Comprehensive top-K recommender system for command and control, using novel evaluation and optimization algorithms

Victor Bajenaru; James Vaccaro; Mitchell Colby; Brett Benyo

doi:10.1117/12.2615933

6 June 2022 Comprehensive top-K recommender system for command and control, using novel evaluation and optimization algorithms

Victor Bajenaru, James Vaccaro, Mitchell Colby, Brett Benyo

Author Affiliations +

Proceedings Volume 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV; 121130V (2022) https://doi.org/10.1117/12.2615933
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

End-users of Command and Control (C2) systems can struggle to effectively determine which Course of Action (CoA) should be chosen out of many complex options. A novel top-K recommender system has been developed to provide C2 end-users with comprehensive CoA recommendations through four critical measures named for explanation purposes only: Optimality, Diversity, Feedback, and Preference. The Optimality measure introduces a novel “Pareto-mesh” technique for quantitative CoA metrics, which offers robust and computationally efficient comparisons of Pareto-front percent coverages among ranking options. The Diversity measure also introduces a novel linchpin-based qualitative/quantitative technique that considers qualitative data’s Hamming distances, quantitative data’s Euclidean distances, and optimizes diversity of top-K CoA’s based a chosen top linchpin CoA using Maximin optimization. The Feedback measure is based on implicit user choices, peripheral simulation results, and real-world scenario results, with benchmarks of state-of-the-art collaborative filtering methods SVD, BiVAE, and NCF for this domain. The Preference measure is based on explicit user preference input of quantitative data, and ranks CoA’s based on a simple weighted sum against quantitative CoA metrics. Representative data related to C2 end-users, request attributes, and CoA’s is randomly generated to perform evaluations of each recommender system measure. Hyper-parameters used to calculate each measure are also globally tuned by means of a Genetic Algorithm multi-objective optimization technique, relying on a per-measure, inversion-minimizing optimization function. The designed recommender system’s hyper-parameter training time is on the order of hours and the system inference time is on the order of milliseconds.

Conference Presentation

Citation Download Citation

Victor Bajenaru, James Vaccaro, Mitchell Colby, and Brett Benyo "Comprehensive top-K recommender system for command and control, using novel evaluation and optimization algorithms", Proc. SPIE 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV, 121130V (6 June 2022); https://doi.org/10.1117/12.2615933

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