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Both My GDC AI Summit Lectures on Utility Theory FREE on GDC Vault!

It turns out that both of the GDC AI Summit lectures that I did with Kevin Dill are up on the GDC Vault for free now! Thanks to the folks at GDC for doing this — it’s a great honor to have my lectures selected as being among the free ones!

The links below will take you to the respective videos on the GDC Vault. If you are planning on attending the 2013 AI Summit at GDC, it will be helpful to watch these first. My lecture this year is a new structure for building utility-based systems using the mathematical concepts expressed in these prior lectures.


From 2010:

gdc_2010

Improving AI Decision Modeling Through Utility Theory
Dave Mark, Intrinsic Algorithm
Kevin Dill, Lockheed Martin

 

Overview:
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.


From 2012:

gdc12_logoEmbracing the Dark Art of Mathematical Modeling in AI
Dave Mark, Intrinsic Algorithm
Kevin Dill, Lockheed Martin

Overview:
Utility-based AI is a widely-used approach, particularly for games with deeper or more complex behavior. While new users may find utility functions complex and intimidating, experienced users see them as a natural and comfortable way to express behavior. In a follow-up of their 2010 lecture, Kevin Dill and Dave Mark will show how simple problems can be laid out quickly and easily using common design patterns. Additionally, they will show how complex situations can make use of utility functions to express more nuanced behavior. They will then walk through real-world examples, showing how they would be expressed in a utility-based architecture.

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