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Improving AI Decision Modeling Through Utility Theory
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Description

Improving AI Decision Modeling Through Utility Theory
Speaker: Kevin Dill (Software Engineer, Lockheed Martin), Dave Mark (President and Lead Designer, Intrinsic Algorithm)
Date/Time: Tuesday (March 9, 2010) 3:00pm — 4:00pm
Location (room): Room 310, South Hall
Experience Level: Intermediate
Summit: AI Summit
Format: 60-minute Lecture

Session Description
The 'if/then' statement has been the workhorse of decision modeling longer than digital computing. Unfortunately, the harsh transition from yes to no often expresses itself through behavior in ways that are just as harsh. Utility theory has roots in areas such as psychology, economics, sociology, and classical game theory. By applying the science of utility theory with algorithmic techniques such as response curves, population distributions, and weighted randoms, we can improve the modeling of the underlying brain of our agents, broaden the potential decision space, and even manage edge cases that other decision systems stumble over.

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