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A Series of Unlikely Events: Learning Patterns by Observing Sequential Behavior

October 2019 Poster
Eric Heim

This poster represents research to apply Inverse Reinforcement Learning techniques to model sequential behavior.

Publisher:

Software Engineering Institute

Abstract

Modeling patterns of behavior is a task that underlies numerous difficult artificial intelligence tasks: How do I detect when adversaries are deviating from normal routines? How can I automate the teaching of novice analysts to perform complex tasks as if they were expert analysts? In this work, we use a class of techniques called Inverse Reinforcement Learning (IRL) to model sequential behavior to answer questions like these and others.