The DOI matches its Crossref record; 36 of the 39 references it cites were found in a registry or an index.
Relational Reinforcement Learning
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“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
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“Such a program is most economical when it is factored by object type, with one transition rule shared across all objects of a type, so that few parameters support many predictions and a rule learned from one object transfers to the rest [Diuk et al., 2008, Guestrin et al., 2003, Džeroski et al., 2001].”
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
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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“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
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Citations
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A Comprehensive Survey of Multiagent Reinforcement Learning
Busoniu, L., Babuska, R., & De Schutter, B. (2008) · IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) · doi:10.1109/tsmcc.2007.913919 · Cited by 2,235
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Emergent Tool Use From Multi-Agent Autocurricula
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Residential Demand Response of Thermostatically Controlled Loads Using Batch Reinforcement Learning
Ruelens, F., Claessens, B., Vandael, S., Schutter, B. D., Babuška, R., & Belmans, R. J. M. (2016) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2016.2517211 · Cited by 361
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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