An inductive logic programming system
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Updated
Dec 13, 2024 - Python
An inductive logic programming system
Metagol - an inductive logic programming system
Awesome Neural Logic and Causality: MLN, NLRL, NLM, etc. 因果推断,神经逻辑,强人工智能逻辑推理前沿领域。
Mechanistically interpretable neurosymbolic AI (Nature Comput Sci 2024): losslessly compressing NNs to computer code and discovering new algorithms which generalize out-of-distribution and outperform human-designed algorithms
cplint is a suite of programs for reasoning with probabilistic logic programs
Symbolic Reinforcement Learning using Inductive Logic Programming
A largely incomplete but hopefully useful list of links to datasets for relational learning and inductive logic programming. No guarantees on availability.
(Explainable AI) - Learning Non-Monotonic Logic Programs From Statistical Models Using High-Utility Itemset Mining
Top-down induction of first-order logical decision trees: master AI thesis implementation
C. Hocquette and A. Cropper, Learning programs with magic values.
C. Hocquette and A. Cropper, Relational program synthesis with numerical reasoning, AAAI23.
bddem is a SWI-Prolog pack for using Binary Decision Diagrams
A novel Inductive Logic Programming(ILP) system based on Meta Inverse Entailment in Python.
Code for the papers "Induction of Subgoal Automata for Reinforcement Learning" (AAAI-20) and "Induction and Exploitation of Subgoal Automata for Reinforcement Learning" (JAIR).
Source code related to the ILP 2019 paper 'LazyBum: Decision tree learning using lazy propositionalization'
BotGNN: Inclusion of Domain-Knowledge into GNNs using Mode-Directed Inverse Entailment
Symbolic function approximator aims to generate a function using a genetic algorithm to approximate a data distribution using the symbolic paradigm with programming logic.
Vertex-Enriched Graph Neural Network (VEGNN)
C. Hocquette, A. Niskanen, M Järvisalo, and Andrew Cropper, Learning MDL Logic Programs From Noisy Data, AAAI24.
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