A methodology for integrating asset administration shells and multi-agent systems

Industry 4.0 (I4.0) is promoting the digitization of industrial environments towards intelligent and distributed industrial automation systems based on Cyber-physical Systems (CPS). Currently, this digitization process is being leveraged by the Asset Administration Shell (AAS), which digitally describes an asset in a standardized and semantically unambiguous form throughout its lifecycle. However, more robust solutions based on autonomous AASs endowed with collaborative and intelligent capabilities, also called proactive AASs, are still in the early stages. In this context, Multi-agent Systems (MAS) are a key enabler to provide the required autonomy, intelligence and collaborative capabilities for the AASs. With this in mind, this paper presents a methodology positioned with respect to the Reference Architecture Model Industrie 4.0 (RAMI4.0) layers, which provides guidelines for integrating AASs and MAS, aiming to support the development of proactive AASs. The applicability of the proposed methodology was tested through the integration of AASs and MAS for a smallscale CPS demonstrator.The authors are grateful to the Foundation for Science and Technology (FCT), Portugal, for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021). The author Lucas Sakurada thanks the FCT for the PhD Grant 2020.09234.BD.info:eu-repo/semantics/publishedVersio

A group learning management method for intelligent tutoring systems

In this paper we propose a group management specification and execution method that seeks a compromise between simple course design and complex adaptive group interaction. This is achieved through an authoring method that proposes predefined scenarios to the author. These scenarios already include complex learning interaction protocols in which student and group models use and update are automatically included. The method adopts ontologies to represent domain and student models, and object Petri nets to specify the group interaction protocols. During execution, the method is supported by a multi-agent architecture

Coordination approaches and systems - part I : a strategic perspective

This is the first part of a two-part paper presenting a fundamental review and summary of research of design coordination and cooperation technologies. The theme of this review is aimed at the research conducted within the decision management aspect of design coordination. The focus is therefore on the strategies involved in making decisions and how these strategies are used to satisfy design requirements. The paper reviews research within collaborative and coordinated design, project and workflow management, and, task and organization models. The research reviewed has attempted to identify fundamental coordination mechanisms from different domains, however it is concluded that domain independent mechanisms need to be augmented with domain specific mechanisms to facilitate coordination. Part II is a review of design coordination from an operational perspective

A framework for smart production-logistics systems based on CPS and industrial IoT

Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems

Towards engineering ontologies for cognitive profiling of agents on the semantic web

Research shows that most agent-based collaborations suffer from lack of flexibility. This is due to the fact that most agent-based applications assume pre-defined knowledge of agents’ capabilities and/or neglect basic cognitive and interactional requirements in multi-agent collaboration. The highlight of this paper is that it brings cognitive models (inspired from cognitive sciences and HCI) proposing architectural and knowledge-based requirements for agents to structure ontological models for cognitive profiling in order to increase cognitive awareness between themselves, which in turn promotes flexibility, reusability and predictability of agent behavior; thus contributing towards minimizing cognitive overload incurred on humans. The semantic web is used as an action mediating space, where shared knowledge base in the form of ontological models provides affordances for improving cognitive awareness

Agent and cyber-physical system based self-organizing and self-adaptive intelligent shopfloor

The increasing demand of customized production results in huge challenges to the traditional manufacturing systems. In order to allocate resources timely according to the production requirements and to reduce disturbances, a framework for the future intelligent shopfloor is proposed in this paper. The framework consists of three primary models, namely the model of smart machine agent, the self-organizing model, and the self-adaptive model. A cyber-physical system for manufacturing shopfloor based on the multiagent technology is developed to realize the above-mentioned function models. Gray relational analysis and the hierarchy conflict resolution methods were applied to achieve the self-organizing and self-adaptive capabilities, thereby improving the reconfigurability and responsiveness of the shopfloor. A prototype system is developed, which has the adequate flexibility and robustness to configure resources and to deal with disturbances effectively. This research provides a feasible method for designing an autonomous factory with exception-handling capabilities