Differentiable Inductive Logic Programming for Structured Examples

Shindo, Hikaru; Nishino, Masami; Yamamoto, Akihiro

doi:10.48550/arxiv.2103.01719

Cited by 2 publications

(2 citation statements)

References 25 publications

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“…Furthermore, ∂ILP only allows for two atoms per rule and predicates of arity of at most two. Shindo et al [671] improve on ∂ILP by introducing several new algorithms that deal with more complex programs including function symbols. For both frameworks scalability remains an issue.…”

Section: Inductive Logic Programmingmentioning

confidence: 99%

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

Wörmann¹,

Bogdoll²,

Bührle³

et al. 2022

Preprint

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The existence of representative datasets is a prerequisite of many successful artificial intelligence and machine learning models. However, the subsequent application of these models often involves scenarios that are inadequately represented in the data used for training. The reasons for this are manifold and range from time and cost constraints to ethical considerations. As a consequence, the reliable use of these models, especially in safety-critical applications, is a huge challenge. Leveraging additional, already existing sources of knowledge is key to overcome the limitations of purely data-driven approaches, and eventually to increase the generalization capability of these models. Furthermore, predictions that conform with knowledge are crucial for making trustworthy and safe decisions even in underrepresented scenarios. This work provides an overview of existing techniques and methods in the literature that combine data-based models with existing knowledge. The identified approaches are structured according to the categories integration, extraction and conformity. Special attention is given to applications in the field of autonomous driving.Acknowledgement: The research leading to these results is funded by the German Federal Ministry for Economic Affairs and Climate Action within the project "KI Wissen -Entwicklung von Methoden für die Einbindung von Wissen in maschinelles Lernen". The authors would like to thank the consortium for the successful cooperation.

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Section: Inductive Logic Programmingmentioning

confidence: 99%

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

Wörmann¹,

Bogdoll²,

Bührle³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Moving away from neural networks but attempting to harness gradient descent, we have approaches such as 𝛿-ILP [24] and derivations [65] which implement t-norms to create di erentiable logic programs to nd a suitable hypothesis in an ILP setting. More recently, by directly modelling rule membership of atoms as learnable weights, Neural Logic Networks [53,52] provide a competitive di erentiable ILP system that leverages gradient descent.…”

Section: -22mentioning

confidence: 99%

pix2rule: End-to-end Neuro-symbolic Rule Learning

Cingillioglu¹,

Russo²

2021

Preprint

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Humans have the ability to seamlessly combine low-level visual input with high-level symbolic reasoning often in the form of recognising objects, learning relations between them and applying rules. Neurosymbolic systems aim to bring a unifying approach to connectionist and logic-based principles for visual processing and abstract reasoning respectively. This paper presents a complete neuro-symbolic method for processing images into objects, learning relations and logical rules in an end-to-end fashion. The main contribution is a di erentiable layer in a deep learning architecture from which symbolic relations and rules can be extracted by pruning and thresholding. We evaluate our model using two datasets: subgraph isomorphism task for symbolic rule learning and an image classi cation domain with compound relations for learning objects, relations and rules. We demonstrate that our model scales beyond state-of-the-art symbolic learners and outperforms deep relational neural network architectures.

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Differentiable Inductive Logic Programming for Structured Examples

Cited by 2 publications

References 25 publications

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

Knowledge Augmented Machine Learning with Applications in Autonomous Driving: A Survey

pix2rule: End-to-end Neuro-symbolic Rule Learning

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