Design Of Complex Engineered Systems Using Multiagent Coordination
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Design of Complex Engineered Systems Using Multiagent Coordination
This thesis is the combination of two research publications working toward a unified strategy in which the design of complex engineered systems can be completed using a multiagent coordination approach. Current engineered system modeling techniques segment large complex models into multiple groups to be simulated independently. These methods restrict the evaluations of such complex systems, as their failure properties are typically unknown until they are experienced in operation. In an effort to help engineers to design complex engineered systems, this research proposes that a distributed yet non-legislated approach can be used in the design processes by splitting up the overall system into specific teams. The approach specifically hypothesizes that multiagent credit assignment can be used to effectively determine how to properly incentivize subsystem designers so that the global set of system-level objectives can be achieved. The first publication presents a multiagent systems based approach for designing a selfreplicating robotic manufacturing factory in space. The simulation in this work is able to present the coordination of the agents during the construction of the factory as the parameters of the learning algorithm are changed. The results show the advantage of using a learning algorithm to design a large system. The second publication presents a hybrid approach to design complex engineered systems, providing a method in which design decisions can be reconciled without the need for either detailed interaction models or external legislating mechanisms. The results of this paper demonstrate that a team of autonomous agents using a cooperative coevolutionary algorithm can effectively design a complex engineered system. Each publication utilized a system model to illustrate and simulate the methods and potential results. By designing complex systems with a multiagent coordination approach, a design methodology can be developed in an effort to reduce design uncertainty and provide mechanisms through which the system level impact of decisions can be estimated without explicitly modeling such interactions.
Complex Engineered Systems
Recent advances in science and technology have led to a rapid increase in the complexity of most engineered systems. In many notable cases, this change has been a qualitative one rather than merely one of magnitude. A new class of Complex Engineered Systems (CES) has emerged as a result of technologies such as the Internet, GPS, wireless networking, micro-robotics, MEMS, fiber-optics and nanotechnology. These complex engineered systems are composed of many heterogeneous subsystems and are characterized by observable complex behaviors that emerge as a result of nonlinear spatio-temporal interactions among the subsystems at several levels of organization and abstraction. Examples of such systems include the World-Wide Web, air and ground traffic networks, distributed manufacturing environments, and globally distributed supply networks, as well as new paradigms such as self-organizing sensor networks, self-configuring robots, swarms of autonomous aircraft, smart materials and structures, and self-organizing computers. Understanding, designing, building and controlling such complex systems is going to be a central challenge for engineers in the coming decades.
Advances in Computer Science for Engineering and Education III
This book comprises high-quality refereed research papers presented at the Third International Conference on Computer Science, Engineering and Education Applications (ICCSEEA2020), held in Kyiv, Ukraine, on 21–22 January 2020, organized jointly by National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, National Aviation University, and the International Research Association of Modern Education and Computer Science. The topics discussed in the book include state-of-the-art papers in computer science, artificial intelligence, engineering techniques, genetic coding systems, deep learning with its medical applications, and knowledge representation with its applications in education. It is an excellent source of references for researchers, graduate students, engineers, management practitioners, and undergraduate students interested in computer science and their applications in engineering and education.