Top-down induction of first-order logical decision trees
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Mode declarations are a popular language bias.
“Mode declarations (Muggleton 1995) are a popular language bias (Blockeel and De Raedt 1998; Srinivasan 2001; Ray 2009; Corapi et al. 2010, 2011; Athakravi et al. 2013; Ahlgren and Yuen 2013; Law et al. 2014).”
From Learning programs by learning from failures (2021) · cites this paper as Blockeel and De Raedt 1998
Section 4 (page unavailable) describes TILDE’s user-specified, PROGOL-like rmode specifications, which constrain allowable query refinements through variable modes and types. This supports the use of mode-based declarations as a language bias, but the paper does not establish that they are popular. (LLM, conf=0.78)
Experimental — accuracy not yet measuredMany ILP systems are not guaranteed to learn optimal solutions, where optimal typically means the smallest program or the program with the minimal description length.
“Many ILP systems (Muggleton 1995; Srinivasan 2001; Blockeel and De Raedt 1998; Ray 2009) are not guaranteed to learn optimal solutions, where optimal typically means the smallest program or the program with the minimal description length.”
From Learning programs by learning from failures (2021) · cites this paper as Blockeel and De Raedt 1998
Section 4 describes TILDE as selecting the best split at each node using a heuristic, while Section 5 compares the compactness of its theories with those of other ILP systems. This is consistent with the absence of a guarantee that TILDE finds the smallest theory, but the paper does not establish that many ILP systems lack such a guarantee or define optimality as the smallest program or minimum description length. Page numbers are unavailable in our copy. (LLM, conf=0.78)
Experimental — accuracy not yet measuredMany ILP systems allow a user to constrain the hypothesis space though clause constraints.
“Many ILP systems allow a user to constrain the hypothesis space though clause constraints (Muggleton 1995; Srinivasan 2001; Blockeel and De Raedt 1998; Ahlgren and Yuen 2013; Law et al. 2014).”
From Learning programs by learning from failures (2021) · cites this paper as Blockeel and De Raedt 1998
Section 4 says TILDE users specify which conjunctions may be added to queries, their maximum frequency, and variable modes and types. This supports user-imposed constraints on TILDE’s hypothesis search, but the passage does not establish that many ILP systems offer clause constraints specifically. No page number is available in our copy. (LLM, conf=0.78)
Experimental — accuracy not yet measuredLFI 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 Blockeel & De Raedt, 1998
The Introduction and Section 2 identify the setting as learning from interpretations and describe each example as a Prolog program encoding its properties, supporting the claim about LFI examples. Neither passage establishes that LFI is the second most popular learning setting. (LLM, conf=0.88)
Experimental — accuracy not yet measuredTILDE is a first-order generalisation of decision trees and uses information gain to divide and conquer the training examples.
“TILDE is a first-order generalisation of decision trees and uses information gain to divide and conquer the training examples.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Blockeel & De Raedt, 1998
The Introduction and Section 4 describe TILDE as a first-order upgrade of C4.5 that uses top-down, divide-and-conquer decision-tree induction. Section 4 specifies gain ratio, rather than information gain, as TILDE’s split criterion. Page numbers are unavailable in our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measured
TILDE learns normal logic programs (which include negation) instead of definite logic programs.
“An interesting aspect of TILDE is that it learns normal logic programs (which include negation) instead of definite logic programs.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Blockeel & De Raedt, 1998
Section 4 says TILDE induces first-order logical decision trees, not logic programs directly. Section 3 says those trees can always be converted to layered normal logic programs, and the paper defines normal programs as permitting negative literals. The claim captures the role of negation but misstates the form TILDE learns. (LLM, conf=0.84)
Experimental — accuracy not yet measuredMany popular ILP systems, such as FOIL, Progol, TILDE, and Aleph struggle to learn recursive programs.
“Indeed, many popular ILP systems, such as FOIL (Quinlan 1990), Progol (Muggleton 1995), TILDE (Blockeel and De Raedt 1998), and Aleph (Srinivasan 2001) struggle to learn recursive programs.”
From Learning programs by learning from failures (2021) · cites this paper as Blockeel and De Raedt 1998
Top-down algorithms start with a general hypothesis and then specialise it.
“Top-down algorithms, (Quinlan, 1990; Blockeel & De Raedt, 1998; Bratko, 1999; Muggleton et al., 2008) start with a general hypothesis and then specialise it.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Blockeel & De Raedt, 1998
TILDE is an instance of top-down methodology.
“TILDE learns from interpretations, instead of entailment as Aleph, and is an instance of top-down methodology.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Blockeel & De Raedt, 1998
TILDE learns from interpretations.
“TILDE learns from interpretations, instead of entailment as Aleph, and is an instance of top-down methodology.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as Blockeel & De Raedt, 1998
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Cites
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439 · see citing works
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Research works printed but not linked: 23
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- Authors
- Blockeel, H., & De Raedt, L.
- Affiliations
- KU Leuven (BE)
- Corresponding author
- Blockeel, Hendrik
- ORCID
- Blockeel, Hendrik
- Venue
- Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 1998-05
- Volume
- 101
- Issue
- 1-2
- Pages
- 285-297
- ISSN
- 0004-3702 (ISSN-L)
- OpenAlex
- W1700914952
- Cited by
- 685
- References
- 1
- Citations by year
- 2026: 6 · 2025: 8 · 2024: 9 · 2023: 21 · 2022: 25 · 2021: 23 · 2020: 23 · 2019: 21 · 2018: 18 · 2017: 34 · 2016: 36 · 2015: 31 · 2014: 33 · 2013: 35 · 2012: 41
- FWCI
- 22.03
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Artificial Intelligence (publishedVersion)
- Lirias (acceptedVersion, other-oa)
- Lirias (submittedVersion, other-oa)
- Lirias (KU Leuven) (submittedVersion)
- Funders
- Vlaamse regering
- 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
- top-down induction, first-order logic, inductive logic programming, decision lists, predicate invention, expressivity
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