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chickenchairwomanAI and Robotics

Oct 19, 2013 (3 years and 11 months ago)

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Management in
complexity


The exploration of a new
paradigm


Complexity in computing and AI

Walter Baets, PhD, HDR

Associate Dean for Innovation and Social Responsibility

Professor Complexity , Knowledge and Innovation

Euromed Marseille


Ecole de Management

Chris Langton


Artificial life research


Genetic programming/algorithms


Self
-
organization (the bee colony)


Interacting (negotiating) agents



Conway’s game of life

One of the earlier artificial life simulations


Simulates behavior of single cells


Rules:



Any live cell with fewer than two neighbors dies of loneliness


Any live cell with more than three neighbors dies of crowding


Any dead cell with exactly three neighbors comes to life


Any cell with two or three neighbors lives, unchanged to the

next generation


John Holland


Father of genetic programming


Agent
-
based systems (network)


Individuals have limited characteristics


Individuals optimize their goals


Limited interaction (communication) rules



Complex Adaptive Systems


Artificial Neural Networks


Agent
-
based systems (network)


Genetic Algorithms


Fuzzy logic


Fuzzy neural networks


ARTIFICIAL NEURAL NETWORKS (ANN) (1)

How does the brain operate?

ARTIFICIAL NEURAL NETWORKS (ANN) (2)


What does an artificial neural network look like?


Out2

Out1


Input Layer


Hidden Layer


Output Layer


X1



X2



X3



X4



X5



Xn

ARTIFICIAL NEURAL NETWORKS (ANN) (3)


How does an artificial neural network works (gets
trained)



NET

TRESHOLD

VALUE

X1



X2



X3



X4

Inputs

W
1

W
2

W
3

W
4

KNOT

Out
-
F (net)

Output

ARTIFICIAL NEURAL NETWORKS (ANN) (4)

Comparison to other DSS techniques (advantages
)


Able to simulate non
-
linear behaviour


Has learning behaviour


Non
-
parametric (no equations)


Fault tolerant (can easily deal with NAs)


Seeking diversity (instead of averages)


Pattern recognition


FUZZY LOGIC (1)


Fuzzy sets and overlapping membership
-
functions

FUZZY LOGIC (2)

Representation of the concept size

using fuzzy sets

FUZZY LOGIC (3)


Fuzzy rules (1)

100


90


80


70


60


50


40


30


20


10


0

0

45 50 55 60 65 70 75 80 85 90

IF WARM

THEN FAST

0

AIR MOTOR SPEED

TEMPERATURE IN DEGREES FAHRENHEIT

FUZZY LOGIC (4)

Fuzzy rules (2)

FUZZY LOGIC (5)

ADVANTAGES:



Smooth behaviour



“Human
-
like” behaviour



Natural language approach

EXAMPLES:



Sendai Subway



Trading systems



Washing machines, CAM
-
corders,


micro
-
waves

FUZZY NEURAL NETWORKS IN MANAGEMENT

Combination of the learning behaviour of neural networks
with the fuzziness and the (though fuzzy) rules


Overlapping and vague memberships is a reality in managerial
problems


Fuzzy rules is a reality in management


Fuzzy and learning behaviour is very human


Pretty much to be discovered in management sciences

GENETIC ALGORITHMS (1)

GENETIC ALGORITHMS (2)

GENETIC ALGORITHMS (3)

GENETIC ALGORITHMS (4)

GENETIC ALGORITHMS (5)

GENETIC ALGORITHMS (6)

GENETIC ALGORITHMS (7)

GENETIC ALGORITHMS (8)

A beginning of evidence

Some research projects

Complexity and emergent learning in innovation projects:


Agents, Sara Lee/DE

Innovation in SME’s: a network structure:


ANNs, brainstorm sessions

Telemedecin: a systemic research into the ICT innovations in the


medical care market:


Agents

Knowledge management at Akzo Nobel: improving the knowledge


creation ability:


ANNs, Akzo Nobel

Information ecology:


For the moment a conceptual model


Agents

Conflict management


Agents

Knowledge management at Bison: contribution to innovation


Agents

Complexity in
economics

Law of increasing returns

(Brian Arthur)



Characteristics of the information economy


(a non
-
linear dynamic system)




Phenomenon of increasing returns




Positive feed
-
back




No equilibrium




Quantum structure of business


(WB)

Summary (until now)



Non
-

linearity



Dynamic behavior



Dependence on initial conditions



Period doubling



Existence of attractors



Determinism



Emergence at the edge of chaos