Self Optimization using Conceptual Graphs for NASA Autonomous Systems

NASA is pursing ever increasing autonomous systems to perform new science and exploration. These missions will be out of contact with mission control for extended periods of time in very harsh and unforgiving environments. To successfully perform these missions autonomously, they will have to have autonomic properties to achieve their mission goals. One of theses properties, self-optimization, will be vital to these missions since deep space science and exploration by their nature require operations in unknown environments. Coupling this with the autonomous nature of these missions, they will require a means to optimize themselves as the mission progresses and the actual environment becomes better known. This paper introduces a self-optimization technique that is being developed at SAIC based on conceptual graphs. In this paper an overview of conceptual graphs will be given and how they can be used in autonomous systems to achieve self-optimization