Abstract. Learning to act in an unknown partially observable domain is a difficult variant of the reinforcement learning paradigm. Research in the area has focused on model-free m...
This paper examines sustained, socially-situated engagement in online learning communities. We propose three levels of engagement that are examined through an empirical study of j...
: This paper presents the design, implementation and evaluation of Deadline-Driven Auctions (DDA), a novel task mapping infrastructure for heterogeneous distributed environments. D...
In this paper, we applied online neuroevolution to evolve nonplayer characters for The Open Racing Car Simulator (TORCS). While previous approaches allowed online learning with per...
Luigi Cardamone, Daniele Loiacono, Pier Luca Lanzi