Inductive Learning of Answer Set Programs
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ASP-encoded ILP systems can often learn optimal and recursive programs.
“Systems that implement this approach can often learn optimal and recursive programs and can harness state-of-the-art ASP solvers, but often struggle with scalability, especially in terms of the problem domain size.”
From Learning programs by learning from failures (2021) · cites this paper as Law et al. 2014
ASP-encoded ILP systems often struggle with scalability, especially in terms of the problem domain size.
“Systems that implement this approach can often learn optimal and recursive programs and can harness state-of-the-art ASP solvers, but often struggle with scalability, especially in terms of the problem domain size.”
From Learning programs by learning from failures (2021) · cites this paper as Law et al. 2014
ILASP3's constraint loop requires at most | E | iterations, where | E | is the number of ILASP examples, which are partial interpretations.
“ILASP3's constraint loop requires at most | E | iterations, where | E | is the number of ILASP examples, which are partial interpretations.”
From Learning programs by learning from failures (2021) · cites this paper as Law et al. 2014
ILASP can represent real numbers as strings and delegate the reasoning to Python via Clingo's scripting feature.
“For instance, ILASP (Law et al. 2014) can represent real numbers as strings and delegate the reasoning to Python via Clingo's scripting feature (Gebser et al. 2014).”
From Learning programs by learning from failures (2021) · cites this paper as Law et al. 2014
Because ILASP3's examples are partial interpretations, it is possible to represent multiple atomic examples in a single partial interpretation example.
“Because ILASP3's examples are partial interpretations (Law et al. 2014), it is possible to represent multiple atomic examples in a single partial interpretation example.”
From Learning programs by learning from failures (2021) · cites this paper as Law et al. 2014
Each learning task in this paper can be represented as a single ILASP positive example.
“In fact, each learning task in this paper can be represented as a single ILASP positive example (Law et al. 2014).”
From Learning programs by learning from failures (2021) · cites this paper as Law et al. 2014
Other recent ILP approaches also employ defining an ASP problem where an answer set corresponds to a definite program.
“The idea is to define an ASP problem where an answer set (a model) corresponds to a definite program, an approach also employed by other recent ILP approaches (Corapi et al. 2011; Law et al. 2014; Kaminski et al. 2018; Schüller and Benz 2018).”
From Learning programs by learning from failures (2021) · cites this paper as Law et al. 2014
Meta-level approaches often delegate the search for a hypothesis to an off-the-shelf solver after which the meta-level solution is translated back to a standard solution for the ILP problem.
“Meta-level approaches often delegate the search for a hypothesis to an off-the-shelf solver (Corapi et al., 2011; Muggleton et al., 2014; Law et al., 2014; Kaminski et al., 2018; Evans et al., 2021; Cropper & Morel, 2021) after which the meta-level solution is translated back to a standard solution for the ILP problem.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Law et al., 2014
Other recent work focuses on learning from transitions and learning from answer sets.
“Other recent work focuses on learning from transitions (Inoue et al., 2014; Evans et al., 2021; Ribeiro et al., 2020) and learning from answer sets (Law et al., 2014).”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Law et al., 2014
LFI is the second most popular learning setting, where an example is an interpretation (a set of facts).
“The second most popular (De Raedt & Dehaspe, 1997; Blockeel & De Raedt, 1998; Law et al., 2014) learning setting is LFI where an example is an interpretation, i.e. a set of facts.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Law et al., 2014
Some ILP systems can learn unstratified ASP programs.
“By contrast, some systems can learn unstratified ASP programs (Law et al., 2014).”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Law et al., 2014
There are many benefits to learning ASP programs.
“There are many benefits to learning ASP programs (Otero, 2001; Law et al., 2014).”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Law et al., 2014
ASPAL, ILASP, HEXMIL, and the Apperception Engine translate an ILP problem into an ASP problem and use powerful ASP solvers to find a model of the problem.
“ASPAL (Corapi et al., 2011), ILASP (Law et al., 2014), HEXMIL (Kaminski et al., 2018), and the Apperception Engine (Evans et al., 2021) translate an ILP problem into an ASP problem and use powerful ASP solvers to find a model of the problem - note that these systems all employ very different algorithms.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Law et al., 2014
Meta-level approaches can often learn optimal and recursive programs.
“Meta-level approaches can often learn optimal and recursive programs.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Law et al., 2014
Many meta-level approaches precompute every possible rule in a hypothesis.
“Many approaches also precompute every possible rule in a hypothesis (Corapi et al., 2011; Law et al., 2014), so struggle to learn programs with large rules, although preliminary work tries to address this issue (Cropper & Dumanˇ ci´ c, 2020).”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Law et al., 2014
Meta-level approaches can learn recursive programs.
“The main advantage of meta-level approaches is that they can learn recursive programs and optimal programs (Corapi et al., 2011; Law et al., 2014; Kaminski et al., 2018; Evans & Grefenstette, 2018; Evans et al., 2021; Cropper & Morel, 2021).”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Law et al., 2014
Newer systems address the limitation of not guaranteeing smallest programs through meta-level reasoning.
“Newer systems address this limitation (Corapi et al., 2011; Law et al., 2014; Cropper & Muggleton, 2016; Kaminski et al., 2018; Cropper & Morel, 2021) through meta-level reasoning (Section 4.5).”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Law et al., 2014
Most ILP approaches use an Occamist bias to find the smallest hypothesis, measured in terms of the number of clauses, literals, or description length.
“Most approaches use an Occamist bias to find the smallest hypothesis, measured in terms of the number of clauses (Muggleton et al., 2015), literals (Law et al., 2014), or description length (Muggleton, 1995).”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Law et al., 2014
Meta-level approaches can learn optimal programs.
