## 2 Review of Literature and Standards
### Distributed Intelligence Architectures in Manufacturing  
_Distributed artificial intelligence_ (DAI) has emerged as a branch of Artificial Intelligence (AI) over thirty years ago as a powerful paradigm for representing and solving complex problems. The growth of the field has been spurred by the advances in distributed computing environ ments and the widespread connectivity. Representing a confluence of ideas from several disciplines, DAI became an independent research discipline with two main sub-fields: distributed problem solving and multi-agent systems. _Distributed problem solving_ deals with the issues related to solving a problem by dividing it among a number of cooperative problem solvers which share the computational burden and knowledge of their partial solutions. _Multi-agent systems_, on the other hand, are concerned with the behavior of loosely coupled autonomous problem solvers, or agents that work together to solve a problem beyond their individual capabilities. Since the DAI field is closely associated with the notion of agents, it is often referred to as multi-agent system research in literature. An important characteristic of DAI is that it combines computational resources of a group of agents such that the group intelligence is more than the sum of the individual agents' capabilities. Agents made a real break-through two decades ago or so when the emphasis in mainstream AI research shifted from goal-seeking, logic-centered to utility-oriented, rational behavior--from single to multiple, collaborative problem-solving entities. It only mattered that an agent did the \"right thing\", even with bounded computational resources [36]. Accordingly, after making observations, it changed its environment by its actions in a way which served best its own interest. The impacts of actions were qualified, and not the mechanisms that generated them. This pragmatic, albeit theoretically founded concept of AI intensified further research in machine learning (when improving the performance of an agent-based on its accumulated experience), in multi-agent systems (when the environment is populated by other agents, too), and also in robotics (when integrating collaborative human and machine agents) [20]. The take-up of agent technologies was accelerated by the development of powerful multi-agent design methodologies, multi-agent simulation as well as programming environments. The agent-based paradigm of computing was early welcome in manufacturing systems control as well [37], with the promise of such much-sought properties like autonomy, cooperation, responsiveness, redundancy, distributedness, and openness [17]. Indeed, it provided an alternative to the traditional, by design centralized, hierarchical, and rigid planning and control schemes. Its current status, achieved advancements, applicability and future outlook are continuously evolving, due to the fact that today's manufacturing systems are becoming increasingly complex, dynamic, and connected [38]. However, even though MAS as a paradigm seems to be suitable to address many of the current requirements imposed by cyber-physical production systems, it still has a long and difficult path for a wider acceptance in industry [39]. Manufacturing services must comply with strong requirements in terms of reliability, robustness, and latency, and solution providers are expected to ensure that agents will operate within certain boundaries of the production, and mitigate unattended behaviors during the execution of manufacturing activities. The so-called Holonic Manufacturing Systems (HMS) addressed these issues in some way, by introducing autonomous, collaborative entities (named holons) to represent the main aspects of manufacturing in terms of product, resource, order, and staff holons. The Product-Resource-Order-Staff Reference Architecture for HMS (PROSA) gained its name from this concept [40].