Reinforcement learning promises a generic method for adapting agents to arbitrary tasks in arbitrary stochastic environments, but applying it to new real-world problems remains di...
Temporal difference reinforcement learning algorithms are perfectly suited to autonomous agents because they learn directly from an agent’s experience based on sequential actio...
Past studies have shown that objects are created and then die in phases. Thus, one way to sustain good garbage collection efficiency is to have a large enough heap to allow many ...
A comfort zone is a tested region of a system’s input space within which it has been observed to behave acceptably. To keep systems operating within their comfort zones, we advo...
Our research explores the possibilities for factoring culture into user models, working towards cultural adaptivity in the semantic web. The aim is to represent the user’s positi...