Inductive logic programming
PulseAugur coverage of Inductive logic programming — every cluster mentioning Inductive logic programming across labs, papers, and developer communities, ranked by signal.
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CPMpy library translates constraint models across solvers
Researchers have developed CPMpy, an open-source library designed to translate high-level constraint satisfaction and optimization models into various lower-level formalisms. This framework allows users to express probl…
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DeepProbLog combines deep learning and logic programming for medical diagnostics
Researchers have developed a novel neuro-symbolic approach called DeepProbLog for diagnostic reasoning, particularly in medical applications where data privacy is a concern. This method integrates deep learning with pro…
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Unofficial FastLAS 2.2.0 Tutorial Released for Inductive Logic Programming
This document serves as an unofficial programmer's guide to FastLAS 2.2.0, a system designed for Inductive Logic Programming (ILP). It offers a hands-on introduction to writing FastLAS programs, starting with syntax and…
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New ILP Pipeline Explains Weather Forecasts with Interpretable Hypotheses
Researchers have developed a new pipeline using Inductive Logic Programming (ILP) to interpret weather bulletins from OSMER FVG, the meteorological observatory for Italy's Friuli Venezia-Giulia region. This system, base…
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New logic-based method optimizes energy costs in project scheduling
Researchers have developed novel approaches to tackle the Resource-Constrained Project Scheduling Problem (RCPSP) when incorporating time-of-use energy tariffs and machine states. The proposed methods include a monolith…
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New neuro-symbolic frameworks boost AI learning efficiency and weak supervision · 2 sources tracked
Researchers have developed a Native Differentiable Virtual Machine (NDVM) that efficiently handles neuro-symbolic learning by differentiating executable programs without compiling each into a separate graph. This approa…
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New research explores advanced RL for agent survival, navigation, and explainability · 7 sources tracked
Researchers are exploring advanced techniques in reinforcement learning (RL) to enhance agent performance and interpretability. One study introduces programmatic policies (PERL) as an alternative to neural policies (NER…
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Neurosymbolic AI generates novel drug candidates
Researchers have developed a novel neurosymbolic model called Symbolic Neural Generators (SNGs) that combines Inductive Logic Programming with large language models. These SNGs learn from a small set of data instances t…
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New framework formalizes neural network circuit interpretation
Researchers have developed a formal framework to advance mechanistic interpretability in neural networks. This approach treats circuit interpretation as inductive theory construction, creating a shared representation fo…
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New ANDRE framework enhances AI's rule extraction from noisy data
Researchers have introduced ANDRE, a novel framework for Inductive Logic Programming (ILP) that addresses the limitations of existing methods in handling noisy and probabilistic data. ANDRE utilizes attention-based logi…