ILP: Just Do It

David Page

Dept. of Biostatistics and Medical Informatics
and Dept. of Computer Sciences
University of Wisconsin
1300 University Ave., Rm 5795 Medical Sciences
Madison, WI 53706
U.S.A.
page@biostat.wisc.edu



Abstract. Inductive logic programming (ILP) is built on a foundation
laid by research in other areas of computational logic. But in spite of this
strong foundation, at 10 years of age ILP now faces a number of new 
challenges brought on by exciting application opportunities. The purpose of
this paper is to interest researchers from other areas of computational
logic in contributing their special skill sets to help JLP meet these 
challenges. The paper presents five future research directions for LLP and
points to initial approaches or results where they exist. It is hoped that
the paper will motivate researchers from throughout computational logic
to invest some time into doing ILP.
References

[1]	D. Becker, T. Sterling, D. Savarese, E. Dorband, U. Ranawake, and C. Packer.
Beowuif: A parallel workstation for scientific computation. In Proceedings of the
1995 International Conference on Parallel Processing (ICPP), pages 1114, 1995.
[2]	M. Botta, A. Giordana, L. Saiita, and M. Sebag. Relational learning: Hard 
problems and phase transitions. 2000.
[3]	M. Craven and J. Kumlien. Constructing biological knowledge bases by 
extracting information from text sources. In Proceedings of the Seventh International
Conference on Intelligent Systems for Molecular Biology, Heidelberg, Germany,
1999. AAAI Press.
[4]	M. Craven and S. Slattery. Combining statistical and relational methods for 
learning in hypertext domains. In Proceedings of the Eighth International Conference
on Inductive Logic Programming (ILP-98), pages 3852. Springer Verlag, 1998.
[5]	J. Cussens. Loglinear models for first-order probabilistic reasoning. In 
Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence. Stockholm,
Sweden, 1999.
[6]	S. Dzeroski. Inductive logic programming and knowledge discovery in databases.
In U. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy, editors, 
Advances in Knowledge Discovery and Data Mining. 1996.
[7]	p. Finn, S. Muggleton, D. Page, and A. Srinivasan. Discovery of pharmacophores
using Inductive Logic Programming. Machine Learning, 30:241270, 1998.
[8]	N. Friedman, L. Getoor, D. Koller, and A. Pfeffer. Learning probabilistic relational
models. In Proceedings of the 16th International Joint Conference on Artificial
Intelligence. Stockholm, Sweden, 1999.
[9]	N. Friedman, D. Koller, and A. Pfeffer. Structured representation of complex
stochastic systems. In Proceedings of the 15th National Conference on Artificial
Intelligence. AAAI Press, 1999.
[10]	A. M. Frisch and C. D. Page. Building theories into instantiation. In Proceedings
of the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI-
95), 1995.
[11]	A. Giordana and L. Saitta. Phase transitions in learning with fol languages.
Technical Report 97, 1998.
[12]	G. Gottlob. Subsumption and implication. Information Processing Letters,
24(2):109111, 1987.
[13]	J. Graham, D. Page, and A. Wild. Parallel inductive logic programming. In
Proceedings of the Systems, Man, and Cybernetics Conference (SMC-2000), page
To appear. IEEE, 2000.
[14]	D. Heckerman. A tutorial on learning with bayesian networks. Microsoft Technical
Report MSR-TR-95-06, 1995.
[15] D. Heckerman, E. Horvitz, and B. Nathwani. Toward normative expert systems:
Part i the pathfinder project. Methods of Information in Medicine, 31:90105,
1992.
[16]	R. King, S. Muggleton, R. Lewis, and M. Sternberg. Drug design by machine
learning: The use of inductive logic programming to model the structure-activity
relationships of trimethoprim analogues binding to dihydrofolate reductase. 
Proceedings of the National Academy of Sciences, 89(23):1132211326, 1992.
[17]	R. King, S. Muggleton, A. Srinivasan, and M. Sternberg. Structure-activity 
relationships derived by machine learning: the use of atoms and their bond 
connectives to predict mutagenicity by inductive logic programming. Proceedings of the
