Intelligent machines, the “idiots savants”

A High Dessert presentation at University College

By Felisa J Vázquez-Abad

Dept of Computer Science and Operations Research

University of MontréalDept of Electrical and Electronic Engineering

University of Melbourne

Monday, May 21 2001

Felisa Vázquez-Abad, High Dessert at University College.

Intelligence and adaptation• Intelligent creatures have control

over the environment• Adapt the surroundings vs

adapting to the conditions of the environment

• What are the elements of control?Understanding Prediction of outcomesCapacity of adaptation

Monday, May 21 2001

Felisa Vázquez-Abad, High Dessert at University College.

Control Theory: the beginning

Description: Physics in the XVIII Century Galileo Galilei: 1564-1642, invents

methodological approach. Sir Isaac Newton: 1643 -1727,

seeks a unifying theory of Physics.

Newton’s Second Law: F = ma … F(t) = s p(s) ds, p(t) = m v(t). Momentum,All objects (including light) travel using the minimum

energy path: J = s L(m, x(t)) dt, control v(t) = x’(t)

“God controls the motion through the velocities”

Monday, May 21 2001

Felisa Vázquez-Abad, High Dessert at University College.

Science and Engineering: from theory to

practiceXVIII and XIX Centuries

Understanding: mathematical models (Newton + Leibniz) and theoretical physics,

• Mechanics• Thermodynamics• Electricity and MagnetismEngineering: inventions, creation of engines• Mechanical objects• Locomotives

Monday, May 21 2001

Felisa Vázquez-Abad, High Dessert at University College.

Science and Engineering: from theory to

practiceXVIII and XIX Centuries

Understanding: mathematical models (Newton + Leibniz) and theoretical physics,

• Mechanics• Thermodynamics• Electricity and MagnetismEngineering: inventions, creation of engines• Mechanical objects• Locomotives

Monday, May 21 2001

Felisa Vázquez-Abad, High Dessert at University College.

Engineering: the 20-th CenturyGood models: electricity,

magnetism, electronics, quantum physics…

• Household appliances

Monday, May 21 2001

Felisa Vázquez-Abad, High Dessert at University College.

Engineering: the 20-th CenturyGood models: electricity,

magnetism, electronics, quantum physics…

• Household appliances

• Telecommunications

• Computers

Monday, May 21 2001

Felisa Vázquez-Abad, High Dessert at University College.

Engineering: the 20-th CenturyGood models: electricity,

magnetism, electronics, quantum physics…

• Household appliances

• Telecommunications

• Computers

New life style, we create machines and adapt our surroundings…

Monday, May 21 2001

Felisa Vázquez-Abad, High Dessert at University College.

Beyond control: intelligence50’s science fiction: the image of the future

Not quite…

Monday, May 21 2001

Felisa Vázquez-Abad, High Dessert at University College.

Beyond control: intelligence50’s science fiction: the image of the future

Not quite… but

Monday, May 21 2001

Felisa Vázquez-Abad, High Dessert at University College.

Beyond control: intelligence50’s science fiction: the image of the future

Not quite… but

“intelligent’’ washing machines (fuzzy logic, automatic control)

Monday, May 21 2001

Felisa Vázquez-Abad, High Dessert at University College.

Beyond control: intelligence50’s science fiction: the image of the future

Not quite… but

Automatic pilots,Robots for mining,Space travel...

Monday, May 21 2001

Felisa Vázquez-Abad, High Dessert at University College.

Control and OptimisationOptimisation: mathematical problem• Objective function (cost)• Control variable u

min J(u), u 2 UThreshold controls:• Maintenance• Telecommunications (e-commerce)• Stabilising mechanisms• Energy supplies

Monday, May 21 2001

Felisa Vázquez-Abad, High Dessert at University College.

Optimisation, examplesMaintenance Strategy

Several components of a system, subject to failures.

Failed component: very expensive, replace or fix.

