We consider model-based reinforcement learning in finite Markov Decision Processes (MDPs), focussing on so-called optimistic strategies. Optimism is usually implemented by carryin...
Motivation – Designers make decisions that later influence how users work with the systems that they have designed. When errors occur in use, it is tempting to focus on the acti...
—Optimal scheduling for concurrent transmissions in rate-nonadaptive wireless networks is NP-hard. Optimal scheduling in rate-adaptive wireless networks is even more difficult, ...
: The Borowsky-Gafni (BG) simulation algorithm is a powerful reduction algorithm that shows that t-resilience of decision tasks can be fully characterized in terms of wait-freedom....
A computational agent model for monitoring and control of a virtual human agent’s resources and exhaustion is presented. It models a physically grounded intelligent decision maki...