We present a novel approach to natural language generation (NLG) that applies hierarchical reinforcement learning to text generation in the wayfinding domain. Our approach aims to...
We consider reinforcement learning as solving a Markov decision process with unknown transition distribution. Based on interaction with the environment, an estimate of the transit...
—We develop opportunistic scheduling policies for cognitive radio networks that maximize the throughput utility of the secondary (unlicensed) users subject to maximum collision c...
Current aggregation systems either have a single inbuilt aggregation mechanism or require applications to specify an aggregation policy a priori. It is hard to predict the read an...
We present quantitative models for the selection pressure of cellular evolutionary algorithms structured in two dimensional regular lattices. We derive models based on probabilisti...
Mario Giacobini, Enrique Alba, Andrea Tettamanzi, ...