Theta-Subsumption in a Constraint Satisfaction Perspective

The covering test intensively used in Inductive Logic Programming, i.e. θ-subsumption, is formally equivalent to a Constraint Satisfaction problem (CSP). This paper presents a general reformulation of θ-subsumption into a binary CSP, and a new θ-subsumption algorithm, termed Django, which combines some main trend CSP heuristics and other heuristics specifically designed for θ-subsumption. Django is evaluated after the CSP standards, shifting from a worst-case complexity perspective to a statistical framework, centered on the notion of Phase Transition (PT). In the PT region lie the hardest on average CSP instances; and this region has been shown of utmost relevance to ILP [4]. Experiments on artificial θ-subsumption problems designed to illustrate the phase transition phenomenon, show that Django is faster by several orders of magnitude than previous θ-subsumption algorithms, within and outside the PT region.

[1]  Christian Bessiere,et al.  MAC and Combined Heuristics: Two Reasons to Forsake FC (and CBJ?) on Hard Problems , 1996, CP.

[2]  Shan-Hwei Nienhuys-Cheng,et al.  Foundations of Inductive Logic Programming , 1997, Lecture Notes in Computer Science.

[3]  Tad Hogg,et al.  Phase Transitions and the Search Problem , 1996, Artif. Intell..

[4]  J. A. Robinson,et al.  A Machine-Oriented Logic Based on the Resolution Principle , 1965, JACM.

[5]  Jörg-Uwe Kietz,et al.  An Efficient Subsumption Algorithm for Inductive Logic Programming , 1994, ICML.

[6]  Luc De Raedt,et al.  Inductive Logic Programming: Theory and Methods , 1994, J. Log. Program..

[7]  Patrick Prosser,et al.  HYBRID ALGORITHMS FOR THE CONSTRAINT SATISFACTION PROBLEM , 1993, Comput. Intell..

[8]  Ashwin Srinivasan,et al.  Theories for Mutagenicity: A Study in First-Order and Feature-Based Induction , 1996, Artif. Intell..

[9]  Marc Gyssens,et al.  Decomposing Constraint Satisfaction Problems Using Database Techniques , 1994, Artif. Intell..

[10]  Michèle Sebag,et al.  Analyzing Relational Learning in the Phase Transition Framework , 2000, ICML.

[11]  Alan K. Mackworth Consistency in Networks of Relations , 1977, Artif. Intell..

[12]  Fritz Wysotzki,et al.  Efficient Theta-Subsumption Based on Graph Algorithms , 1996, Inductive Logic Programming Workshop.

[13]  Rina Dechter,et al.  Tree Clustering for Constraint Networks , 1989, Artif. Intell..

[14]  Georg Gottlob,et al.  On the efficiency of subsumption algorithms , 1985, JACM.

[15]  Jean-Daniel Zucker,et al.  Semantic Abstraction for Concept Representation and Learning , 2001 .

[16]  Edward P. K. Tsang,et al.  Foundations of constraint satisfaction , 1993, Computation in cognitive science.