Real-time Learning and Planning in Environments with Swarms: A Hierarchical and a Parameter-based Simulation Approach. - Lukasz Pelcner; Shaling Li; Matheus Aparecido do Carmo Alves; Leandro Soriano Marcolino; Alex Collins (AAMAS, 2020)

On-line Estimators for Ad-hoc Task Allocation. - Elnaz Shafipour Yourdshahi; Matheus Aparecido do Carmo Alves; Leandro Soriano Marcolino; Plamen Angelov 001 (AAMAS, 2020)

Effective and unburdensome forecast of highway traffic flow with adaptive computing. - Matheus Aparecido do Carmo Alves; Robson L. F. Cordeiro (Knowl. Based Syst., 2021)

AdLeap-MAS: An Open-source Multi-Agent Simulator for Ad-hoc Reasoning. - Matheus Aparecido do Carmo Alves; Amokh Varma; Yehia Elkhatib; Leandro Soriano Marcolino (AAMAS, 2022)

On-line estimators for ad-hoc task execution: learning types and parameters of teammates for effective teamwork. - Elnaz Shafipour Yourdshahi; Matheus Aparecido do Carmo Alves; Amokh Varma; Leandro Soriano Marcolino; Jó Ueyama; Plamen Angelov 001 (Auton. Agents Multi Agent Syst., 2022)

Information-guided Planning: An Online Approach for Partially Observable Problems. - Matheus Aparecido do Carmo Alves; Amokh Varma; Yehia Elkhatib; Leandro Soriano Marcolino (NeurIPS, 2023)

It Is Among Us: Identifying Adversaries in Ad-hoc Domains using Q-valued Bayesian Estimations. - Matheus Aparecido do Carmo Alves; Amokh Varma; Yehia Elkhatib; Leandro Soriano Marcolino (AAMAS, 2024)

Incentive-based MARL Approach for Commons Dilemmas in Property-based Environments. - Lukasz Pelcner; Matheus Aparecido do Carmo Alves; Leandro Soriano Marcolino; Paula A. Harrison; Peter M. Atkinson (AAMAS, 2024)

Incentive-Driven Multi-agent Reinforcement Learning Approach for Commons Dilemmas in Land-Use. - Lukasz Pelcner; Matheus Aparecido do Carmo Alves; Leandro Soriano Marcolino; Paula A. Harrison; Peter M. Atkinson (PRIMA, 2024)