Definition
One hypothesis is more general than another one if it covers all instances that are also covered by the latter one. The former hypothesis is called a generalization of the latter one, and the latter a specialization of the former. When using logical formulae as hypotheses, the generality relation coincides with the notion of logical entailment, which implies that the generality relation can be analyzed from a logical perspective. The logical analysis of generality, which is pursued in this chapter, leads to the perspective of induction as the inverse of deduction. This forms the basis for an analysis of various logical frameworks for reasoning about generality and for traversing the space of possible hypotheses. Many of these frameworks (such as for instance, θ-subsumption) are employed in the field of inductive logic...
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Recommended Reading
Buntine W (1998) Generalized subsumption and its application to induction and redundancy. Artif Intell 36:375–399
De Raedt L (1997) Logical settings for concept learning. Artif Intell 95:187–201
De Raedt L (2008) Logical and relational learning. Springer, New York
Flach PA (1994) Simply logical: intelligent reasoning by example. Wiley, New York
Michalski RS (1983) A theory and methodology of inductive learning. Artif Intell 20(2):111–161
Muggleton S (1987) Duce, an oracle based approach to constructive induction. In: Proceedings of the 10th international joint conference on artificial intelligence. Morgan Kaufmann, San Francisco, pp 287–292
Muggleton S (1995) Inverse entailment and Progol. New Gener Comput 13(3–4):245–286
Muggleton S, Buntine W (1988) Machine invention of first order predicates by inverting resolution. In: Proceedings of the 5th international workshop on machine learning. Morgan Kaufmann, San Francisco, pp 339–351
Muggleton S, De Raedt L (1994) Inductive logic programming: theory and methods. J Logic Program 19/20:629–679
Muggleton S, Feng C (1990) Efficient induction of logic programs. In: Proceedings of the 1st conference on algorithmic learning theory, Ohmsma, Tokyo, pp 368–381
Nienhuys-Cheng S-H, de Wolf R (1997) Foundations of inductive logic programming. Springer, Berlin
Plotkin GD (1970) A note on inductive generalization. In: Machine intelligence, vol 5. Edinburgh University Press, Edinburgh, pp 153–163
Plotkin GD (1971) A further note on inductive generalization. In: Machine intelligence, vol 6. Edinburgh University Press, Edinburgh, pp 101–124
Rouveirol C (1994) Flattening and saturation: two representation changes for generalization. Mach Learn 14(2):219–232
Sammut C, Banerji RB (1986) Learning concepts by asking questions. In: Michalski RS, Carbonell JG, Mitchell TM (eds) Machine learning: an artificial intelligence approach, vol 2. Morgan Kaufmann, San Francisco, pp 167–192
Semeraro G, Esposito F, Malerba D (2006) Ideal refinement of datalog programs. In: Proceedings of the 5th international workshop on logic program synthesis and transformation, Utrecht. Lecture notes in computer science, vol 1048. Springer, pp 120–136
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Science+Business Media New York
About this entry
Cite this entry
De Raedt, L. (2017). Logic of Generality. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_489
Download citation
DOI: https://doi.org/10.1007/978-1-4899-7687-1_489
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4899-7685-7
Online ISBN: 978-1-4899-7687-1
eBook Packages: Computer ScienceReference Module Computer Science and Engineering