Iterative Learning of Answer Set Programs from Context Dependent Examples

DOI10.1017/S1471068416000351zbMath1379.68074arXiv1608.01946MaRDI QIDQ4593062

Alessandra Russo, Mark M. Law, Krysia Broda

Publication date: 9 November 2017

Published in: Theory and Practice of Logic Programming (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1608.01946

zbMATH Keywords

answer set programmingiterative learningnon-monotonic inductive logic programming

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Logic programming (68N17)

Related Items (14)

Best-effort inductive logic programming via fine-grained cost-based hypothesis generation. The Inspire system at the inductive logic programming competition ⋮ The complexity and generality of learning answer set programs ⋮ Incremental and Iterative Learning of Answer Set Programs from Mutually Distinct Examples ⋮ Lifting symmetry breaking constraints with inductive logic programming ⋮ Learning MAX-SAT from contextual examples for combinatorial optimisation ⋮ Inductive general game playing ⋮ Making sense of sensory input ⋮ A Logic-Based Framework Leveraging Neural Networks for Studying the Evolution of Neurological Disorders ⋮ Reactive Answer Set Programming ⋮ Learning programs by learning from failures ⋮ Learning hierarchical probabilistic logic programs ⋮ Inductive learning of answer set programs for autonomous surgical task planning. Application to a training task for surgeons ⋮ Induction and Exploitation of Subgoal Automata for Reinforcement Learning ⋮ Learning any memory-less discrete semantics for dynamical systems represented by logic programs

Uses Software

• Aleph

Cites Work

• Incremental learning of event definitions with inductive logic programming
• Nonmonotonic abductive inductive learning
• Brave induction: a logical framework for learning from incomplete information
• Meta-interpretive learning: application to grammatical inference
• Learning from interpretation transition
• Inductive Learning of Answer Set Programs
• Learning Through Hypothesis Refinement Using Answer Set Programming
• Learning weak constraints in answer set programming
• Induction from answer sets in nonmonotonic logic programs
• Inductive logic programming
• Inductive Logic Programming

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