[1]

R.B. Banerji. Learning in the limit in a growing language. In*IJCAI-87*, pages 280–282, Los Angeles, CA, 1987. Kaufmann.

[2]

C. Bennett. 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.

[3]

I. Bratko.

*Prolog for artificial intelligence*. Addison-Wesley, London, 1986.

MATH[4]

W. Buntine. Generalised subsumption and its applications to induction and redundancy.

*Artificial Intelligence*, 36(2):149–176, 1988.

MATHCrossRefMathSciNet[5]

R. Carnap.

*The Continuum of Inductive Methods*. Chicago University, Chicago, 1952.

MATH[6]

G. Chaitin.

*Information, Randomness and Incompleteness — Papers on Algorithmic Information Theory*. World Scientific Press, Singapore, 1987.

MATH[7]

Dietterich. Limitations of inductive learning. In*Proceedings of the Sixth International Workshop on Machine Learning*, pages 124–128, San Mateo, CA, 1989. Morgan-Kaufmann.

[8]

A. Ehrenfeucht, D. Haussler, M. Kearns, and L. Valiant. A general lower bound on the number of examples needed for learning. In*COLT 88: Proceedings of the Conference on Learning*, pages 110–120, Los Altos, CA, 1988. Morgan-Kaufmann.

[9]

S. Feferman et al., editor.*Gödel’s Collected Works*. Oxford University Press, Oxford, 1980.

[10]

M.S. Fox and J. McDermott. The role of databases in knowledge-based systems. Technical Report CMU-RI-TR-86-3, Carnegie-Mellon University, Robotics Institute, Pittsburgh, PA, 1986.

[11]

A.M. Frisch and C.D. Page. On inductive generalisation with taxonomic back-ground knowledge. Technical report, University of Illinois, Urbana, 1989.

[12]

K. Gödel. Über formal unentscheidbare Sätze der Principia Mathematica und verwandter System I.

*Monats. Math. Phys.*, 32:173–198, 1931.

CrossRef[13]

E.M. Gold. Language identification in the limit.

*Information and Control*, 10:447–474, 1967.

MATHCrossRef[14]

D. Haussler. Quantifying inductive bias: AI learning algorithms and Valiant’s learning framework.

*Artificial intelligence*, 36:177–221, 1988.

MATHCrossRefMathSciNet[15]

J. Hayes-Michie. News from Brainware.*Pragmatica*, 1:10–11, 1990.

[16]

H. Ishizaka. Learning simple deterministic languages. In*Computational learning theory: proceedings of the second annual workshop*, San Mateo, CA, 1989. Kaufmann.

[17]

R.A. Kowalski.*Logic for Problem Solving*. North Holland, 1980.

[18]

B. Kuipers. Qualitative simulation.

*Artificial Intelligence*, 29:289–338, 1986.

MATHCrossRefMathSciNet[19]

X. Ling and M. Dawes Theory reduction with uncertainty: A reason for theoretical terms. Technical Report 271, University of Western Ontario, 1990.

[20]

J.W. Lloyd.

*Foundations of Logic Programming*. Springer-Verlag, Berlin, 1984.

MATH[21]

M.J. Maher. Equivalences of logic programs. In*Proceedings of Third International Conference on Logic Programming*, Berlin, 1986. Springer.

[22]

T.M. Mitchell, R.M. Keller, and S.T. Kedar-Cabelli. Explanation-based generalization: A unifying view.*Machine Learning*, 1(1):47–80, 1986.

[23]

H. Mortimer.

*The Logic of Induction*. Ellis Horwood, Chichester, England, 1988.

MATH[24]

S.H. Muggleton. Duce, an oracle based approach to constructive induction. In*IJCAI-87*, pages 287–292. Kaufmann, 1987.

[25]

S.H. Muggleton. Inverting the resolution principle. In*Machine Intellience 12 (in press)* Oxford University Press, 1988.

[26]

S.H. Muggleton. A strategy for constructing new predicates in first order logic. In*Proceedings of the Third European Working Session on Learning*, pages 123–130. Pitman, 1988.

[27]

S.H. Muggleton.*Inductive Acquisition of Expert Knowledge*. Addision-Wesley, Wokingham, England, 1990.

[28]

S.H. Muggleton and W. Buntine. Machine invention of first-order predicates by inverting resolution. In*Proceedings of the Fifth International Conference on Machine Learning*, pages 339–352. Kaufmann, 1988.

[29]

S.H. Muggleton and C. Feng. Efficient induction of logic programs. In*Proceedings of the First Conference on Algorithmic Learning Theory*, Tokyo, 1990. Ohmsha.

[30]

T. Niblett. A study of generalisation in logic programs. In*EWSL-88*, London, 1988. Pitman.

[31]

G.D. Plotkin.*Automatic Methods of Inductive Inference*. PhD thesis, Edinburgh University, August 1971.

[32]

J.R. Quinlan. Discovering rules from large collections of examples: a case study. In D. Michie, editor,*Expert Systems in the Micro-electronic Age*, pages 168–201. Edinburgh University Press, Edinburgh, 1979.

[33]

J.R. Quinlan. Learning relations: comparison of a symbolic and a connectionist approach. Technical Report 346, University of Sydney, 1989.

[34]

J.A. Robinson. A machine-oriented logic based on the resolution principle.

*JACM*, 12(1):23–41, January 1965.

MATHCrossRef[35]

C. Rouveirol and J-F Puget. A simple and general solution for inverting resolution. In*EWSL-89*, pages 201–210, London, 1989. Pitman.

[36]

C. Sammut and R.B. Banerji. Learning concepts by asking questions. In R. Michalski, J. Carbonnel, and T. Mitchell, editors,*Machine Learning: An Artificial Intelligence Approach. Vol. 2*, pages 167–192. Kaufmann, Los Altos, CA, 1986.

[37]

E.Y. Shapiro.*Algorithmic program debugging*. MIT Press, 1983.

[38]

E.H. Shortliffe and B. Buchanan. A model of inexact reasoning in medicine.

*Mathematical Biosciences*, 23:351–379, 1975.

CrossRefMathSciNet[39]

S. Slocombe, K. Moore, and M. Zelonf. Engineering expert systems applications. In*Proceedings of the Annual Conference of the BCS Specialist Group on Expert Systems*. British Computer Society, London, 1986.

[40]

R.J. Solomonoff. A formal theory of inductive inference.*J. Comput. Sys.*, 7:376–388, 1964.

[41]

L. Sterling and E. Shapiro.

*The art of Prolog: advanced programming techniques*. MIT-Press, Cambridge, MA, 1986.

MATH[42]

A. Turing. Systems of logic based on ordinals.*Proceedings of the London Mathematical Society*, pages 161–228, 1939.

[43]

A. Turing. The automatic computing engine.*Lecture to the London Mathematical Society*, 1947.

[44]

R. Wirth. Completing logic programs by inverse resolution. In*EWSL-89*, pages 239–250, London, 1989, Pitman.