Algorithms for stabilizing intelligent networks

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. Inspired by psychology, these algorithms could be developed and applied towards creating stable, predictable, and artificially intelligent networks. These algorithms collectively represent ways for intelligent systems to identify and correct unpredictable or unstable behaviors, creating stable emotional states that govern behaviors with given specific circumstances, and establishing an evolvable synthetic neural network that can eventually be scaled from low-level functions to higher level decision making processes. These algorithms could be key to research in autonomous spacecraft, nanorobotic swarms, and sensor networks.