# Comparison of Multi-Agent Platform Usability for Industrial-Grade Applications
## 2 Related Works  
Although, thus far, AOSE has _not_ been widely adopted in industrial-grade projects, some notable examples can be traced back to the early 2000s [13]. For instance, in the military domain, MAS-based solutions were employed in the simulation and modelling of military training. A prominent example is the Human Variability in Computer Generated Forces (HV-CGF) project [14], funded by the UK Ministry of Defence. This project utilised the JACK agent platform [15] to simulate changes in military personnel behaviour, with JACK agents modelling combatants' beliefs, desires, and intentions. The agents' behaviour was analysed in relation to various drug moderators, such as caffeine. Today, Australian company AOS, the creators of JACK, remains one of the few that openly offers commercial MAS-based solutions. It should be noted that although other companies also continue to offer agent-based solutions, they do not state this information explicitly. In a different context, SCA Packaging [16] developed an agent-based simulation model where agents represented entities such as plant managers and customers, allowing the study of interactions and relationships between order patterns and plant resource capabilities. In the transportation sector, Magenta Technology introduced the agent-based i-Scheduler [17] for vessel scheduling tasks, employing a formal knowledge model for ship scheduling based on Magenta's proprietary multi-agent technology. Additionally, Whitestein Technologies developed the Living Systems Adaptive Transportation Network (LS/ATN) [18], a system designed to optimise costs in transport logistics. In this system, agents represented vehicle drivers, who provided the system with their planned routes, enabling the system to assess the feasibility of additional loads. Although initially marketed with a focus on agent-related features, over time, Whitestein's products have gradually moved away from emphasising these aspects. Energy-related simulations have also benefited from MAS applications. Notably, NuTech Solutions developed the Supply Chain Production Optimizer [19], a system that tailored oil production processes according to factors such as client demand, energy pricing, and weather conditions. In the financial sector, Acklin B.V. introduced the KIR system [20], which automated the exchange of insurance information through agent communication. In all these cases, proprietary agent software was utilised. Among more recent industrial-grade MAS applications, projects by the company Actoron [21] stand out. Actoron, the primary developer of the Jadex agent platform [22], delivered a commercial agent-based control system for a German waste incineration plant in 2017. They also developed a Belief-Desire-Intention (BDI)-based customer-centred digital assistance system for The Allstate Corporation. In 2019, Actoron collaborated with Huawei to create an agent-based scheduling solution designed to mitigate disruptions in production chains. In contemporary MAS applications, the Simudyne platform [23] is employed by Barclays for modelling risk assessment and management [24]. Similarly, AnyLogic's solutions [25] are used by companies such as Walmart for simulations and modelling, particularly in verifying automated tools that support online warehouse operations. Despite these examples, the overall industrial interest in AOSE solutions has declined. A much greater number of applications for agent-based systems have been found in the scientific domain, where agent-based solutions have been employed to study social interactions [26; 27], logistics [28], distributed resource management [29], or planning problems [30]. A number of these practical applications have been presented at conferences, including the International Conference on Autonomous Agents and Multiagent Systems (AAMASs) [31] and the International Workshop on Engineering Multi-Agent Systems (EMASs) [32]. However, these conferences have not upheld the forefront emphasis on AOSE.