| 1. |
- De Raedt, Luc, 1964-, et al.
(author)
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From Statistical Relational to Neuro-Symbolic Artificial Intelligence
- 2021
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In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. - California : ijcai.org. ; , s. 4943-4950
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Conference paper (peer-reviewed)abstract
- Neural-symbolic and statistical relational artificial intelligence both integrate frameworks for learning with logical reasoning. This survey identifies several parallels across seven different dimensions between these two fields. These cannot only be used to characterize and position neural-symbolic artificial intelligence approaches but also to identify a number of directions for further research.
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| 2. |
- Manhaeve, Robin, et al.
(author)
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Approximate Inference for Neural Probabilistic Logic Programming
- 2021
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In: Proceedings of the 18th International Conference on Principles of Knowledge Representation and Reasoning. - California : International Joint Conferences on Artificial Intelligence Organization. - 9781956792997 ; , s. 475-486
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Conference paper (peer-reviewed)abstract
- DeepProbLog is a neural-symbolic framework that integrates probabilistic logic programming and neural networks.It is realized by providing an interface between the probabilistic logic and the neural networks.Inference in probabilistic neural symbolic methods is hard, since it combines logical theorem proving with probabilistic inference and neural network evaluation.In this work, we make the inference more efficient by extending an approximate inference algorithm from the field of statistical-relational AI. Instead of considering all possible proofs for a certain query, the system searches for the best proof.However, training a DeepProbLog model using approximate inference introduces additional challenges, as the best proof is unknown at the start of training which can lead to convergence towards a local optimum.To be able to apply DeepProbLog on larger tasks, we propose: 1) a method for approximate inference using an A*-like search, called DPLA* 2) an exploration strategy for proving in a neural-symbolic setting, and 3) a parametric heuristic to guide the proof search.We empirically evaluate the performance and scalability of the new approach, and also compare the resulting approach to other neural-symbolic systems.The experiments show that DPLA* achieves a speed up of up to 2-3 orders of magnitude in some cases.
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| 3. |
- Manhaeve, Robin, et al.
(author)
-
Neural probabilistic logic programming in DeepProbLog
- 2021
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In: Artificial Intelligence. - : Elsevier. - 0004-3702 .- 1872-7921. ; 298
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Journal article (peer-reviewed)abstract
- We introduce DeepProbLog, a neural probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learning techniques of the underlying probabilistic logic programming language ProbLog can be adapted for the new language. We theoretically and experimentally demonstrate that DeepProbLog supports (i) both symbolic and subsymbolic representations and inference, (ii) program induction, (iii) probabilistic (logic) programming, and (iv)(deep) learning from examples. To the best of our knowledge, this work is the first to propose a framework where general-purpose neural networks and expressive probabilistic-logical modeling and reasoning are integrated in a way that exploits the full expressiveness and strengths of both worlds and can be trained end-to-end based on examples. (C) 2021 Elsevier B.V. All rights reserved.
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