All paper's cited in Stephen Muggleton's Publications
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- S.H. Muggleton
A. Fidjeland, W. Luk.
Scalable
acceleration of inductive logic programs.
In IEEE international conference on field-programmable technology,
pages 252 -- 259. IEEE, 2002.
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- M. A-Razzak, T. Hassan, and
R. Pettipher.
Extran-7: A Fortran-based software package for building expert systems.
In M.A. Bramer, editor, Research and Development in Expert
Systems, pages 23-30. Cambridge University Press, Cambridge, 1984.
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- H. Abramson and
V. Dahl.
Logic Grammars.
Springer-Verlag, Berlin, 1989.
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- Harvey Abramson.
Definite clause translation grammars.
Technical report, Vancouver, BC, Canada, Canada, 1984.
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- H. Ade, L. De Raedt, and
M. Bruynooghe.
Theory revision.
In S. Muggleton, editor, Proceedings of the 3rd International Workshop on
Inductive Logic Programming, pages 179-192, 1993.
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- R. Agrawal.
Sample mean based index policies with o(log n) regret for the multi-armed
bandit problem.
Advanced Applied Probability, 27:1054-1078, 1995.
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- L. Ai, S.H. Muggleton,
C. Hocquette, M. Gromowski, and U. Schmid.
Beneficial
and harmful explanatory machine learning.
Machine Learning, 110:695-721, 2021.
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- L. Ai, J. Langer, S.H.
Muggleton, and U. Schmid.
Explanatory
machine learning for sequential human teaching.
Machine Learning, 112:3591-3632, 2023.
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- James S Aitken.
Learning information extraction rules: An inductive logic programming approach.
In ECAI, pages 355-359, 2002.
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- K. Ali and M. Pazzani.
Hydra : a noise tolerant relational concept-learning algorithm.
In Proceedings of the 13th International Joint Conference on Artificial
Intelligence. Morgan Kaufmann, 1993.
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- B.P. Allen.
Case-based reasoning: business applications.
Communications of the ACM, 37(3):40-44, 1994.
- [12]
- J.F. Allen.
Natural language understanding.
Benjamin/Cummings, Menlo Park, CA, 1995.
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- E. Alpaydin.
Intoduction to Machine Learning.
MIT Press, 2004.
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- H. Alshawi.
The Core Language Engine.
M.I.T.Press, 1992.
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- A.P. Ambler, H.G.
Barrow, C.M. Brown, R.M. Burstall, and R. J. Popplestone.
A versatile system for computer controlled assembly.
Artificial Intelligence, 6(2):129-156, 1975.
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- A. Amini, H. Lodhi
S.H. Muggleton, and M.J.E. Sternberg.
A novel
logic-based approach for quantitative toxicology prediction.
Journal of Chemical Information and Modelling, 47(3):998-1006,
2007.
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- A. Amini, P.J.
Shrimpton, S.H. Muggleton, and M.J.E. Sternberg.
A general
approach for developing system-specific functions to score protein-ligand
docked complexes using support vector inductive logic programming.
Proteins, 69(4):823-831, 2007.
DOI: 10.1002/prot.21782.
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- B. Andres, B. Kaufmann,
O. Matheis, and T. Schaub.
Unsatisfiability-based optimization in clasp.
In Proceedings of the 28th International Conference on Logic
Programming, 2012.
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- E. Andrianantoandro, S. Basu, D. Karig, and R. Weiss.
Synthetic biology: new engineering rules for an emerging discipline.
Molecular Systems Biology, 2(2006:0028), 2006.
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- N. Angelopoulos and J. Cussens.
Markov chain Monte Carlo using tree-based priors on model structure.
In UAI-2001, Los Altos, CA, 2001. Kaufmann.
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- N. Angelopoulos and S.H. Muggleton.
Machine
learning metabolic pathway descriptions using a probabilistic relational
representation.
Electronic Transactions in Artificial Intelligence, 6, 2002.
- [22]
- D. Angluin and C.H.
Smith.
A survey of inductive inference: theory and methods.
Computing Surveys, 15(3):237-269, 1983.
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- D. Angluin.
On the complexity of minimum inference of regular sets.
Information and Control, 39:337-350, 1978.
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- D. Angluin.
Inference of reversible languages.
Journal of the ACM, 29:741-765, 1982.
- [25]
- D. Angluin.
A note on the number of queries needed to identify regular languages.
Information and Control, 51(1):76-87, 1982.
- [26]
- D. Angluin.
