# The Role of Multi-Agent Systems in Realizing Asset Administration Shell Type 3
## 3 AAS Type 3 Approaches Based on Multi-Agent Systems
\begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Ref.** & **Application Domain** & **Solution Description** \\ \hline
[26] & Smart manufacturing (proto-typical implementation in a demonstrator) & - Discusses that the asset information described in the AAS may be used as a standardized agent’s knowledge representation, i.e., the agents get all the relevant information to make decisions from AAS submodels - Assets: demonstrator (a stack, a crane, a stamp, and a conveyor with ramps and pushers) \\ \hline
[29; 30] & Smart manufacturing (proto-typical implementation in a demonstrator) & - Proposes a design pattern for implementing AAS based on industrial agents. The agents are responsible for decision-making capabilities and integration with physical assets - Assets: product and industrial robot \\ \hline
[31] & Smart manufacturing (simulated experiments) & - Presents a method for developing AAS using the MAS as a key enabler to distribute intelligence and offer collaborative functionalities - Assets: assets simulated in a virtual environment \\ \hline
[32] & Dynamic replanning in production environments (proto-typical implementation in a demonstrator) & - Concentrates on modeling AAS submodels for resource and product agents to use in dynamic replanning applications - Assets: demonstrator (a module for transportation, assembly, flashing of information on the product, and two quality control modules) \\ \hline
[33] & Human–robot shared assembly task (prototypical implementation in a demonstrator) & - Proposes a framework that combines MAS with AASs to enable the human-centered production paradigm. The focus is on the active involvement of operators in the industrial collaboration process with other assets - Assets: human, robot, and camera \\ \hline
[34] & Plan and control in industrial automation systems (proto-typical implementation in a demonstrator) & - Uses LLM agents to interpret complex information, generate insights, and support decision-making in industrial automation systems. The information is primarily sourced from AASs, which provide descriptive details about a specific asset - Assets: transport robots and painting stations \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of research works that combine MAS and AAS. Overall, there is a general consensus that submodels serve as standardized knowledge representations or information sources for agents. These submodels capture all asset-related information in both human- and machine-readable formats, which agents can leverage to support decision-making processes and to construct or enrich their internal knowledge bases. In this context, several standardized submodels are available for this purpose, e.g., _Control Component Instance_[39], _Asset Interfaces Description_[15], and _Time-Series Data_[40], which can be used to describe the skills provided by an asset (e.g., drilling, assembly, and transport), the methods for interfacing with it (e.g., host address, port, connection state, communication protocol, endpoints, data formats, and input/output parameters), and the mechanisms for managing, accessing, and storing operational data. Additionally, other submodels can be defined to address specific application needs and to represent agent-specific information, such as descriptions of agent behaviors, decision-making strategies, or interaction protocols, providing greater transparency and understanding of agent capabilities. The standardized structure of submodels enables agents to interpret and process them using suitable programming logic, promoting reusability, scalability, and consistency across heterogeneous assets and application domains. Moreover, agents that are equipped with Large Language Models (LLMs) could enhance their learning and interpretation of submodels. This is possible due to LLMs' ability to process both structured and unstructured data, generalize across different formats, and adapt to varied contexts, ultimately improving the system's flexibility and responsiveness.