1. 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 Montréal Dept of Electrical and Electronic Engineering University of Melbourne

2. 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 outcomes Capacity of adaptation

3. 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”

4. Monday, May 21 2001 Felisa Vázquez-Abad, High Dessert at University College. Science and Engineering: from theory to practice XVIII and XIX Centuries Understanding: mathematical models (Newton + Leibniz) and theoretical physics, Mechanics Thermodynamics Electricity and Magnetism Engineering: inventions, creation of engines Mechanical objects Locomotives

5. Monday, May 21 2001 Felisa Vázquez-Abad, High Dessert at University College. Science and Engineering: from theory to practice XVIII and XIX Centuries Understanding: mathematical models (Newton + Leibniz) and theoretical physics, Mechanics Thermodynamics Electricity and Magnetism Engineering: inventions, creation of engines Mechanical objects Locomotives

6. Monday, May 21 2001 Felisa Vázquez-Abad, High Dessert at University College. Engineering: the 20-th Century Good models: electricity, magnetism, electronics, quantum physics… Household appliances

7. Monday, May 21 2001 Felisa Vázquez-Abad, High Dessert at University College. Engineering: the 20-th Century Good models: electricity, magnetism, electronics, quantum physics… Household appliances Telecommunications Computers

8. Monday, May 21 2001 Felisa Vázquez-Abad, High Dessert at University College. Engineering: the 20-th Century Good models: electricity, magnetism, electronics, quantum physics… Household appliances Telecommunications Computers New life style, we create machines and adapt our surroundings…

9. Monday, May 21 2001 Felisa Vázquez-Abad, High Dessert at University College. Beyond control: intelligence 50’s science fiction: the image of the future Not quite…

10. Monday, May 21 2001 Felisa Vázquez-Abad, High Dessert at University College. Beyond control: intelligence 50’s science fiction: the image of the future Not quite… but

11. Monday, May 21 2001 Felisa Vázquez-Abad, High Dessert at University College. Beyond control: intelligence 50’s science fiction: the image of the future Not quite… but “intelligent’’ washing machines (fuzzy logic, automatic control)

12. Monday, May 21 2001 Felisa Vázquez-Abad, High Dessert at University College. Beyond control: intelligence 50’s science fiction: the image of the future Not quite… but Automatic pilots, Robots for mining, Space travel...

13. Monday, May 21 2001 Felisa Vázquez-Abad, High Dessert at University College. Control and Optimisation Optimisation: mathematical problem Objective function (cost) Control variable u min J(u), u 2 U Threshold controls: Maintenance Telecommunications (e-commerce) Stabilising mechanisms Energy supplies

14. Monday, May 21 2001 Felisa Vázquez-Abad, High Dessert at University College. Optimisation, examples Maintenance 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 ?

15. Monday, May 21 2001 Felisa Vázquez-Abad, High Dessert at University College. Optimisation, examples Maintenance 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)

16. 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)

17. Monday, May 21 2001 Felisa Vázquez-Abad, High Dessert at University College. Complex systems: example Transportation: Subway network Cost: 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.

18. Monday, May 21 2001 Felisa Vázquez-Abad, High Dessert at University College. Complex systems: example Transportation: Subway network Interaction between lines? Benefit of one may yield penalties for others: transfer passengers.

19. Monday, May 21 2001 Felisa Vázquez-Abad, High Dessert at University College. Complex systems: example Transportation: Subway network Interaction between lines? Benefit of one may yield penalties for others: transfer passengers. Greedy algorithms do not yield global optimality: Individual benefit is not The benefit of all

20. Monday, May 21 2001 Felisa Vázquez-Abad, High Dessert at University College. Complex systems: example Seek 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)

21. 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.

22. Monday, May 21 2001 Felisa Vázquez-Abad, High Dessert at University College. Learning: example Telecommunications 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

23. Monday, May 21 2001 Felisa Vázquez-Abad, High Dessert at University College. Learning: example Telecommunications 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?

24. Monday, May 21 2001 Felisa Vázquez-Abad, High Dessert at University College. Learning: example Telecommunications System under uncertainty Control u Measurements: J(u) and the sensitivity r J(u) u(n+1) = u(n) -  r J(u)

25. 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)

26. 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.