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Relational Reinforcement Learning
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Relational Reinforcement approaches for learning robot strategies are based on the use of ground instances and do not involve predicate invention to extend the relational vocabulary provided by the user.
“However, unlike the approach for learning recursive robot strategies described in Sect. 5.1 the Relational Reinforcement approaches are based on the use of ground instances and do not involve predicate invention to extend the relational vocabulary provided by the user.”
From Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited (2015) · cites this paper as Džeroski et al. 2001
The Introduction describes states as sets of ground facts, and §5.2 specifies relational states built from basic facts. Section 4.4 says the user can influence learning through supplied background predicates and declarative bias. The paper studies blocks-world planning, however, and the passages do not explicitly establish that the approach never invents predicates or that it learns robot strategies. (LLM, conf=0.84)
Experimental — accuracy not yet measuredOrganizing the program by type ties many predictions to few rules, and it lets a rule learned from one object apply to every object of its type.
“Organizing the program by type ties many predictions to few rules, and it lets a rule learned from one object apply to every object of its type [Diuk et al., 2008, Džeroski et al., 2001, Battaglia et al., 2018].”
From OPINE-World: Programmatic World Modeling with Ontology-error-Prioritized Interactive Exploration for ARC-AGI-3 (2026) · cites this paper as Džeroski et al., 2001
The Introduction says relational variables let learned results transfer between different blocks, and §6.3.2 describes short learned policies that use variables rather than block identities. But §4.3 describes types as restrictions on valid clauses, not as the mechanism tying predictions to few rules; §6.3.2 also reports that some learned policies fail to generalize to states with more blocks. The claim’s guarantee that a rule applies to every object of its type is therefore stronger than the paper supports. No page numbers are available in our copy. (LLM, conf=0.85)
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Metadata
- Authors
- Džeroski, S., De Raedt, L., & Driessens, K.
- Affiliations
- Jožef Stefan Institute (SI)
- KU Leuven (BE)
- Venue
- Machine Learning
- Published
- 2001-04
- Volume
- 43
- Issue
- 1-2
- Pages
- 7-52
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W3020831056
- Cited by
- 371
- References
- 39
- Citations by year
- 2025: 2 · 2024: 6 · 2023: 6 · 2022: 10 · 2021: 13 · 2020: 23 · 2019: 9 · 2018: 8 · 2017: 8 · 2016: 12 · 2015: 7 · 2014: 12 · 2013: 18 · 2012: 28
- FWCI
- 13.78
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Machine Learning (publishedVersion)
- Research Publications (Maastricht University) (publishedVersion, other-oa)
- Lirias (submittedVersion, other-oa)
- Research Publications (Maastricht University) (submittedVersion, other-oa)
- Funders
- Vlaamse regering
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
- Keywords
- relational reinforcement learning, relational learning, inductive logic programming, blocks world, planning, structural representation, transfer learning
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