Search Results for author: Manuel Lopes

Found 6 papers, 1 papers with code

Agents for Automated User Experience Testing

no code implementations13 Apr 2021 Pedro M. Fernandes, Manuel Lopes, Rui Prada

The automation of functional testing in software has allowed developers to continuously check for negative impacts on functionality throughout the iterative phases of development.

Class Teaching for Inverse Reinforcement Learners

2 code implementations29 Nov 2019 Manuel Lopes, Francisco Melo

In this paper we propose the first machine teaching algorithm for multiple inverse reinforcement learners.

Norms for Beneficial A.I.: A Computational Analysis of the Societal Value Alignment Problem

no code implementations26 Jun 2019 Pedro Fernandes, Francisco C. Santos, Manuel Lopes

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.

Active Learning for Autonomous Intelligent Agents: Exploration, Curiosity, and Interaction

no code implementations6 Mar 2014 Manuel Lopes, Luis Montesano

In this survey we present different approaches that allow an intelligent agent to explore autonomous its environment to gather information and learn multiple tasks.

Active Learning online learning

Multi-Armed Bandits for Intelligent Tutoring Systems

no code implementations11 Oct 2013 Benjamin Clement, Didier Roy, Pierre-Yves Oudeyer, Manuel Lopes

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.

Multi-Armed Bandits

Exploration in Model-based Reinforcement Learning by Empirically Estimating Learning Progress

no code implementations NeurIPS 2012 Manuel Lopes, Tobias Lang, Marc Toussaint, Pierre-Yves Oudeyer

Formal exploration approaches in model-based reinforcement learning estimate the accuracy of the currently learned model without consideration of the empirical prediction error.

Model-based Reinforcement Learning reinforcement-learning

Cannot find the paper you are looking for? You can Submit a new open access paper.