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Compile the Hypothesis Space: Do it Once, Use it Often

Fonseca N.A., Camacho R., Rocha R., Costa V.S.

Fundamenta Informaticae

2008

Vol. 89, nr 1

45-67

Inductive Logic Programming (ILP) is a powerful and well-developed abstraction for multi-relational data mining techniques. Despite the considerable success of ILP, deployed ILP systems still have efficiency problems when applied to complex problems. In this paper we propose a novel technique that avoids the procedure of deducing each example to evaluate each constructed clause. The technique is based on the Mode Directed Inverse Entailment approach to ILP, where a bottom clause is generated for each example and the generated clauses are subsets of the literals of such bottom clause. We propose to store in a prefix-tree all clauses that can be generated from all bottom clauses together with some extra information. We show that this information is sufficient to estimate the number of examples that can be deduced froma clause and present an ILP algorithmthat exploits this representation. We also present an extension of the algorithm where each prefix-tree is computed only once (compiled) per example. The evaluation of hypotheses requires only basic and efficient operations on trees. This proposal avoids re-computation of hypothesis’ value in theorylevel search, in cross-validation evaluation procedures and in parameter tuning. Both proposals are empirically evaluated on real applications and considerable speedups were observed.

Presenting a technique for registering images and range data using a topological representation of a path within an environment

Ferreira F., Davim L., Rocha R., Dias J., Santos V.

Journal of Automation Mobile Robotics and Intelligent Systems

2007

Vol. 1, No. 3

47-56

This article presents a novel method to the utilize topological representation of a path, thatpath that is created from sequences of images from digital cameras and sensor data from range sensors. A topological representation of the environment is created by leading the robot around the environment during a familiarisation phaseLeading the robot around the environment during a familiarisation phase creates a topological representation of the environment. While moving down the same path, the robot is able to localise itself within the topological representation that is has been previously created. The principal contribution to the state of the art is that, by using a topological representation of the environment, individual 3D data sets acquired from a set of range sensors need not be registered in a single, [Global] Coordinate Reference System. Instead, 3D point clouds for small sections of the environment are indexed to a sequence of multi-sensor views, of images and range data. Such a registration procedure can be useful in the construction of 3D representations of large environments and in the detection of changes that might occur within these environments.