# The Role of Multi-Agent Systems in Realizing Asset Administration Shell Type 3
## 4 Agent-Based Reference Model for Realizing AAS Type 3
### Strategies for Developing AI-Based Behaviors  
Decision-making is another key element identified in the definitions of AAS Type 3, with intelligence being a necessary requirement to support this process. In this context, agents are inherently intelligent entities capable of perceiving their environment, reasoning, and making autonomous decisions to achieve specific goals. However, implementing this process often requires AI-based strategies, ranging from rule-based mechanisms to more advanced AI techniques, e.g., based on Machine Learning (ML) or Deep Learning (DL). The implementation of rule-based mechanisms can be simple and direct, typically using \"if-then\" statements that define clear decision-making logic. However, for more complex, dynamic environments, integrating advanced AI models based on ML/DL into agents significantly enhances their intelligence capabilities. In this sense, this section provides an approach to implementing and deploying such AI models into an agent-based AAS, particularly using standardized, machine-readable interchange formats, such as Predictive Model Markup Language (PMML), Portable Format for Analytics (PFA), and Open Neural Network Exchange (ONNX), which enables the description of AI models in a structured and interoperable manner. It is important to note that the use of PMML, PFA, ONNX, or other standardized formats is highly recommended but not mandatory. These formats may bring several benefits, such as representing AI models in a standardized manner, enhancing interoperability, facilitating seamless integration across diverse systems, and reducing the time required to make a model production-ready. However, alternative approaches to representing and exchanging AI models may also be adopted, depending on specific requirements or constraints, such as unique application needs or legacy system compatibility. As illustrated in Figure 9, the agent-based AAS should act as a model consumer, where AI models developed in a training/modeling environment are deployed to the deployment/operational environment for execution. The (re)design phase encompasses two key steps: (i) the _model producer_ (e.g., experts, systems, or tools) is responsible for developing, training, and validating AI models, and (ii) the _modeling submodel_ handles the conversion and exportation of these models into standardized, machine-readable interchange formats, such as PMML, PFA or ONNX, for inclusion in appropriate submodels (e.g., _AI Dataset_[52], _AI Model Nameplate_[53], and _AI Deployment_[54], which describe the AI lifecycle). On the other hand, the operational phase involves deploying the AI models. During this phase, the _model consumer_, represented by the agent-based AAS, retrieves the AI models described in PMML, PFA, ONNX, or another standardized format from the designated submodels and executes them seamlessly. This decoupling, combined with the use of standardized, machine-readable interchange formats for AI models, facilitates AI development while enabling modularity, interoperability, and seamless sharing of models across various applications and systems. These standardized formats ensure that AI models, developed in specialized environments using appropriate programming languages and tools, can be deployed in environments that might be different from where the models were initially trained, facilitating their integration into diverse operational settings. Moreover, integrating these standardized AI models into AAS submodels offers an I4.0-compliant approach to describing and representing AI within the I4.0 context. Based on that, once an AI model is included in a specific submodel, the agent-based AAS needs to retrieve this model from the proper submodel and consume it using an appropriate scoring engine approach. A scoring engine is a software component that processes input data through an AI model to produce, e.g., predictions or inferences. It is able to interpret the specific structure of an AI model and applies the model to input data in real-time.