In bandit problems, a decision-maker must choose between a set of alternatives, each of which has a fixed but unknown rate of reward, to maximize their total number of rewards ov...
Michael D. Lee, Shunan Zhang, Miles Munro, Mark St...
This paper describes a computationally feasible approximation to the AIXI agent, a universal reinforcement learning agent for arbitrary environments. AIXI is scaled down in two ke...
Joel Veness, Kee Siong Ng, Marcus Hutter, William ...
In this paper, we examine an emerging variation of the classification problem, which is known as the inverse classification problem. In this problem, we determine the features to b...
Comparing humans and machines is one important source of information about both machine and human strengths and limitations. Most of these comparisons and competitions are performe...
Javier Insa-Cabrera, David L. Dowe, Sergio Espa&nt...
Research in reinforcement learning has produced algorithms for optimal decision making under uncertainty that fall within two main types. The first employs a Bayesian framework, ...