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.
We evaluated two cursor designs in the continuum between the traditional point cursor and the bubble cursor by Grossman and Balakrishnan. The lazy bubble cursor expanded to envelop the closest target when the ratio of the distances to the closest and the second closest target was less than 1:2. In addition to this lazy behavior the cone cursor had a tail that stayed on the last enveloped target until the next target was enveloped. In an experiment with 18 participants we found that the bubble cursor was faster than our cursors that had smaller target activation areas but the difference remained very small. Of the bubble cursor variants the lazy bubble exhibited higher error rate than the other two. Thus, the winners on the objective metrics were the bubble cursor and the cone cursor. The lazy bubble cursor and the bubble cursor were preferred in subjective ratings.
The design of a prototypical scalable and tiling multimonitor aware window manager is described that may overcome some of the layout management problems encountered with tiling window managers. The system also features a novel approach to monitor configuration in which monitors are treated as independent movable viewports to the large virtual desktop. This approach is expected to address a number of distal access and monitor configuration problems. In particular, it will enable many uses of multiple monitors that require dynamic or flexible monitor configurations.
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.
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