National Academy of Sciences, 93:438442, 1996.
[18]	C. Lee. A completeness theorem and a computer program for finding theorems
derivable from given axioms. PhD thesis, University of California, Berkeley, 1967.
[19]	T. Leung, M. Burl, and P. Perona. Probabilistic affine invariants for recognition.
In Proceedings IEEE Conference on Computer Vision and Pattern Recognition,
1998.
[20]	T. Leung and J. Malik. Detecting, localizing and grouping repeated scene elements
from images. IEEE Transactions on Pattern Analysis and Machine Intelligence,
page To appear, 2000.
[21]	M. Litzkow, M. Livny, and M. Mutka. Condora hunter of idle workstations. In
Proceedings of the International Conference on Distributed Computing Systems,
pages 104111, 1988.
[22]	J. Marcinkowski and L. Pachoiski. Undecidability of the horn-clause implication
problem. In Proceedings of the 33rd IEEE Annual Symposium on Foundations of
Computer Science, pages 354362. IEEE, 1992.
[23] 	T.M. Mitchell. The need for biases in learning generalizations. Technical Report
CBM-TR-117, Department of Computer Science, Rutgers University, 1980.
[24] 	T.M. Mitchell. Generalisation as search. Artificial Intelligence, 18:203226, 1982.
[25] 	S. Muggleton. Predicate invention and utilization. Journal of Experimental and
Theoretical Artificial Intelligence, 6(1): 127130, 1994.
[26]	S. Muggleton. Inverse entailment and Progol. New Generation Computing,
13:245286, 1995.
[27]	S. Muggleton. Learning stochastic logic programs. In Proceedings of the AAAI2000
Workshop on Learning Statistical Models from Relational Data. AAAI, 2000.
[28]	S. Muggleton and W. Buntine. Machine invention of first-order predicates by
inverting resolution. In Proceedings of the Fifth International Conference on 
Machine Learning, pages 339352. Kaufmann, 1988.
[29]	S. Muggleton, R. King, and M. Sternberg. Protein secondary structure prediction
using logic-based machine learning. Protein Engineering, 5(7):647657, 1992.
[30] L. Ngo and P. Haddawy. Probabilistic logic programming and bayesian networks.
Algorithms, Concurrency, and Knowledge: LNCS 1023, pages 286300, 1995.
[31]	L. Ngo and P. Haddawy. Answering queries from context-sensitive probabilistic
knowledge bases. Theoretical Computer Science, 171:147177, 1997.
[32]	M. Sebag and C. Rouveirol. Tractable induction and classification in fol. In
Proceedings of the 15th International Joint Conference on Artificial Intelligence,
pages 888892. Nagoya, Japan, 1997.
[33]	B. Selman, H. Kautz, and B. Cohen. Noise strategies for improving local search.
In Proceedings of the Twelfth National Conference on Artificial Intelligence. AAAI
Press, 1994.
[34]	B. Selman, H. Levesque, and D. Mitchell. A new method for solving hard 
satisfiability problems. In Proceedings of the Tenth National Conference on Artificial
Intelligence, pages 440446. AAAI Press, 1992.
[35]	A. Srinivasan and R.C. Camacho. Numerical reasoning with an ILP system 
capable of lazy evaluation and customised search. Journal of Logic Programming,
40:185214, 1999.
[36]	A. Srinivasan, S. Muggleton, R. King, and M. Sternberg. Theories for 
mutagenicity: a study of first-order and feature based induction. Artificial Intelligence,
85(1,2):277299, 1996.
[37]	M. Thrcotte, S. Muggleton, and M. Sternberg. Application of inductive logic
programming to discover rules governing the three-dimensional topology of protein
structures. In Proceedings of the Eighth International Conference on Inductive
Logic Programming (ILP-98), pages 5364. Springer Verlag, 1998.
[38]	Y. Wang and D. Skillicorn. Parallel inductive logic for data mining.
http://www.cs.queensu.ca/home/skill/papers.html#datamining, 2000.
[39]	R. Wirth and P. ORorke. Constraints on predicate invention. In Proceedings of
the 8th International Workshop on Machine Learning, pages 457461. Kaufmann,
1991.
[40]	J. Zelle and R. Mooney. Learning semantic grammars with constructive 
inductive logic programming. In Proceedings of the Eleventh National Conference on
Artificial Intelligence, pages 817822, San Mateo, CA, 1993. Morgan Kaufmann.
