Discovering Rules by Meta-level Abduction

Inoue, Katsumi; Furukawa, K.; Kobayashi, Ichiro; Nabeshima, Hidetomo

doi:10.1007/978-3-642-13840-9_6

Cited by 18 publications

(24 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Inoue et al 2010). One important feature of MIL, which distinguishes it from other existing approaches, is that it introduces new predicate symbols which represent relations rather than new objects or propositions.…”

Section: Related Workmentioning

confidence: 99%

Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited

2015

View full text Add to dashboard Cite

Since the late 1990s predicate invention has been under-explored within inductive logic programming due to difficulties in formulating efficient search mechanisms. However, a recent paper demonstrated that both predicate invention and the learning of recursion can be efficiently implemented for regular and context-free grammars, by way of metalogical substitutions with respect to a modified Prolog meta-interpreter which acts as the learning engine. New predicate symbols are introduced as constants representing existentially quantified higher-order variables. The approach demonstrates that predicate invention can be treated as a form of higher-order logical reasoning. In this paper we generalise the approach of meta-interpretive learning (MIL) to that of learning higher-order dyadic datalog programs. We show that with an infinite signature the higher-order dyadic datalog class H 2 2 has universal Turing expressivity though H 2 2 is decidable given a finite signature. Additionally we show that Knuth-Bendix ordering of the hypothesis space together with logarithmic clause bounding allows our MIL implementation Metagol D to PAC-learn minimal cardinality H 2 2 definitions. This result is consistent with our experiments which indicate that Metagol D efficiently learns compact H 2 2 definitions involving predicate invention for learning robotic strategies, the East-West train challenge and NELL. Additionally higher-order concepts were learned in the NELL language learning domain. The Metagol code and datasets described in this paper have been made publicly available on a website to allow reproduction of results in this paper.

show abstract

Section: Related Workmentioning

confidence: 99%

Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited

2015

View full text Add to dashboard Cite

show abstract

“…MIL is related to other studies where abduction has been used for predicate invention (e.g. [7]). One important feature of MIL, which distinguishes it from other existing approaches, is that it introduces new predicate symbols which represent relations rather than new objects or propositions.…”

Section: Background and Related Workmentioning

confidence: 93%

Towards Machine Learning of Predictive Models from Ecological Data

Tamaddoni‐Nezhad

Bohan

Raybould

et al. 2015

Inductive Logic Programming

View full text Add to dashboard Cite

Abstract. In a previous paper we described a machine learning approach which was used to automatically generate food-webs from nationalscale agricultural data. The learned food-webs in the previous study consist of hundreds of ground facts representing trophic links between individual species. These species food-webs can be used to explain the structure and dynamics of particular eco-systems, however, they cannot be directly used as general predictive models. In this paper we describe the first steps towards this generalisation and present initial results on (i) learning general functional food-webs (i.e. trophic links between functional groups of species) and (ii) meta-interpretive learning (MIL) of general predictive rules (e.g. about the effect of agricultural management). Experimental results suggest that functional food-webs have at least the same levels of predictive accuracies as species food-webs despite being much more compact. The results also suggest that when the number of training examples are limited, functional food-webs have a higher predictive accuracy. In this paper we also present initial experiments where predicate invention and recursive rule learning in MIL are used to learn food-webs as well as predictive rules directly from data.

show abstract

“…On the other hand, abduction has played essential roles in knowledge discovery and development in AI and philosophy of science. Hence, integration of abduction and induction (Flach and Kakas 2000) has been discussed since 2000 in such diverse aspects as implementation of ILP systems using abductive methods (Muggleton and Bryant 2000;Inoue 2004;Ray et al 2003;Inoue et al 2010;Corapi et al 2010) and "closing the loop" methodologies in scientific discovery (Flach and Kakas 2000;King et al 2004;Synnaeve et al 2011).…”

Section: Ilp's Contribution To Logical Foundations Of Inductive Reasomentioning

confidence: 99%

“…Understanding predicate invention from the viewpoint of semantics of theory update is thus important now. A method to discover unknown relations from incomplete networks has been recently introduced in Inoue et al (2010) based on meta-level abduction, which infer missing rules, missing facts, and unknown causes that involve predicate invention in the form of existentially quantified hypotheses. This invention is also a realization of hidden object invention (Dietterich et al 2008), but lifting meta-level abduction to higher-order is also a promising method in this direction.…”

Section: The Engineering Of Ilp Systems: How?mentioning

confidence: 99%

ILP turns 20

et al. 2011

Self Cite

View full text Add to dashboard Cite

Inductive Logic Programming (ILP) is an area of Machine Learning which has now reached its twentieth year. Using the analogy of a human biography this paper recalls the development of the subject from its infancy through childhood and teenage years. We show how in each phase ILP has been characterised by an attempt to extend theory and implementations in tandem with the development of novel and challenging real-world applications. Lastly, by projection we suggest directions for research which will help the subject coming of age.

show abstract

Discovering Rules by Meta-level Abduction

Cited by 18 publications

References 14 publications

Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited

Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited

Towards Machine Learning of Predictive Models from Ecological Data

ILP turns 20

Contact Info

Product

Resources

About