Autonomic Autopoiesis

In this method an autonomic entity manages a system through the generation of one or more stay alive signals by a hierarchical evolvable synthetic neural system. The generated signal is based on the current functioning status and operating state of the system and dictates whether the system will stay alive, initiate self-destruction, or initiate sleep mode. This method provides a solution to the long standing need for a synthetic autonomous entity capable of adapting itself to changing external environments and ceasing its own operation upon the occurrence of a predetermined condition deemed harmful.

This innovation is an autonomic method capable of transmitting a neutralizing data signal to counteract a potentially harmful signal. This otoacoustic component of an autonomic unit can render a potentially harmful incoming signal inert. For selfmanaging systems, the technology can offer a selfdefense capability that brings new levels of automation and dependability to systems.

Inventors at NASA have developed a novel approach to optimizing human machine teaming. The technology enables the inclusion of the state of the human operator in system wide prognostics for increasingly autonomous vehicles. It also could inform the design of automation and intelligent systems for low proficiency and reduced crews. The system monitors and measures multiple variables in real time, the status of the human operator and communicates that information to an intelligent machine. Status could include behavior, skill, physical or medical status, or mental state. Once this information pathway is established, the predictability of pilot or operator status will be improved so the autonomous system can be said to develop trust in human operators much like humans develop trust in automation. The system would utilize non-contact instrumentation for biosignal, posture and behavioral gesture sensing for automation decision making.

Some of the current challenges faced by research in artificial intelligence and autonomous control systems include providing self control, resilience, adaptability, and stability for intelligent systems, especially over a long period of time, in changing environments. The Evolvable Neural Software System (ENSS), Formulation for Emotion Embedding in Logic Systems (FEELS), Stability Algorithm for Neural Entities (SANE), and the Logic Expansion for Autonomously Reconfigurable Neural Systems (LEARNS) are foundations for tackling some of these challenges, by providing the basic algorithms evolvable systems could use to manage its own behavior. These algorithms would allow networks to self regulate, noticing unusual behavior and the circumstances that may have caused that behavior, and then correcting to behave more predictably when similar circumstances are encountered. The process is similar to how psychology in organisms evolved iteratively, eventually finding and keeping better responses to given stimuli.

The technology is a self-latching piezoelectric actuator with power-off, set-and-hold capability. Integrated into an aerodynamic control surface or engine inlet, the self-latching piezocomposite actuator may function as a trim tab, variable camber airfoil, vortex generator, or winglet with adjustable shapes. Deflections could be made in-flight, and set and maintained (latched) without a constant power draw, which current piezo actuators require to control and manage their electric fields. The control device leverages the shape memory behavior (specifically, the remnant stress-strain behavior) to create a morphing actuator that changes and holds the new shape with no applied control signal.