activity
20202022
most citedFeed-in Tariff Contract Schemes and Regulatory Uncertainty

1 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

cs.MA20221 cited

Learning to Cooperate with Completely Unknown Teammates

Alexandre Neves, Alberto Sardinha

A key goal of ad hoc teamwork is to develop a learning agent that cooperates with unknown teams, without resorting to any pre-coordination protocol. Despite a vast number of ad hoc…

cs.AI2022

Assisting Unknown Teammates in Unknown Tasks: Ad Hoc Teamwork under Partial Observability

João G. Ribeiro, Cassandro Martinho, Alberto Sardinha +1

In this paper, we present a novel Bayesian online prediction algorithm for the problem setting of ad hoc teamwork under partial observability (ATPO), which enables on-the-fly colla…

cs.CL2021

Online Learning Meets Machine Translation Evaluation: Finding the Best Systems with the Least Human Effort

Vânia Mendonça, Ricardo Rei, Luisa Coheur +2

In Machine Translation, assessing the quality of a large amount of automatic translations can be challenging. Automatic metrics are not reliable when it comes to high performing sy…

eess.SY2021

A Methodology for the Development of RL-Based Adaptive Traffic Signal Controllers

Guilherme S. Varela, Pedro P. Santos, Alberto Sardinha +1

This article proposes a methodology for the development of adaptive traffic signal controllers using reinforcement learning. Our methodology addresses the lack of standardization i…

math.OC20201 cited

Feed-in Tariff Contract Schemes and Regulatory Uncertainty

Luciana Barbosa, Cláudia Nunes, Artur Rodrigues +1

This paper presents a novel analysis of two feed-in tariffs (FIT) under market and regulatory uncertainty, namely a sliding premium with cap and floor and a minimum price guarantee…