Having to cope with memory limitations is an ubiquitous issue in heuristic search. We present theoretical and practical results on new variants for exploring state-space with respe...
In Smart Spaces (SSs), the capability of learning from experience is fundamental for autonomous adaptation to environmental changes and for proactive interaction with users. New re...
Alessio Dore, Andrea F. Cattoni, Carlo S. Regazzon...
The best recent supervised sequence learning methods use gradient descent to train networks of miniature nets called memory cells. The most popular cell structure seems somewhat ar...
In many cases, normal uses of a system form patterns that will repeat. The most common patterns can be collected into a prediction model which will essentially predict that usage p...
Many agent problems in a grid world have a restricted sensory information and motor actions. The environmental conditions need dynamic processing of internal memory. In this paper...