Open-ended learning is regarded as the ultimate milestone, especially in intelligent robotics. Preferably it should be unsupervised and it is by its nature inductive. In this article we want to give an overview of attempts to use Inductive Logic Programming (ILP) as a machine learning technique in the context of embodied autonomous agents. Relatively few such attempts exist altogether and the main goal in reviewing several of them was to find a thorough understanding of the difficulties that the application of ILP has in general and especially in this area. The second goal was to review any possible directions for overcoming these obstacles standing on the way of more widespread use of ILP in this context of embodied autonomous agents. Whilst the most serious problems, the mismatch between ILP and the large datasets encountered with embodied autonomous agents seem difficult to overcome we also found interesting research actively pursuing to alleviate these problems.
Matching 3D objects by their similarity is a fundamental problem in computer vision, multimedia databases, molecular biology, computer graphics and a variety of other fields. A challenging aspect of this problem is to find a suitable shape signature/descriptor that can be constructed and compared quickly, while still discriminating between similar and dissimilar shapes. We find that the major problems in comparing 3D mesh objects lie in the non-uniform vertex sampling and level of detail distribution, in the non-uniform polygon topology and in mesh-representation anomalies, so the primary motivation behind the work presented in this paper is the introduction of mesh-parameterization which brings meshes into a form having uniform vertex sampling, uniform polygon topology and filtered anomalies, by spherically mapping the mesh surface. Further, we present two approaches in inferring shapedescriptors from the spherically mapped objects and the results from the conducted experiments.
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