The problem of detecting objects and their movements in sensor data is of crucial importance in providing safe navigation through both indoor and outdoor environments for the visually impaired. In our setting we use depth-sensor data obtained from a simulator and use inductive logic programming (ILP), a subfield of machine learning that deals with learning concept descriptions, to learn how to detect borders, find the border that is nearest to some point of interest, and border correspondence through time. We demonstrate how ILP can be used to tackle this problem in an incremental manner by using previously learned predicates to construct more complex ones. The learned concept descriptions show high (> 90%) accuracy and their natural language interpretation closely matches an intuitive understanding of their meaning.

One of the main issues when using inductive logic programming (ILP) in practice remain the long running times that are needed by ILP systems to induce the hypothesis. We explore the possibility of reducing the induction running times of systems that use asymmetric relative minimal generalisation (ARMG) by analysing the bottom clauses of examples that serve as inputs into the generalisation operator. Using the fact that the ARMG covers all of the examples and that it is a subset of the variabilization of one of the examples, we identify literals that cannot appear in the ARMG and remove them prior to computing the generalisation. We apply this procedure to the ProGolem ILP system and test its performance on several real world data sets. The experimental results show an average speedup of 36 % compared to the base ProGolem system and 12 % compared to ProGolem extended with caching, both without a decrease in the accuracy of the produced hypotheses. We also observe that the gain from using the proposed method varies greatly, depending on the structure of the data set.

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