Learning Through Hypothesis Refinement Using Answer Set Programming
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Publication:2943878
DOI10.1007/978-3-662-44923-3_3zbMath1319.68166OpenAlexW178618149MaRDI QIDQ2943878
Domenico Corapi, Alessandra Russo, Duangtida Athakravi, Krysia Broda
Publication date: 7 September 2015
Published in: Inductive Logic Programming (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/978-3-662-44923-3_3
Related Items (8)
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 ⋮ Online learning of event definitions ⋮ Iterative Learning of Answer Set Programs from Context Dependent Examples ⋮ Top program construction and reduction for polynomial time meta-interpretive learning ⋮ Learning programs by learning from failures ⋮ Incremental learning of event definitions with inductive logic programming
Cites Work
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- Incremental learning of event definitions with inductive logic programming
- Nonmonotonic abductive inductive learning
- Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited
- Brave induction: a logical framework for learning from incomplete information
- INDUCTIVE LOGIC PROGRAMMING AS ABDUCTIVE SEARCH
- Normative design using inductive learning
- Induction on Failure: Learning Connected Horn Theories
- Inductive Logic Programming: Theory and methods
- Knowledge Representation, Reasoning and Declarative Problem Solving
- Induction from answer sets in nonmonotonic logic programs
- The DLV system for knowledge representation and reasoning
- TopLog: ILP Using a Logic Program Declarative Bias
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