Augmented cognition tool for rapid military decision making.

Taylor, Shawn Ellis ; Bernard, Michael Lewis ; Verzi, Stephen J. ; Dubicka, Irene ; Vineyard, Craig Michael

关键词: ALGORITHMS;    COMPUTER ARCHITECTURE;    DECISION MAKING;    LEARNING;    NEURAL NETWORKS;    STORAGE;    DATA ACQUISITION;   

DOI  :  10.2172/1029756
RP-ID  :  SAND2011-7337
PID  :  OSTI ID: 1029756
Others  :  TRN: US201201%%166

学科分类：社会科学、人文和艺术（综合）

美国|英语

来源: SciTech Connect

【 摘 要 】

This report describes the laboratory directed research and development work to model relevant areas of the brain that associate multi-modal information for long-term storage for the purpose of creating a more effective, and more automated, association mechanism to support rapid decision making. Using the biology and functionality of the hippocampus as an analogy or inspiration, we have developed an artificial neural network architecture to associate k-tuples (paired associates) of multimodal input records. The architecture is composed of coupled unimodal self-organizing neural modules that learn generalizations of unimodal components of the input record. Cross modal associations, stored as a higher-order tensor, are learned incrementally as these generalizations form. Graph algorithms are then applied to the tensor to extract multi-modal association networks formed during learning. Doing so yields a novel approach to data mining for knowledge discovery. This report describes the neurobiological inspiration, architecture, and operational characteristics of our model, and also provides a real world terrorist network example to illustrate the model's functionality.