Interoperability Between Heterogeneous Multi-agent Systems Recommended by FIPA: Towards a Weakly Coupled Approach Based on a Network of Recurrent Neurons of the LSTM type

Amrani, Noureddine El Abid; Youssfi, Mohamed; Bouattane, Omar; Abra, Oum El Kheir

doi:10.1109/commnet49926.2020.9199643

Cited by 1 publication

(3 citation statements)

References 11 publications

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“…We further note if the approach uses a rule-based (symbolic) or learning-based (subsymbolic) approach, if the approach utilizes goal-based cooperation, and if they consider fault resiliency and resource efficiency. Subsymbolic approaches have increased in popularity recently and we have identified nine papers that show interesting research and approaches, namely [23,28,29,43,58,59,61,62,68]. Not all of these papers mention interoperability explicitly, due to research groups working in separate fields with different goals in mind.…”

Section: Discussionmentioning

confidence: 99%

“…However, the accuracy of a deep learning model is heavily dependent on the quality and quantity of available data, and some models can behave stochastically, unlike rule-based approaches where the failure modes are related to inconsistent or incorrect definitions. Recent works integrating machine-learning techniques for message interoperability in SoS [28] and multi-agent systems [29] demonstrate that there is a developing interest in such techniques, but further work is necessary to make machine learning a viable approach to interoperability.…”

Section: Learning-based Approachmentioning

confidence: 99%

“…Ref. [29] presents a solution for interoperability between a base context defined in the FIPA-ACL language and agent-specific context described with ontologies. The proposed architecture uses neural networks to decode messages into a list of components that are further analysed to decide agent states, for example "Permanent danger".…”

Section: Semantic Interoperabilitymentioning

confidence: 99%

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AI Concepts for System of Systems Dynamic Interoperability

Nilsson,

Javed,

Albertsson

et al. 2024

Sensors

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Interoperability is a central problem in digitization and sos engineering, which concerns the capacity of systems to exchange information and cooperate. The task to dynamically establish interoperability between heterogeneous cps at run-time is a challenging problem. Different aspects of the interoperability problem have been studied in fields such as sos, neural translation, and agent-based systems, but there are no unifying solutions beyond domain-specific standardization efforts. The problem is complicated by the uncertain and variable relations between physical processes and human-centric symbols, which result from, e.g., latent physical degrees of freedom, maintenance, re-configurations, and software updates. Therefore, we surveyed the literature for concepts and methods needed to automatically establish sos with purposeful cps communication, focusing on machine learning and connecting approaches that are not integrated in the present literature. Here, we summarize recent developments relevant to the dynamic interoperability problem, such as representation learning for ontology alignment and inference on heterogeneous linked data; neural networks for transcoding of text and code; concept learning-based reasoning; and emergent communication. We find that there has been a recent interest in deep learning approaches to establishing communication under different assumptions about the environment, language, and nature of the communicating entities. Furthermore, we present examples of architectures and discuss open problems associated with ai-enabled solutions in relation to sos interoperability requirements. Although these developments open new avenues for research, there are still no examples that bridge the concepts necessary to establish dynamic interoperability in complex sos, and realistic testbeds are needed.

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