Natlog: a Lightweight Logic Programming Language with a Neuro-symbolic Touch

Tarau, Paul

doi:10.4204/eptcs.345.27

Cited by 8 publications

(8 citation statements)

References 17 publications

(43 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The second important feature of the developed architecture is that the Actor Prolog language is a translator to Java (Morozov et al, 2015) that enables one to obtain a high-performance code and simplifies the connection of the software system with the OpenCV library. Note that only a few neural-symbolic systems in the world are based on professional programming languages/libraries like Python (Mojarad et al, 2020, Tarau, 2021, Li et al, 2022, Winters et al, 2022, Ciatto et al, 2022, Kotlin (Ciatto et al, 2021), PyTorch (Manhaeve et al, 2018, Yang et al, 2020, TensorFlow (Li et al, 2022, Ciatto et al, 2022, and Keras (Mojarad et al, 2020). Most neural-symbolic systems are applied only for solving toy problems and only a few neural-symbolic systems applied modern neural architectures like YOLO (Khan et al, 2019) and VGG16 (Padalkar et al, 2023).…”

Section: The Architecture Of the System For The Logical Analysis Of V...mentioning

confidence: 99%

Investigation and Development of Methods and Algorithms for Analyzing Video-Eeg Monitoring of Delayed Cerebral Ischemia After Subarachnoid Haemorrhage

Morozov

Sushkova

Синкин

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. This work aims to study and develop methods and tools for analyzing video-EEG monitoring of delayed cerebral ischemia after subarachnoid haemorrhage. A study was made of methods and algorithms for processing video images, which are supposed to be used to recognize life events and medical care for a patient, which can lead to artefacts in the patient’s EEG records. An approach to video monitoring of the patient was proposed and tested on real video monitoring data using object-oriented logic programming methods in combination with neural network methods for image analysis.

show abstract

Section: The Architecture Of the System For The Logical Analysis Of V...mentioning

confidence: 99%

Investigation and Development of Methods and Algorithms for Analyzing Video-Eeg Monitoring of Delayed Cerebral Ischemia After Subarachnoid Haemorrhage

Morozov

Sushkova

Синкин

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…To make our research goals and related language design proposals experimentally testable, we have embedded in Python a lightweight Prolog-like language, Natlog. We refer to [9] for a description, in an early, proof-of-concept version, of its concrete syntactic and semantic features, while we will focus here on advanced use cases based on a fresh "from-scratch" reimplementation.…”

Section: Logic Languages As Prompt-generators and Large Language Mode...mentioning

confidence: 99%

“…Our Natlog system has been originally introduced in [9], to which we refer to for syntax, semantics and low level implementation details. It is currently evolving as a fresh implementation 2 , and it will be used as a testbed for the key ideas of this paper.…”

Section: Logic Languages As Prompt-generators and Large Language Mode...mentioning

confidence: 99%

Natlog: Embedding Logic Programming into the Python Deep-Learning Ecosystem

Tarau

2023

Electron. Proc. Theor. Comput. Sci.

Self Cite

View full text Add to dashboard Cite

Driven by expressiveness commonalities of Python and our Python-based embedded logic-based language Natlog, we design high-level interaction patterns between equivalent language constructs and data types on the two sides.By directly connecting generators and backtracking, nested tuples and terms, coroutines and firstclass logic engines, reflection and meta-interpretation, we enable logic-based language constructs to access the full power of the Python ecosystem.We show the effectiveness of our design via Natlog apps working as orchestrators for JAX and Pytorch pipelines and as DCG-driven GPT3 and DALL.E prompt generators.Keyphrases: embedding of logic programming in the Python ecosystem, high-level inter-paradigm data exchanges, coroutining with logic engines, logic-based neuro-symbolic computing, logic grammars as prompt-generators for Large Language Models, logic-based neural network configuration and training.

show abstract

“…2020;Lamb et al . 2020;Tarau 2021) where LP and neural networks are combined or integrated in several ways following the purpose of engineering more generally intelligent systems capable of coupling the inferential capabilities of LP with the flexible pattern-matching capabilities of neural networks.…”

Section: Artificial Intelligencementioning

confidence: 99%

Fifty Years of Prolog and Beyond

Körner

Leuschel

Barbosa³

et al. 2022

Theory and Practice of Logic Programming

View full text Add to dashboard Cite

Both logic programming in general and Prolog in particular have a long and fascinating history, intermingled with that of many disciplines they inherited from or catalyzed. A large body of research has been gathered over the last 50 years, supported by many Prolog implementations. Many implementations are still actively developed, while new ones keep appearing. Often, the features added by different systems were motivated by the interdisciplinary needs of programmers and implementors, yielding systems that, while sharing the “classic” core language, in particular, the main aspects of the ISO-Prolog standard, also depart from each other in other aspects. This obviously poses challenges for code portability. The field has also inspired many related, but quite different languages that have created their own communities. This article aims at integrating and applying the main lessons learned in the process of evolution of Prolog. It is structured into three major parts. First, we overview the evolution of Prolog systems and the community approximately up to the ISO standard, considering both the main historic developments and the motivations behind several Prolog implementations, as well as other logic programming languages influenced by Prolog. Then, we discuss the Prolog implementations that are most active after the appearance of the standard: their visions, goals, commonalities, and incompatibilities. Finally, we perform a SWOT analysis in order to better identify the potential of Prolog and propose future directions along with which Prolog might continue to add useful features, interfaces, libraries, and tools, while at the same time improving compatibility between implementations.

show abstract

Natlog: a Lightweight Logic Programming Language with a Neuro-symbolic Touch

Cited by 8 publications

References 17 publications

Investigation and Development of Methods and Algorithms for Analyzing Video-Eeg Monitoring of Delayed Cerebral Ischemia After Subarachnoid Haemorrhage

Investigation and Development of Methods and Algorithms for Analyzing Video-Eeg Monitoring of Delayed Cerebral Ischemia After Subarachnoid Haemorrhage

Natlog: Embedding Logic Programming into the Python Deep-Learning Ecosystem

Fifty Years of Prolog and Beyond

Contact Info

Product

Resources

About