“Of Machines and Men”Artificial Intelligence and Decision Making

Abdel Salam Sayyad

Ph. D. CandidateWest Virginia University

A faculty candidate talk given at St. Mary’s College of MarylandApril 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

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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.

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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?

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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?

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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:

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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?

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Genetic Algorithms

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… and the “optimum” solution is:

The fittest individual in the final generation.

Multi-Objective Optimization:No single “optimum” solution

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Higher-level Decision Making

The Pareto Front

The Chosen Solution

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

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Survival of the fittest(according to IBEA [Zitzler and Kunzli 2004])

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• 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

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THE BETTER TEAM LOST!!!

Better skillsBetter passes Controlled the ball longer

Better coordination But… scored less

The better team loses in single-objective optimization

Soccer tournament

• Did you win? Lose? Or tie?

• Win = 2, Loss = 0, Tie = 1.

• You are worth your total points.

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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 better team loses because of NSGA-II

The Future of Decision Making

• Collaborative

• Distributed

• Teams of Machines and People.

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