Hierarchical Temporal Memory algorithms for understanding asymmetric warfare

There is a problem in combat modeling when it comes to accurately representing asymmetric warfare. The roots of this problem stem from the ways in which warfare has evolved over the last century from force-on-force combat to the network-centric warfare (NCW) that has emerged today in its most exemplary fashion in the Middle East, especially in the context of Joint Urban Operations (JUO). Therefore, this paper focuses on some of the previous attempts as a means to motivate the need for a new technique that understands the interactions on which an accurate representation of NCW rests. Given the difficulties of previous modeling attempts, this work then focuses on the profound impact that human behavior has on the modeling of NCW. Furthermore, with such attention to human behavior, the discussion of modeling NCW shifts to the human brain and its impact on behavior in such an environment. Thus the topic of this paper turns towards what results from neuroscience are useful for understanding NCW and how those facts can be translated into use on modern computers, via the use of hierarchical temporal memory (HTM) algorithms; finally, the inherent issues with such a task shall then be discussed.