Inductive Logic Programming and Embodied Agents

Abstract

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.

Inductive Logic Programming (ILP) and Reasoning by Analogy in Context of Embodied Robot Learning

“…We have discussed these issues at length in (Kulakov et al, 2009). We believe that with the results presented, these issues can be partially addressed but the overall conclusion is that ILP in the existing implementations is probably not suited for the overall goal of the XPERO project.…”