An HTM-Based C4ISR Visualization Environment

THE PROBLEM The modern battlefield has become overwhelmingly complex. The sheer volume of data, its multimodal, rapidly evolving, and often spatial nature dictate a new approach that allows commanders to more quickly and effectively interpret and interact with the data. While techniques are under study to automate the process, they are often ineffective in interpreting noisy, contradictory, or incomplete data. Furthermore, current architectures don't adequately support the delegation of intel gathering among human operators, nor the division of the task between human and programmatic agents. Ultimately, we need better techniques for enhancing or replacing the human operator wherever possible in battle command. THE SOLUTION We propose to incorporate and validate a cortically-inspired approach, Hierarchical Temporal Memory (HTM) [1] , for dynamic pattern recognition in the domain of C4ISR. HTM is showing great promise in complex pattern recognition, particularly where data is incomplete or ambiguous. We propose to build an intuitive and natural visualization environment embodying this technology. The result will be an easy-to-use system that can infer the intentions of hostile forces from their movements, one that can be deployed into real-world C4ISR data environments. The result will be improved decision-making in the field.