The automation of functional testing in software has allowed developers to continuously check for negative impacts on functionality throughout the iterative phases of development.
systems following human conscious policies that, when introduced in society, lead to an equilibrium where the gains for the adopters are not at a cost for non-adopters, thus increasing the overall wealth of the population and lowering inequality.
In this survey we present different approaches that allow an intelligent agent to explore autonomous its environment to gather information and learn multiple tasks.
We present an approach to Intelligent Tutoring Systems which adaptively personalizes sequences of learning activities to maximize skills acquired by students, taking into account the limited time and motivational resources.
Formal exploration approaches in model-based reinforcement learning estimate the accuracy of the currently learned model without consideration of the empirical prediction error.