Queries and concept learning.
Machine Learning, 2(4):319-342, 1988.
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- Martin HG Anthony and Norman Biggs.
Computational learning theory.
1997.
- [28]
- C. Apté, F.J.
Damerau, and S.M. Weiss.
Automated learning of decision rules for text categorization.
ACM Trans on Information Systems, 12:233-251, 1994.
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- B. Arbab and D. Michie.
Generating rules from examples.
In IJCAI-85, pages 631-633, Los Altos, CA, 1985. Kaufmann.
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- Brenna D Argall,
Sonia Chernova, Manuela Veloso, and Brett Browning.
A survey of robot learning from demonstration.
Robotics and autonomous systems, 57(5):469-483, 2009.
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- J. Arima.
Preduction: a common form of induction and analogy.
In IJCAI-97, pages 23-29. Morgan Kaufmann, 1997.
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- J. Arima.
Logical Foundations of Induction and Analogy.
PhD thesis, Kyoto University, 1998.
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- M. Arita and
T. Nishioka.
Hierarchical classification of chemical reactions.
Bio Industry, 17(7):45-50, 2000.
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- R. Armstrong,
D. Freitag, T. Joachims, and T. Mitchell.
Webwatcher: a learning apprentice for the World Wide Web.
In AAAI Spring symposium on Information Gathering from Heterogeneous,
Distributed Environments, Stanford, 1995.
http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-6/web-agent/www/project-home.html.
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- A. Arvanitis,
S.H. Muggleton, J. Chen, and H. Watanabe.
Abduction with stochastic logic programs based on a possible worlds semantics.
In Short Paper Proceedings of the 16th International Conference on
Inductive Logic Programming. University of Corunna, 2006.
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- F. Baganz,
A. Hayes, D. Marren, D.C.J. Gardner, and S.G. Oliver.
Evaluation of replacement markers for functional analysis studies in
Saccharomyces cerevisiae.
Yeast, 13:1563-1573, 1997.
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- M. Bain and
S.H. Muggleton.
Non-monotonic
learning.
In D. Michie, editor, Machine Intelligence 12, pages 105-120.
Oxford University Press, 1991.
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- M. Bain.
Specification of attributes for computer induction.
TIRM, The Turing Institute, Glasgow, 1987.
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- M. Bain.
Experiments in non-monotonic first-order induction.
TIRM, The Turing Institute, Glasgow, 1990.
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- M. Bain.
Machine-learned rule-based control.
In J. McGhee, editor, Knowledge-Based Systems for Industrial
Control, pages 222-243. London, 1990.
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Experiments in non-monotonic learning.
In Proceedings of the Eighth International Workshop on Machine
Learning, pages 380-384, San Mateo, CA, 1991. Morgan Kaufmann.
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- R.B. Banerji.
Learning in the limit in a growing language.
In IJCAI-87, pages 280-282, Los Angeles, CA, 1987. Kaufmann.
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- R.B. Banerji.
Learning theoretical terms.
In S.H. Muggleton, editor, Inductive Logic Programming. Academic
Press, London, 1992.
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Crockford, E. Holmes, F. Pazos, M.J.E. Sternberg, S.H. Muggleton, and J.K.
Nicholson.
Integrative
top-down system metabolic modeling in experimental disease states via
data-driven bayesian methods.
Journal of Proteome Research, 7(2):497-503, 2008.
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- H. Bannai, Y. Tamada,
O. Maruyama, and S. Miyano.
Hypothesiscreator:
Concepts for accelerating the computational knowledge discovery process.
Electronic Transactions in Artificial Intelligence,
6-B1(019):73-83, November 2001.
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and L. Saitta.
Learning mutually dependent relations.
Journal of Intelligent Information Systems, 2, 1992.
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- H.G. Barrow and
S.H. Salter.
Design of low-cost equipment for cognitive robot research.
In B. Meltzer and D. Michie, editors, Machine Intelligence 5,
pages 555-566. Edinburgh University Press, 1969.
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and J.M. Tenenbaum.
Interpreting line drawings as three-dimensional surfaces.
Artificial Intelligence, 17:75-116, 1981.
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Theoretical models of learning to learn.
In T. Mitchell and S. Thrun, editors, Learning to Learn. Kluwer,
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Ben-David and Reba Schuller.
Exploiting task relatedness for multiple task learning.
In Learning Theory and Kernel Machines, pages 567-580. Springer,
2003.
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On the theory of average case complexity.
