Of Machines and Men: AI and Decision Making

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Information about Of Machines and Men: AI and Decision Making

Published on April 6, 2014

Author: abedsayyad

Source: slideshare.net

“Of Machines and Men” Artificial Intelligence and Decision Making Abdel Salam Sayyad Ph. D. Candidate West Virginia University A faculty candidate talk given at St. Mary’s College of Maryland April 4th, 2014

Bio • 2011 - 2014 – Ph.D. Student, West Virginia University • 2005 - 2011 – Instructor of Computer Engineering, Birzeit University • 2000 - 2005 – Electronic Engineer, Patton Electronics Company, Maryland • 1998- 2000 – Master’s Student, University of Maryland • 1993- 1998 – Bachelor’s Student, Birzeit University 2

Fiction or Futuristic? • https://www.youtube.com/watch?v=05bGPiyM4jg • DISCLAIMER – We at St. Mary’s College of Maryland DO NOT CONDONE racial slurs against robots or persons of robotic heritage. 3

Fiction or Futuristic? • What’s the common thread among: – I, Robot – The Matrix – 2001: A Space Odyssey – Terminator? • Why is it frightening if machines where to make decisions on behalf of people? • How’s that different from using machines to help people make decisions? • When would you feel safe around a robot? 4

Who’s a better decision maker? • Case 1: (I, Robot) • After an accident, a robot calculated that detective Spooner had a 45% chance of survival, but a little girl only had an 11% chance. So, the robot maximized utility and pursued the goal that was most likely to succeed, saving detective Spooner although he pleaded with the robot to save the little girl. • Would a human, given the same knowledge of probabilities, have made a similar decision? • Was it OK to program the robot such that he overruled detective Spooner’s orders? 5

Who’s a better decision maker? • Case 2: (Deep Blue vs. Garry Kasparov) • 1st Match (1996): Kasparov won 4-2 • 2nd Match (1997): Deep Blue won 3.5-2.5 • According to Nate Silver, in game 1 of the 2nd match, Deep Blue made a random move due to a software glitch. Kasparov was thrown off because he couldn’t understand the rationale behind the move, and that led him to lose game 2, which he could have drawn. • Read about it in: 6

Decision Making • Decision Space: – What are all the possible combinations of decisions that can be made? • Objective Space: – How do we measure the “goodness” of each combination of decisions? 7

Genetic Algorithms 8 … and the “optimum” solution is: The fittest individual in the final generation.

Multi-Objective Optimization: No single “optimum” solution 9 Higher-level Decision Making The Pareto Front The Chosen Solution

Survival of the fittest (according to NSGA-II [Deb et al. 2002]) 10

Survival of the fittest (according to IBEA [Zitzler and Thiele 2004]) 11 • Repeat till Pt and Qt are down to the size of Pt: – Compare every individual’s dominance with respect to everyone else – Sort all instances by F – Delete worst, recalculate, delete worst, recalculate, … • Continuous dominance criterion.

Soccer 12 THE BETTER TEAM LOST!!! Better skills Better passes Controlled the ball longer Better coordination But… scored less

Soccer tournament • Did you win? Lose? Or tie? • Win = 2, Loss = 0, Tie = 1. • You are worth your total points. 13 Game Team 1 Team 2 1 Won 6-1 Tied 1-1 2 Won 8-0 Won 2-0 3 Lost 0-1 Won 1-0 Total points 4 5 WHICH ONE IS THE BETTER TEAM?!

The Future of Decision Making • Collaborative • Distributed • Teams of Machines and People. 14

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