Iterative Learning of Answer Set Programs from Context Dependent Examples
Open accessAbstract
In recent years, several frameworks and systems have been proposed that extend Inductive Logic Programming (ILP) to the Answer Set Programming (ASP) paradigm. In ILP, examples must all be explained by a hypothesis together with a given background knowledge. In existing systems, the background knowledge is the same for all examples; however, examples may be context-dependent. This means that some examples should be explained in the context of some information, whereas others should be explained in different contexts. In this paper, we capture this notion and present a context-dependent extension of the Learning from Ordered Answer Sets framework. In this extension, contexts can be used to further structure the background knowledge. We then propose a new iterative algorithm, ILASP2i, which exploits this feature to scale up the existing ILASP2 system to learning tasks with large numbers of examples. We demonstrate the gain in scalability by applying both algorithms to various learning tasks. Our results show that, compared to ILASP2, the newly proposed ILASP2i system can be two orders of magnitude faster and use two orders of magnitude less memory, whilst preserving the same average accuracy.
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One approach to mitigate the grounding problem is to use context-dependent examples, where BK can be associated with specific examples so that an ILP system need only ground part of the BK.
“One approach to mitigate this problem is to use context-dependent examples (Law et al., 2016), where BK can be associated with specific examples so that an ILP systems need only ground part of the BK.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Law et al., 2016
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Cites
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Learning programs by learning from failures
Cropper, A., & Morel, R. (2021) · Machine Learning · doi:10.1007/s10994-020-05934-z · Cited by 86 · see citing works
Inductive Logic Programming At 30: A New Introduction
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- Authors
- LAW, M., RUSSO, A., & BRODA, K.
- Affiliations
- Imperial College London (GB)
- Venue
- Theory and Practice of Logic Programming
- Publisher
- Cambridge University Press (CUP)
- Published
- 2016-09
- Volume
- 16
- Issue
- 5-6
- Pages
- 834-848
- ISSN
- 1471-0684 (ISSN-L)
- 1475-3081
- OpenAlex
- W2498573650
- Cited by
- 8
- References
- 12
- Citations by year
- 2024: 1 · 2022: 1 · 2021: 1 · 2020: 2 · 2019: 1 · 2018: 2
- FWCI
- 0.82
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Theory and Practice of Logic Programming (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Spiral (Imperial College London) (submittedVersion)
- arXiv (Cornell University)
- 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 › Information Systems › Data Mining Algorithms and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Keywords
- answer set programming, inductive logic programming, iterative learning algorithm, scalability, prior knowledge
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