Journal of Information and System Sciences, 44:193-219, 1992.
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J. Louradour, , R. Collobert, R., and J. Weston.
Curriculum learning, 2009.
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Logical depth and physical complexity.
In R. Herken, editor, The Universal Turing Machine A Half Century
Survey, pages 227-257. Kammerer and Unverzagt, Hamburg, 1988.
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- F. Bergadano and D. Gunetti.
An interactive system to learn functional logic programs.
In Proceedings of the 13th International Joint Conference on Artficial
Intelligence. Morgan Kaufmann, 1993.
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S. Ponsero.
Integrating empirical and analytic learning in concept acquisition.
In Proceedings of the International Symposium on Methodologies for
Intelligent Systems: Lecture Notes in Artificial Intelligence.
Springer-Verlag, 1989.
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A. Giordana, and L Saitta.
Concept acquisition in noisy environments.
IEEE Transactions on Pattern Analysis and Machine Intelligence,
10:555-578, 1988.
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, and S. Ponsero.
Deduction in top-down inductive learning.
In Proceedings of the Sixth International Workshop on Machine
Learning, Los Altos, CA, 1989. Kaufmann.
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Towards an inductive logic programming language.
Technical Report ESPRIT project no. 6020 ILP Deliverable TO1, Computer Science
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Statistical Decision Theory and Bayesian Analysis.
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Mathematical go endgames: nightmares for the professional go
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Ishi Press International, 1994.
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Winning ways for your mathematical plays, volume 1.
A K Peters/CRC Press, London, 2001.
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Winning ways for your mathematical plays, volume 2.
A K Peters/CRC Press, London, 2001.
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Blockbusting and domineering.
Journal of Combinatorial Theory Series A, 49(1), 1988.
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and A.F.M Smith.
Bayesian theory.
Wiley, New York, 1994.
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Application of formal methods to biological regulatory networks: extending
thomas' asynchronous logical approach with temporal logic.
Journal of heoretical Biology, 229:339-347, 2004.
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Value dividends, the Harsanyi set and extensions, and the proportional
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International Journal of Game Theory, pages 1-23, 2020.
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Feldman.
On the synthesis of finite-state machines from samples of their behaviour.
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R. Krishnaswamy.
Constructing programs from example computations.
IEEE Transactions on Software Engineering, 2(3), 1976.
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The inference of regular LISP programs from examples.
IEEE Transactions on Systems, Man and Cybernetics, 8(8):585-600,
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Fundamental mechanisms in machine learning and inductive inference.
In W. Bibel and P. Jorrand, editors, Fundamentals of Artificial
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Human-like computing: Report of a workshop held on 17 & 18 february 2016,
bristol, uk.
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Published in Les Prix Nobel 1988, printed in Sweden by Norstedts
Tryckeri, Stockholm, Sweden.
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and L. De Raedt.
Lookahead and discretisation in ILP.
In N. Lavrac and S. Dzeroski, editors, Proceedings of the Seventh
International Workshop on Inductive Logic Programming, pages 77-84.
Springer-Verlag, Berlin, 1997.
LNAI 1297.
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Scaling up inductive logic programming by learning from interpretations.
Data Mining and Knowledge Discovery, 3(1):59-93, 1999.
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- Hendrik
Blockeel, Luc Dehaspe, Bart Demoen, Gerda Janssens, Jan Ramon, and Henk
Vandecasteele.
Improving the efficiency of inductive logic programming through the use of
query packs.
Journal of Artificial Intelligence Research, 16(1):135-166,
2002.
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Towards a mathematical theory of inductive inference.
Information and Control, 28:125-155, 1975.
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Lessons from ethology for autonomous agent architectures.
In K. Furukawa, D. Michie, and S.H. Muggleton, editors, Machine
Intelligence 15: intelligent agents. Oxford University Press, Oxford,
1999.
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A. Ehrenfeucht, D. Haussler, and M. Warmuth.
Classifying learnable geometric concepts with the Vapnik-Chervonenkis
dimension.
In Proceedings of the 18th ACM Symposium on Theory of
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A. Ehrenfeucht, D. Haussler, and M.K. Warmuth.
Learnability and the Vapnik-Chervonenkis dimension.
Journal of the ACM, 36(4):929-965, 1989.
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On the necessity of Occam algorithms.
UIUCDCS-R-89-1544, University of Illinois at Urbana-Champaign, 1989.
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M. Stefik.
The LOOPS manual.
