Matthew J. Robbins
Air Force Institute of Technology
41 Papers
64 Citations
Matthew J. Robbins is an academic researcher from Air Force Institute of Technology. The author has contributed to research in topics: Markov decision process & Computer science. The author has an hindex of 15, co-authored 33 publications. Previous affiliations of Matthew J. Robbins include Wright-Patterson Air Force Base & University of Illinois at Urbana–Champaign.
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Papers
Optimal Policies for the Management of an Electric Vehicle Battery Swap Station
TL;DR: The EVB-SSMP is formulated as a finite-horizon, discrete-time Markov decision problem and an optimal policy is found using dynamic programming to determine the optimal policy for charging and discharging batteries that maximizes expected total profit over a fixed time horizon.
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Evaluating the impact of legislation prohibiting hand-held cell phone use while driving
TL;DR: In this paper, the authors analyzed the impact of hand-held cell phone use on driving safety based on historical automobile-accident-risk-related data and statistics, which would be of interest to transportation policy-makers.
Approximate dynamic programming for missile defense interceptor fire control
TL;DR: An approximate dynamic programming (ADP) approach is utilized to explore the efficacy of applying approximate methods to the problem of optimizing the defensive response to a missile attack, and demonstrates that the ADP policy provides high-quality decisions for a majority of the state space.
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A markov decision process model for the optimal dispatch of military medical evacuation assets
TL;DR: A Markov decision process (MDP) model is developed to examine aerial military medical evacuation (MEDEVAC) dispatch policies in a combat environment and indicates how to optimally dispatch MEDEVAC helicopters to casualty events in order to maximize steady-state system utility.
An Analytical Comparison of Social Network Measures
TL;DR: There exists high correlation among 14 of the 24 tested network measures, many of which also exhibit statistically significant differences with respect to computation time, which are of interest to analysts seeking to identify measures that provide similar ranked outcomes and where computational efficiency is an important consideration.
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