Reinforcement learning framework for collaborative agents interacting with soldiers in dynamic military contexts

Sean L. Barton; Derrik Asher

doi:10.1117/12.2303827

27 April 2018 Reinforcement learning framework for collaborative agents interacting with soldiers in dynamic military contexts

Sean L. Barton, Derrik Asher

Author Affiliations +

Proceedings Volume 10653, Next-Generation Analyst VI; 1065303 (2018) https://doi.org/10.1117/12.2303827
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

Modern Soldiers increasingly rely on computational and autonomous systems for the completion of their missions^1,2. Challenges arise around the use of such systems that depend largely on the relationship between agents and human actors³ . Improving agents in the field requires the development of adaptive, human-aware systems that learn behaviors based on the needs of their human counterparts, acting effectively as teammates rather than tools⁴ . The development of such agent teammates is non-trivial, but recent advances in machine-learning and artificial-intelligence are promising. We identify deep reinforcement learning (RL)⁵ , multi-agent RL⁶ , and human-guided RL⁷ as powerful tools for the creation of adaptive agent teammates. We propose a three-armed approach to the development of agent teammates that leverages these advances in RL. First, multi-agent deep learning can be used to solve increasingly complex problems. Second, human-guided reinforcement can be used to constrain agent behavior and speed up the discovery of optimal strategies. Third, human behavioral profiles derived from surveys of work-interest variables for specific military occupation specialty (MOS) codes can be used to tailor agent behavior to the needs of Soldiers. This approach addresses the necessary computational framework, the learning paradigm needed to discover behavior, and the human dimension that contextualizes behavior.

Conference Presentation

Citation Download Citation

Sean L. Barton and Derrik Asher "Reinforcement learning framework for collaborative agents interacting with soldiers in dynamic military contexts", Proc. SPIE 10653, Next-Generation Analyst VI, 1065303 (27 April 2018); https://doi.org/10.1117/12.2303827

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available