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About

About

Luis Paulo Reis is an Associate Professor at the University of Minho in Portugal and Director of LIACC â?? Artificial Intelligence and Computer Science Laboratory where he also coordinates the Human-Machine Intelligent Cooperation Research Group. He is a IEEE Senior Member and vice-president of both the Portuguese Society for Robotics and the Portuguese Association for Artificial Intelligence. During the last 25 years he has lectured courses, at the University, on Artificial Intelligence, Intelligent Robotics, Multi-Agent Systems, Simulation and Modelling, Educational/Serious Games and Computer Programming. He was principal investigator of more than 10 research projects in those areas. He won more than 50 scientific awards including wining more than 15 RoboCup international competitions and best papers at conferences such as ICEIS, Robotica, IEEE ICARSC and ICAART. He supervised 17 PhD and 95 MSc theses to completion. He organized more than 50 scientific events and belonged to the Program Committee of more than 250 scientific events. He is the author of more than 250 publications in international conferences and journals (indexed at SCOPUS or ISI Web of Knowledge).

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Publications

2020

Robot 2019: Fourth Iberian Robotics Conference

Authors
Silva, MF; Luís Lima, J; Reis, LP; Sanfeliu, A; Tardioli, D;

Publication
Advances in Intelligent Systems and Computing

Abstract

2020

Robot 2019: Fourth Iberian Robotics Conference - Advances in Robotics, Volume 1, Porto, Portugal, 20-22 November, 2019

Authors
Silva, MF; Lima, JL; Reis, LP; Sanfeliu, A; Tardioli, D;

Publication
ROBOT (1)

Abstract

2020

Game Adaptation by Using Reinforcement Learning Over Meta Games

Authors
Reis, S; Reis, LP; Lau, N;

Publication
Group Decision and Negotiation

Abstract
In this work, we propose a Dynamic Difficulty Adjustment methodology to achieve automatic video game balance. The balance task is modeled as a meta game, a game where actions change the rules of another base game. Based on the model of Reinforcement Learning (RL), an agent assumes the role of a game master and learns its optimal policy by playing the meta game. In this new methodology we extend traditional RL by adding the existence of a meta environment whose state transition depends on the evolution of a base environment. In addition, we propose a Multi Agent System training model for the game master agent, where it plays against multiple agent opponents, each with a distinct behavior and proficiency level while playing the base game. Our experiment is conducted on an adaptive grid-world environment in singleplayer and multiplayer scenarios. Our results are expressed in twofold: (i) the resulting decision making by the game master through gameplay, which must comply in accordance to an established balance objective by the game designer; (ii) the initial conception of a framework for automatic game balance, where the balance task design is reduced to the modulation of a reward function (balance reward), an action space (balance strategies) and the definition of a balance space state. © 2020, Springer Nature B.V.

2020

Preface

Authors
Silva, MF; Lima, JL; Reis, LP; Sanfeliu, A; Tardioli, D;

Publication
Advances in Intelligent Systems and Computing

Abstract

2020

TIMAIRIS: Autonomous Blank Feeding for Packaging Machines

Authors
Pedrosa, EF; Lim, GH; Amaral, F; Pereira, A; Cunha, B; Azevedo, JL; Dias, P; Dias, R; Reis, LP; Shafii, N; Tudico, A; Mazzotti, C; Carricato, M; Badini, S; Rea, D; Lau, N;

Publication
Bringing Innovative Robotic Technologies from Research Labs to Industrial End-users - The Experience of the European Robotics Challenges

Abstract