NASA’s Earth Science Vision calls for a global sensor web comprised of heterogeneous platforms with on-board information processing, capable of orchestrating real-time collaborative operations with other platforms and ground stations. Such a global sensor web will be a system of systems, including many distributed real-time embedded (DRE) systems, such as multi-satellite formations. Individual systems of the sensor web must collect and analyze large quantities of data via sequences of heterogeneous data collection, manipulation, and coordination tasks to meet specified goals for earth science applications. In large DRE systems, such as those composing a global sensor web, the sheer number of available components often poses a combinatorial planning problem for identifying component sequences to achieve specified goals. Moreover, the dynamic nature of these systems requires runtime management and modification of deployed components.
We present the design of the Multi-agent Architecture for Coordinated Responsive Observations which includes two novel services contributing to the design and deployment of autonomous, predictable, and high performance DRE systems that operate in dynamic and uncertain environments: (i) the Spreading Activation Partial Order Planner (SA-POP) that performs decision-theoretic planning and scheduling using a spreading activation network to capture the probabilistic functional relationships between tasks (implemented as components) and goals; and (ii) the Resource Allocation and Control Engine (RACE), which is an open source adaptive resource management framework built atop standards-based QoS-enabled component middleware. We illustrate the effectiveness of our approach in the face of changing operational conditions, workloads, and resource availability, in the context of salient Earth science missions.