Xerox, Palo Alto, CA, 1983.
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M. Sassin.
Dialog-based
learning (dbl) for adaptive interface agents and programming-by-demonstration
systems.
Technical Report CMU-CS-93-175, Carnegie Mellon University, School of Computer
Science, Pittsburgh, PA 15213, July 1993.
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Boffey, D.R. Brooks, S.J. Clark, A.M. Dewar, L.G. Firbank, A.J. Haughton,
C. Hawes, M.S. Heard, M.J. May, et al.
Effects on weed and invertebrate abundance and diversity of herbicide
management in genetically modified herbicide-tolerant winter-sown oilseed
rape.
Proceedings of the Royal Society B: Biological Sciences,
272(1562):463, 2005.
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G. Caron-Lormier, S.H. Muggleton, A. Raybould, and A. Tamaddoni-Nezhad.
Automated
discovery of food webs from ecological data using logic-based machine
learning.
PloS ONE, 6(12), 2011.
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C. Mulder, G. Woodward, A. Tamaddoni-Nezhad, N. Bluthgen, M.J.O Pocock, S.H.
Muggleton, D.M. Evans, J. Astegiano, F. Massol, N. Loeuille, S. Petit, and
S. Macfadyen.
Networking
agroecology: Integrating the diversity of agroecosystem interactions.
In G. Woodward and D.A. Bohan, editors, Advances in Ecological Research,
Vol. 49, pages 2-67. Academic Press, Amsterdam, 2013.
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The Laws of Thought.
MacMillan & Co., London, 1854.
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Specialisation of logic programs by pruning SLD-trees.
In S. Wrobel, editor, Proceedings of the Fourth Inductive Logic
Programming Workshop (ILP94), pages 31-48, Bonn, 1994. GDM-studien
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Induction of logic programs by example-guided unfolding.
Journal of Logic Programming, 40(2-3):159-183, 1999.
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Kristina Berggren, Thomas Elebring, Peter J. Greasley, and Michael
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Scaffold
hopping, synthesis and structure-activity relationships of
5,6-diaryl-pyrazine-2-amide derivatives: A novel series of CB1 receptor
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Bioorganic & Medicinal Chemistry, 15(12):4077 -- 4084,
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Predicate invention and learning from positive examples only.
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Relational learning as search in a critical region.
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Nim, a game with a complete mathematical theory.
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Inductive learning applied to program construction and verification.
In S. Muggleton, editor, Proceedings of the 3rd International Workshop on
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A representation of pattern-knowledge in chess endgames.
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S.H. Muggleton.
Applications of
Inductive Logic Programming.
Communications of the ACM, 38(11):65-70, 1995.
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Muggleton, and A. Varsek.
Learning qualitative
models of dynamic systems.
In Proceedings of the Eighth International Machine Learning
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Muggleton, and A. Karalic.
Applications of Inductive Logic Programming.
In R.S. Michalski, I. Bratko, and M. Kubat, editors, Machine Learning and
Data Mining. John Wiley and Sons Ltd., Chichester, 1998.
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Generating human-understandable decision rules.
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Discovery of abstract concepts by a robot.
In Proceedings of Discovery Science 2010, LNAI 6332, pages
372-379, Berlin, 2010. Springer-Verlag.
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Learning to relate terms in a multiple agent environment.
Technical report, LIACC, Porto, Portugal, 1990.
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Knowledge states and meta-knowledge maintenance.
In I. Bratko and N. Lavrac, editors, Progress in Machine
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Classification and Regression Trees.
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Bagging predictors.
Machine Learning, 24(2):123-140, 1996.
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C. Chothia, T.J. Hubbard, and A.G. Murzin.
Understanding protein structure: using scop for fold interpretation.
Methods in Enzymology, 266:635-643, 1996.
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Learning declarative bias.
In Proceedings of the 17th International Conference on Inductive Logic
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SAGE: a logical agent-based environment monitoring and control
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Logical reasoning in formal and everyday reasoning tasks.
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A robust layerd control system for a mobile robot.
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Closed loop machine
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Combining
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Electronic Transactions in Artificial Intelligence,
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E. Philalithis, and X. Li.
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Induction of horn clauses: methods and the plausible generalization algorithm.
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Induction of Horn-clauses: methods and the plausible generalization
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Generalised subsumption and its applications to induction and redundancy.
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A Theory of Learning Classification Rules.
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Ind package of machine learning algorithms.
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From explanation to synthesis: Compositional program induction for learning
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Abductive
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