Preventive replacements: when age is over L.Cost: if L is too small we are paying too

dearly and discarding working components, if L is too big we are risking failures and

this may be fatal.… how to choose optimal L ?

Monday, May 21 2001

Felisa Vázquez-Abad, High Dessert at University College.

Optimisation, examplesMaintenance Strategy

Several components of a system, subject to failures.Failed component: very expensive, replace or fix.Preventive replacements: when age is over L.

We invented a new method using advanced simulation techniques: the computer recreates a series of scenarios in parallel “imaginary worlds” to choose the best value of L.

(2000 Jacob Wolfowitz Prize for Theoretical Advances in

the Mathematical and Management Sciences)

Monday, May 21 2001

Felisa Vázquez-Abad, High Dessert at University College.

Optimisation, complex systems • Multiple objectives (minimal cost

and better service)• Complex interactions (several

components in system: networks)• Uncertainty in external conditions• Control agents should be as

independent as possible (decentralised, asynchronous control)

Monday, May 21 2001

Felisa Vázquez-Abad, High Dessert at University College.

Complex systems: example

Transportation: Subway networkCost: trade-off between operational cost

and social cost (wait of passengers)Control: Frequency of trains on each line.

Per line: if frequency is too high wait is small but cost is high, and viceversa: seek for optimum.

Monday, May 21 2001

Felisa Vázquez-Abad, High Dessert at University College.

Complex systems: example

Transportation: Subway networkInteraction between lines? Benefit of one may

yield penalties for others: transfer passengers.

Monday, May 21 2001

Felisa Vázquez-Abad, High Dessert at University College.

Complex systems: example

Transportation: Subway networkInteraction between lines? Benefit of one may

yield penalties for others: transfer passengers.

Greedy algorithms do not yield global optimality:

Individual benefit is notThe benefit of all

Monday, May 21 2001

Felisa Vázquez-Abad, High Dessert at University College.

Complex systems: exampleSeek optimum: slowly change the

controls in the direction of improvement of the cost to seek optimality.

Gradient-search methods: J(u)

u

Slope=gradient

Method: u(n+1) = u(n) - r J(u)

Monday, May 21 2001

Felisa Vázquez-Abad, High Dessert at University College.

Beyond Optimisation: learning• Static control: mathematical model,

optimisation, find optimal control.• Changes in external or internal

conditions: – operating components become old, – users of system change patterns

• Dynamic control: prediction and anticipation to changes, adaptation.

Monday, May 21 2001

Felisa Vázquez-Abad, High Dessert at University College.

Learning: exampleTelecommunications

Mobile switching centre: calls from other geographical regions arrive into MSC. It then looks for user (if his phone is available) to connect call within local service area.

MCS

Monday, May 21 2001

Felisa Vázquez-Abad, High Dessert at University College.

Learning: exampleTelecommunications

Interference: to search the user, signals are sent from MSC to the power station at target cell. This signal may cause interference with on-going calls which are connected.

Search strategy: exhaustive always finds client but degrades performance, – one trial only? Which one? – Can we learn patterns of behaviour?

Monday, May 21 2001

Felisa Vázquez-Abad, High Dessert at University College.

Learning: exampleTelecommunications

System under uncertainty

Control u

Measurements: J(u) and the sensitivity

rJ(u)

u(n+1) = u(n) - r J(u)

Monday, May 21 2001

Felisa Vázquez-Abad, High Dessert at University College.

Beyond Control: Intelligence

Elements of learning:

• Measure performance (define model)

• Understand impact of our actions (sensibility)

• Capacity to react to those measurements (the updating algorithms)

Monday, May 21 2001

Felisa Vázquez-Abad, High Dessert at University College.

Beyond Control: Intelligence

My View of Control in XXI Century• Global information not physically localised,• Improvement mechanisms result from

collective coherent behaviour of simple components,

• Each component acts independently and locally,

Capacity for learning and adaptation: sum of individual “simple-minded” efforts.

The “idiots savants” are the simple control agents distributed across a complex system.