An Advanced Computational Approach to System of Systems Analysis & Architecting Using Agent-Based Behavioral Model
Enterprises and System of Systems
Report Number: SERC-2013-TR-021-3
Publication Date: 2013-11-08
Project: Flexible Intelligent Learning Architectures for Systems of Systems (FILA-SoS)
Dr. Cihan Dagli
A major challenge to the successful planning and evolution of an Acknowledged System of Systems (SoS) is the current lack of understanding of the impact that the presence or absence of a set of constituent systems has on the overall SoS capability. Since the candidate elements of a SoS are fully functioning, stand-alone systems in their own right; they have goals and objectives of their own to satisfy, some of which may compete with those of the overarching SoS. These system-level concerns drive decisions to participate (or not) in the SoS. Typically, individual systems are invited to join the SoS construct, and persuaded to interface and cooperate with other Systems to create the “new” capabilities of the proposed SoS. Current SoS evolution strategies lack a means for modeling the impact of decisions concerning participation or non-participation of any given set of systems on the overall capability of the SoS construct. Without this capability, it is difficult to optimize the SoS design. The goal of this research is to model the evolution of the architecture of an acknowledged SoS that accounts for the ability and willingness of constituent systems to support the SoS capability development. In particular, the research focuses on the impact of individual system behavior on the SoS capability and architecture evolution processes. The agent based model (ABM) structure is developed to provide an Acknowledged SoS manager a decision making tool in negotiation of SoS architectures during wave model cycles The overall ABM consists of 3 major elements; SoS acquisition environment, SoS agent, and a system agent. Each agent has its own set of behavior patterns. SoS meta- architecture obtained from one of the SoS metaarchitectures generation modules, namely; Fuzzy –genetic optimization model, Multi-Level Optimization model, and Multi-objective optimization model, drives the negotiation process. ABM also provides alternatives for participating systems to choose from three types of negotiation models. The negotiation model for SoS is fixed. The ABM has one instance of the SoS agent and multiple instances of the system agent. The number of instances of the system agent corresponds to the number of systems in the SoS. This approach helps create multiple alternatives to generate architectures for acknowledged SoS. An Intelligence, Surveillance and Reconnaissance (ISR) SoS, consisting of 22 individual systems with five capabilities, is used as a domain example to demonstrate the framework of the ABM for one wave cycle. The current analysis environment has matured to the point where it could support SoS analysis and decision-making, which would identify new opportunities to improve the SoS analysis tools. The next step is to create the demonstration and presentation materials necessary to describe the capabilities of the ABM, and provide an overview of analysis tools to potential users identified by the sponsor.