F

300

-250

-350

-300

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-350

Probability of Local Search

Th Eff t f L i St t S l ti Q lit f F1

3.95

4The Effect of Learning Strategy on Solution Quality of F1

100%B25%L50%L75%L

3.9

3.95 75%L100%L20%L

3.85

Fitn

ess

3.75

3.8

Ave

rage

F

3.7

3 6

3.65

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 13.6

Probability of Local Search

Parameter settingParameter setting

Genetic–fuzzy systems (Karr)y y ( )1

,µership,

t 1 t 4t 3t 2

Membe set 1

(low)set 4(high)

set 3set 2

0

M

n 2 n 3 x 1 x 2 n 4 x 3

Fuzzy variable

minmin

max

max min

max

Chromosome (binary):

Fuzzy variable

min4min3min2 max2 max3max1

( y)

Why hybrids?y yMultifaceted problems

( b dd d) bl kb d d l– (embedded) blackboard modelClarification and verification– use of supporting evidence

Capability enhancementCapability enhancement– Lamarckian/Baldwinian inheritance

Parameter setting– genetic–fuzzy– genetic–fuzzy

Kärcher RobocleanerKärcher Robocleaner video clipvideo clip

http://uk.youtube.com/watch?v=1zhttp://uk.youtube.com/watch?v 1zFU6BCR-lA

Intelligent robotics: the futureg

Bridging the intelligence spectrum through hybridsg y

E b dd d AIEmbedded AI

DARBS / emDARBSb dd d ll l h d– embedded parallel hardware

– new applications– open source

AcknowledgementsgARBS/DARBS/emDARBS

L N ll P t i k W K W h Ch– Lars Nolle, Patrick Wong, Kum Wah Choy, Gary Li, Heather Phillips, Hua Meng, N il W d k Ni h l H llNeil Woodcock, Nicholas Hallam

Plasma control– Nick Braithwaite, Phil Picton,

Jafar Al-Kuzee Lars Nolle Heather PhillipsJafar Al Kuzee, Lars Nolle, Heather PhillipsHybrid genetic algorithms– Tarek El-Mihoub