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Wanted: Cognitive security systems that are predictive and forward looking

James Ionson
of JDC Inc.

The state of security is defined by processing data acquired by multiple security peripherals, such as security cameras and radar, as well as motion, biometric, chemical and radioactive sensors, etc. Processing techniques and algorithms facilitate security-event detection and a cognitive security system utilizes symbolic cognitive architectures and inference process algebras that enable the system to possess instinctive qualities and autonomously “learn” and adapt to a dynamically changing security environment. 

These symbolic cognitive architectures and inference process algebras enable the system to infer intentions or activities of aggressors through the detection of their actions. These algebras and architectures have built-in, cost-optimization mechanisms that allow them to deal with undetermined, incomplete and uncertain information.  

These algebras have been successfully applied to the U.S. Office of Naval Research robotics test-bed to derive Generic Behavior Message-passing Language for behavior planning, control and communication of heterogeneous Autonomous Underwater Vehicles operating in hostile environments.  

$-calculus expresses all variables as “cost” expressions. One of the cost functions used in this system is “uncertainty,” which operates using an internal value system that is not only dependent on physical conditions of the real-time security environment, but also depends upon meta-states of the environment associated with unforeseen changes and/or conditions that lie outside the baseline goals and threat profiles of known adversaries.  

The system’s “internal values”

These internal values are designed in accordance with psychological terms that human beings associate with “drives” and “emotions.” These internal values do not actually realize real drives and emotions, but the system is designed to exhibit behavior that is governed by drives and emotions. 

The system imitates emotionally-driven human behavior and responds to dynamic changes in the security state, just as humans might. Specifically, the ”emotional state“ of the security system is strongly influenced by psychological internal values simulated, for example, by “suspicion,” which is associated with an increase in unusual or atypical sensory inputs from security peripherals, and ”curiosity,” when there are dramatic fluctuations in the sensory data. 

These internal values help define the emotional state of the security system with “fear” being associated with increased suspicion and “happiness” being associated with  increased curiosity, through use of symbolic cognitive architectures and inference process algebras.

A cognitive system autonomously updates in real time with new and/or emerging adversary goals and threat profiles that could characterize new or unforeseen security breaches.  


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