## 4 Modeling and Simulation Methods and Their Applications
### Metamodel and Metamodeling Supporting an Autonomous System  
Modeling languages such as SysML provide particularly useful graphical syntax for human understanding. However, because they only contain abstract semantics, it is necessary to add concrete semantics related to domain knowledge for domain-specific applications. The essence of a metamodel is to create an intermediate layer between a common abstract modeling language and specific implementation instances to represent domain knowledge. Its key role lies in achieving business knowledge extraction and model reuse. The process of creating a metamodel, which we call metamodeling methods, has also become a core activity in implementing specific practices of the MBSE methodology. In order to overcome the learning curve of modeling languages, methods, and tools, metamodels and metamodeling will become key enablers for promoting the transformation of traditional systems engineering processes into methods of MBSE. Model-driven architecture is a process that focuses on models and is driven by model mapping. The system development approach in the MDA environment aims to accurately describe different problem spaces by creating various models during development activities and using model transformations to drive the entire development process, including analysis, design, and implementation. The Platform-Independent Model (PIM) is then built by specializing the run-time properties in SysML diagrams, such as the unmanned adaptive  
Figure 2: Example of swarm ontology model for a specific mission. control units and their dynamic evolution, which reflect the structures and behaviors of the design components. This section mainly introduces metamodels with unified semantic interpretation for a behavior model of artificial intelligence systems, which can be used as a reference to guide the specification and implementation of various autonomous unmanned systems across many domains in the MBSE process, such as Unmanned Aerial Vehicle (UAV), Unmanned Ground Vehicle (UGV), Unmanned Underwater Vehicle (UUV), etc. From the viewpoint of Command, Control, and Communication (C3), the interoperability allows information interactions between an unmanned system and others and information sharing and task allocation between different command levels and different units, e.g., STANAG 4586--Standard Interfaces of UAV Control System (UCS) for NATO UAV Interoperability, which defines data formats, interface requirements, communication protocols, etc. [28]. Meanwhile, from another viewpoint of Open System Architecture (OSA), we should consider a common/open architecture, modular component, test verification, and data integration for unmanned systems. As another example, the SAE standard Joint Architecture for Unmanned Systems emphasizes capturing and categorizing common interfaces and services to enable the continued growth of standard sets and robotic technology [29]. The primary paradigm of Artificial Intelligence (AI) is mainly knowledge-based systems, in which the knowledge related to application domains, the external environment, and the decision making process are defined and presented via symbolic models. On the other hand, MBSE advocates for the specification, analysis, design, verification, and validation of systems using formal models. Therefore, from the perspective of the development of modern AI systems, our first choice is the representation language of symbolic knowledge and the automatic reasoning mechanism based on logical language. With the V-model, it depicts the development activities that go through the ConOps to the integration and the V&V that helps identify errors early in the life cycle. However, the V-model has the drawback of being rigid and less flexible. However, the walking skeleton model is a lean approach for incremental development, popularly used in software design, and especially suitable for a systems approach to AI implementation to rapidly adjust the scope of a system.