Amy McGovern

Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density

This paper presents a method by which a reinforcement learning agent can automatically discover certain types of subgoals online. By creating useful new subgoals while learning, the agent is able to… (More)

Autonomous Discovery of Abstractions through Interaction with an Environment

Amy McGovern
SARA
2002

The ability to create and use abstractions in complex environments, that is, to systematically ignore irrelevant details, is a key reason that humans are effective problem solvers. My research… (More)

Scheduling Straight-Line Code Using Reinforcement Learning and Rollouts

The execution order of a block of computer instructions can make a difference in its running time by a factor of two or more. In order to achieve the best possible speed, compilers use heuristic… (More)

Roles of Macro-actions in Accelerating Reinforcement Learning TITLE2:

Amy McGovern, Richard S. Sutton
1997

Several researchers have proposed reinforcement learning methods that obtain advantages in learning by using temporally extended actions, or macro-actions, but none has carefully analyzed what these… (More)

Spatiotemporal Relational Probability Trees: An Introduction

Amy McGovern, Nathan C. Hiers, Matthew W. Collier, David John Gagne, Rodger A. Brown
Eighth IEEE International Conference on Data…
2008

We introduce spatiotemporal relational probability trees (SRPTs), probability estimation trees for relational data that can vary in both space and time. The SRPT algorithm addresses the exponential… (More)

Identifying predictive multi-dimensional time series motifs: an application to severe weather prediction

Amy McGovern, Derek H. Rosendahl, Rodger A. Brown, Kelvin Droegemeier
Data Mining and Knowledge Discovery
2010

We introduce an efficient approach to mining multi-dimensional temporal streams of real-world data for ordered temporal motifs that can be used for prediction. Since many of the dimensions of the… (More)

Exploiting relational structure to understand publication patterns in high-energy physics

Amy McGovern, Lisa Friedland, +4 authors David D. Jensen
SIGKDD Explorations
2003

We analyze publication patterns in theoretical high-energy physics using a relational learning approach. We focus on four related areas: understanding and identifying patterns of citations, examining… (More)

Mobile Agents on the Digital Battlefield

Martin O. Hofmann, Amy McGovern, Kenneth R. Whitebread
Agents
1998

The Domain Adaptive Information System (DAIS) is a mobile intebigent agent system for information discovery and dissemination in a military intelligence network DAIS is used reliably over low… (More)

Using spatiotemporal relational random forests to improve our understanding of severe weather processes

Amy McGovern, David John Gagne, Nathaniel Troutman, Rodger A. Brown, Jeffrey B. Basara, John K. Williams
Statistical Analysis and Data Mining
2011

Major severe weather events can cause a significant loss of life and property. We seek to revolutionize our understanding of and our ability to predict such events through the mining of severe… (More)

Macro-Actions in Reinforcement Learning: An Empirical Analysis

Amy McGovern, Richard S. Sutton
1998

Several researchers have proposed reinforcement learning methods that obtain advantages in learning by using temporally extended actions, or macro-actions, but none has carefully analyzed what these… (More)

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