Neuro-Symbolic Hybrid Systems for Industry 4.0: A Systematic Mapping Study

Sittón, Inés; Alonso, Ricardo S.; Hernández-Nieves, Elena; González, Sara Rodríguez; Rivas, Alberto

doi:10.1007/978-3-030-21451-7_39

Cited by 8 publications

(3 citation statements)

References 60 publications

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“…Recall that the space of possible latent concept values Z is given as background knowledge. As H is in the language of ASP, we use NeurASP [Yang et al, 2020] to optimise a semantic loss function [Xu et al, 2018] for neural network training. Informally, for each training data point, the neural network is trained to predict the latent concept values that result in the target label, given H and B.…”

Section: Neural and Symbolic Componentsmentioning

confidence: 99%

“…Within Artificial Intelligence (AI), one of the ultimate goals is to assist humans in complex decision making, a key challenge in multiple industries such as healthcare, automated maintenance, and security [Lyn Paul et al, 2019;Sittón et al, 2019;Han et al, 2021]. Neuro-Symbolic AI aims to address this challenge by combining the best features of both deep learning and symbolic reasoning techniques [d'Avila Garcez et al, 2019;De Raedt et al, 2020].…”

Section: Introductionmentioning

confidence: 99%

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Neuro-Symbolic Learning of Answer Set Programs from Raw Data

Cunnington

Law²,

Lobo

et al. 2023

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence

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One of the ultimate goals of Artificial Intelligence is to assist humans in complex decision making. A promising direction for achieving this goal is Neuro-Symbolic AI, which aims to combine the interpretability of symbolic techniques with the ability of deep learning to learn from raw data. However, most current approaches require manually engineered symbolic knowledge, and where end-to-end training is considered, such approaches are either restricted to learning definite programs, or are restricted to training binary neural networks. In this paper, we introduce Neuro-Symbolic Inductive Learner (NSIL), an approach that trains a general neural network to extract latent concepts from raw data, whilst learning symbolic knowledge that maps latent concepts to target labels. The novelty of our approach is a method for biasing the learning of symbolic knowledge, based on the in-training performance of both neural and symbolic components. We evaluate NSIL on three problem domains of different complexity, including an NP-complete problem. Our results demonstrate that NSIL learns expressive knowledge, solves computationally complex problems, and achieves state-of-the-art performance in terms of accuracy and data efficiency. Code and technical appendix: https://github.com/DanCunnington/NSIL

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Section: Neural and Symbolic Componentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neuro-Symbolic Learning of Answer Set Programs from Raw Data

Cunnington

Law²,

Lobo

et al. 2023

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence

View full text Add to dashboard Cite

show abstract

“…The Internet of Things implies the connection of different heterogeneous objects, including buildings, machinery, vehicles, and electronic devices, such as sensors and actuators interconnected by means of communication protocols and forming wireless or wired networks [18] to collect information and extract knowledge [19]. Since then, the scope of IoT has spread throughout a great variety of environments and disciplines, including solutions for development of Smart Cities [20,21], Industry 4.0 [22][23][24], transportation and logistics [25,26], smart homes and hotels [13,27,28] or, more relevant to this research, energy efficiency [8,29,30]. IoT provides multiple solutions to each of its application areas.…”

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confidence: 99%

A Review on Edge Computing in Smart Energy by means of a Systematic Mapping Study

et al. 2019

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Context: Smart Energy is a disruptive concept that has led to the emergence of new energy policies, technology projects, and business models. The development of those models is driven by world capitals, companies, and universities. Their purpose is to make the electric power system more efficient through distributed energy generation/storage, smart meter installation, or reduction of consumption/implementation costs. This work approaches Smart Energy as a paradigm that is concerned with systemic strategies involving the implementation of innovative technological developments in energy systems. However, many of the challenges encountered under this paradigm are yet to be overcome, such as the effective integration of solutions within Smart Energy systems. Edge Computing is included in this new technology group. Objective: To investigate developments that involve the use of Edge Computing and that provide solutions to Smart Energy problems. The research work will be developed using the methodology of systematic mapping of literature, following the guidelines established by Kitchenham and Petersen that facilitate the identification of studies published on the subject. Results: Inclusion and exclusion criteria have been applied to identify the relevant articles. We selected 80 papers that were classified according to the type of publication (journal, conferences, or book chapter), type of research (conceptual, experience, or validation), type of activity (implement, validate, analyze) and asset (architecture, framework, method, or models). Conclusion: A complete review has been conducted of the 80 articles that were closely related to the questions posed in this research. To reach the goal of building Edge Computing architectures for Smart Energy environments, several lines of research have been defined. In the future, such architectures will overcome current problems, becoming highly energy-efficient, cost-effective, and capacitated to process and respond in real-time.Electronics 2020, 9, 48 2 of 33 Ledger Technologies (such as blockchain) [15]; or distributed sensing and actuation technologies such as the Internet of Things (IoT) [3,16].Regarding the Internet of Things (IoT), its recognition as a paradigm dates back to 2011-2012, when renowned companies and institutions such as Gartner, Forbes, or Wired started using this term to refer to the emerging technology [17]. The Internet of Things implies the connection of different heterogeneous objects, including buildings, machinery, vehicles, and electronic devices, such as sensors and actuators interconnected by means of communication protocols and forming wireless or wired networks [18] to collect information and extract knowledge [19]. Since then, the scope of IoT has spread throughout a great variety of environments and disciplines, including solutions for development of Smart Cities [20,21], Industry 4.0 [22][23][24], transportation and logistics [25,26], smart homes and hotels [13,27,28] or, more relevant to this research, energy efficiency [8,29,30]. IoT provid...

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Deep Symbolic Learning and Semantics for an Explainable and Ethical Artificial Intelligence

Alonso

2020

Advances in Intelligent Systems and Computing

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Neuro-Symbolic Hybrid Systems for Industry 4.0: A Systematic Mapping Study

Cited by 8 publications

References 60 publications

Neuro-Symbolic Learning of Answer Set Programs from Raw Data

Neuro-Symbolic Learning of Answer Set Programs from Raw Data

A Review on Edge Computing in Smart Energy by means of a Systematic Mapping Study

Deep Symbolic Learning and Semantics for an Explainable and Ethical Artificial Intelligence

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