“The main advantage of meta-level approaches is that they can learn recursive programs and optimal programs (Corapi et al., 2011; Law et al., 2014; Kaminski et al., 2018; Evans & Grefenstette, 2018; Evans et al., 2021; Cropper & Morel, 2021).”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Law et al., 2014
Many meta-level approaches encode an ILP problem as a single (often very large) ASP problem.
“A key issue is that many approaches encode an ILP problem as a single (often very large) ASP problem (Corapi et al., 2011; Law et al., 2014; Kaminski et al., 2018; Evans et al., 2021), so struggle to scale to problems with very large domains.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Law et al., 2014
ILASP can represent real numbers as strings and can delegate reasoning to Python via Clingo's scripting feature.
“For instance, ILASP (Law et al., 2014) can represent real numbers as strings and can delegate reasoning to Python (via Clingo's scripting feature).”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Law et al., 2014
The placeholder approach to predicate invention requires a user to manually specify the arity and argument types of a symbol.
“However, this placeholder approach is limited because it requires that a user manually specify the arity and argument types of a symbol (Law et al., 2014), which rather defeats the point, or requires generating all possible invented predicates (Evans & Grefenstette, 2018; Evans et al., 2021), which is computationally expensive.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Law et al., 2014
Law et al. (2014) uses ILASP to induce the rules for Sudoku and show that this more expressive formalism allows for game rules to be expressed more compactly.
“Law et al. (2014) uses ILASP to induce the rules for Sudoku and show that this more expressive formalism allows for game rules to be expressed more compactly.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Law et al. (2014)
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“To give a clear overview of Popper, Table 1 compares Popper to Aleph (Srinivasan 2001), a classical ILP system, and Metagol (Cropper and Muggleton 2016), ILASP3 (Law 2018), and ∂ILP (Evans and Grefenstette 2018), three state-of-the-art ILP systems based on Prolog, ASP, and neural networks respectively.”
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“Such querying is not possible with standard SAT and ASP approaches.”
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Tarzariol, A., Gebser, M., & Schekotihin, K. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/284 · Cited by 5 · see citing works
Search Space Expansion for Efficient Incremental Inductive Logic Programming from Streamed Data
Law, M., Broda, K. B., & Russo, A. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/374 · Cited by 5 · see citing works
Self-citationMachine Comprehension of Text Using Combinatory Categorial Grammar and Answer Set Programs.
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Self-citationLearning formulas in finite variable logics
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Learning logic programs by explaining their failures
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Learning Reward Machines in Cooperative Multi-agent Tasks
Ardon, L., Furelos-Blanco, D., & Russo, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-56255-6_3 · Cited by 4 · see citing works
Self-citationTowards Explainable Weather Forecasting Through FastLAS
Dreossi, T., Agostino Dovier, A., Formisano, A., Law, M., Manzato, A., Russo, A., & Tait, M. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-74209-5_20 · Cited by 4 · see citing works
Self-citationAn Abductive-Inductive Algorithm for Probabilistic Inductive Logic Programming.
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Self-citationLearning Logic Programs by Combining Programs
Cropper, A., & Hocquette, C. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230309 · Cited by 4 · see citing works
Learning Assumption-Based Argumentation Frameworks
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Using an ASG Based Generative Policy to Model Human Rules
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Self-citationLearning answer set programs with aggregates via sampling and genetic programming
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Differentiable Inductive Logic Programming in High-Dimensional Space
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Lifting symmetry breaking constraints with inductive logic programming
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Generative Policies for Coalition Systems - A Symbolic Learning Framework
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Self-citationLearning to Break Symmetries for Efficient Optimization in Answer Set Programming
Tarzariol, A., Gebser, M., Schekotihin, K., & Law, M. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i5.25804 · Cited by 3 · see citing works
Self-citationEvaluating the Apperception EnginePreprint
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NSL: Hybrid Interpretable Learning From Noisy Raw DataPreprint
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Self-citationLearning Commonsense Knowledge through Interactive DialoguePreprint
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Self-citationWu, M., Yu, F. R., & Liu, P. X. (2024) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2024.3398661 · Cited by 2 · see citing works
Neuro-symbolic Predicate Invention: Learning relational concepts from visual scenes
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Learning possibilistic dynamic systems from state transitions
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Inductive learning of robot task knowledge from raw data and online expert feedback
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Towards Neural-Symbolic Learning to support Human-Agent Operations
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Generalisation through Negation and Predicate Invention
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Learning to Rank the Distinctiveness of Behaviour in Serial Offending
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Self-citationBridging Logic Programming and Deep Learning for Explainability through ILASP
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Inductive Synthesis of Inductive Heap Predicates
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Predicate Renaming via Large Language Models
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Sketched Answer Set Programming
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Proxy Attribute Discovery in Machine Learning Datasets via Inductive Logic Programming
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Forgetting to Learn Logic Programs
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- Authors
- Law, M., Russo, A., & Broda, K.
- Affiliations
- Imperial College London (GB)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2014
- Pages
- 311-325
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W228104253
- Cited by
- 133
- References
- 19
- Citations by year
- 2026: 6 · 2025: 13 · 2024: 14 · 2023: 11 · 2022: 12 · 2021: 23 · 2020: 15 · 2019: 16 · 2018: 11 · 2017: 5 · 2016: 4 · 2015: 2
- FWCI
- 2.16
- Type
- conference paper
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- Spiral (Imperial College London) (submittedVersion)
- Imperial College Research Computing Service Data Repository
- Funders
- Engineering and Physical Sciences Research Council (EP/K033425/1, EP/K033522/1)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Keywords
- answer set programs, inductive logic programming, partial interpretation, soundness and completeness, planning, nonmonotonic reasoning
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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