An inductive logic programming system
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Oct 25, 2026 - Python
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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。
neural-network logic first-order-logic markov logic-programming causality causal-inference causal inductive-logic-programming
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
program-synthesis knowledge-distillation inductive-logic-programming domain-adaptation explainable-ai interpretable distilling neurosymbolic model-distillation out-of-distribution-generalization mechanistic-interpretability
cplint is a suite of programs for reasoning with probabilistic logic programs
machine-learning prolog probabilistic-programming logic-programming inductive-logic-programming probabilistic-logic-programming
Symbolic Reinforcement Learning using Inductive Logic Programming
reinforcement-learning artificial-intelligence ilp answer-set-programming inductive-logic-programming symbolic-reinforcement-learning
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
machine-learning data-mining inductive-logic-programming explainable-ai explainable-ml rule-induction
📏 Logic rule learning for computer networking
Top-down induction of first-order logical decision trees: master AI thesis implementation
A novel Inductive Logic Programming(ILP) system based on Meta Inverse Entailment in Python.
machine-learning artificial-intelligence logic-programming automl automated-machine-learning inductive-logic-programming inference-engine automated-data-science explainable-ai explainable-ml abductive-learning abductive-commonsense-reasoning inverse-entailment pygol
C. Hocquette and A. Cropper, Learning programs with magic values.
C. Hocquette and A. Cropper, Relational program synthesis with numerical reasoning, AAAI23.
Code for the papers "Induction of Subgoal Automata for Reinforcement Learning" (AAAI-20) and "Induction and Exploitation of Subgoal Automata for Reinforcement Learning" (JAIR).
bddem is a SWI-Prolog pack for using Binary Decision Diagrams
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
inductive-logic-programming neuro-symbolic-learning deep-relational-learning graph-neural-networks background-knowledge
C. Hocquette, A. Niskanen, M Järvisalo, and Andrew Cropper, Learning MDL Logic Programs From Noisy Data, AAAI24.
Symbolic function approximator aims to generate a function using a genetic algorithm to approximate a data distribution using the symbolic paradigm with programming logic.
python genetic-algorithm function symbolic symbolic-regression inductive-logic-programming approximator
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