###### Abstract
This way, even if the asset comes from a different manufacturer or holds different specifications, it will still be able to use the same communication principles. In the case of an agent, however, this may be partially differentiated since communication is also provided within a closed framework, a so-called multi-agent system. In other words, communication among the agents does not need to be hosted by the agent's AAS, as there is already a message-based interaction provided within the framework which hosts these agents. On the other hand, communication with external software and assets is not predefined within the multi-agent system, and the AAS can provide a way for these entities to interact. On top of that, the AAS allows the description of the agent specifications and capabilities, which are provided in order to fit seamlessly within a complex system architecture. An agent, in general, is an autonomous entity with different capabilities for reacting to external events. The motivation behind multi-agent systems is to achieve decentralized optimization and control of a complex system. As a result, the introduction of the multi-agent system in practice includes a set of different decision-making software that is programmed to react to specific system events. As the system operates, an initial trigger/event may fire the initialization of an agent, which itself may produce one or more events of its own, triggering other agents. This chain reaction of events allows the multi-agent system to produce individual solutions as a subset of a more complex problem. This theoretical implementation of a multi-agent system allows the optimization software to deal with the problem not as a unique optimization block but rather as multiple parts in the pursuit of the overall solution. Nevertheless, the design of the agents, as well as the capabilities that they will provide, is a challenging process. It is necessary that, based on these aspects, the agents will be able to negotiate with each other and solve the problems which they are provided with. Production scheduling and control is one of the many fields where agents and multi-agent systems can be found beneficial. Scheduling or planning problems are usually complex computational problems that require sophisticated algorithms in order to achieve the cross-section of accuracy and rapidness. There is, in fact, a large cluster of different scheduling problems, some of which have already found polynomial-reduced algorithms for solving the problems, while others still remain on the side of NP-hard or NP-complete problems. What may support this aspect, however, is the selection of the correct algorithm or method in order to solve each problem based on the nature of the problem itself. This is what may be referred to as meta-scheduling, indicating the ability of the system to exploit different scheduling methods based on the context of the problem. The case, however, is how the software can classify the different problems and provide a solution using the right algorithms. This question could be alternatively stated as how, within a multi-agent system, where different optimization agents coexist, the scheduling agent capabilities can be expressed in a way that will be accessible/interoperable with other assets or agents and have the intelligence to select the highest probable algorithm for any given problem. As such, scheduling operations may be handled in a more cognitive way, allowing the underlying software to make accurate selections when it comes to optimization while at the same time being able to coexist within the Industry 4.0 paradigm supported by the AAS. This research paper focuses on the provision of an AAS-based information model for the description of scheduling agents in a way that will increase interoperability and allow more efficient decision making.