A Series of Unlikely Events: Learning from Sequential Behavior for Activity-Based Intelligence and Modeling Human Expertise
• Presentation
This presentation describes work to use inverse reinforcement learning techniques to perform activity-based intelligence.
Publisher
Software Engineering Institute
Subjects
Abstract
The Department of Defense (DoD) and the intelligence community (IC) frequently analyze activity based intelligence (ABI) to inform missions about routine patterns of life (POL) and unlikely events that signal important changes. We propose an alternative approach, inverse reinforcement learning (IRL), that observes all states and actions in data and computes a statistical model of the world that includes whether each behavior is part of a routine.
Part of a Collection
CMU SEI Research Review 2019