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On-line Estimators for Ad-hoc Task Execution: Learning Types and Parameters of Teammates for Effective Teamwork.

, , , , , and . AAMAS, page 140-142. ACM, (2023)

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On-line Estimators for Ad-hoc Task Allocation., , , and . AAMAS, page 1999-2001. International Foundation for Autonomous Agents and Multiagent Systems, (2020)AdLeap-MAS: An Open-source Multi-Agent Simulator for Ad-hoc Reasoning., , , and . AAMAS, page 1893-1895. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), (2022)Incentive-based MARL Approach for Commons Dilemmas in Property-based Environments., , , , and . AAMAS, page 2414-2416. International Foundation for Autonomous Agents and Multiagent Systems / ACM, (2024)It Is Among Us: Identifying Adversaries in Ad-hoc Domains using Q-valued Bayesian Estimations., , , and . AAMAS, page 472-480. International Foundation for Autonomous Agents and Multiagent Systems / ACM, (2024)Real-time Learning and Planning in Environments with Swarms: A Hierarchical and a Parameter-based Simulation Approach., , , , and . AAMAS, page 1019-1027. International Foundation for Autonomous Agents and Multiagent Systems, (2020)On-line Estimators for Ad-hoc Task Execution: Learning Types and Parameters of Teammates for Effective Teamwork., , , , , and . AAMAS, page 140-142. ACM, (2023)