38 citations · 121 across the 7 of their papers we have counts for
7 papers
Inverse Reinforcement Learning for Strategy Identification
Mark Rucker, Stephen Adams, Roy Hayes +1
In adversarial environments, one side could gain an advantage by identifying the opponent's strategy. For example, in combat games, if an opponents strategy is identified as overly…
Empirically Measuring Transfer Distance for System Design and Operation
Tyler Cody, Stephen Adams, Peter A. Beling
Classical machine learning approaches are sensitive to non-stationarity. Transfer learning can address non-stationarity by sharing knowledge from one system to another, however, in…
Cyberphysical Security Through Resiliency: A Systems-centric Approach
Cody Fleming, Carl Elks, Georgios Bakirtzis +4
Cyber-physical systems (CPS) are often defended in the same manner as information technology (IT) systems -- by using perimeter security. Multiple factors make such defenses insuff…
Value-Decomposition Multi-Agent Actor-Critics
Jianyu Su, Stephen Adams, Peter A. Beling
The exploitation of extra state information has been an active research area in multi-agent reinforcement learning (MARL). QMIX represents the joint action-value using a non-negati…
An Ontological Metamodel for Cyber-Physical System Safety, Security, and Resilience Coengineering
Georgios Bakirtzis, Tim Sherburne, Stephen Adams +3
System complexity has become ubiquitous in the design, assessment, and implementation of practical and useful cyber-physical systems. This increased complexity is impacting the man…
Counterfactual Multi-Agent Reinforcement Learning with Graph Convolution Communication
Jianyu Su, Stephen Adams, Peter A. Beling
We consider a fully cooperative multi-agent system where agents cooperate to maximize a system's utility in a partial-observable environment. We propose that multi-agent systems mu…
Multi-agent Inverse Reinforcement Learning for Certain General-sum Stochastic Games
Xiaomin Lin, Stephen C. Adams, Peter A. Beling
This paper addresses the problem of multi-agent inverse reinforcement learning (MIRL) in a two-player general-sum stochastic game framework. Five variants of MIRL are considered: u…