Role-Based Modeling for Designing Agent Behavior in Self-Organizing Multi-Agent Systems

By K. Lhaksmana, Yohei Murakami, T. Ishida. 2018

40 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Mechanical Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Chongjie Zhang
2. Tonghan Wang
3. Bei Peng
4. Shimon Whiteson
5. Heng Dong
6. Anuj Mahajan
7. Tarun Gupta
8. Victor R. Lesser

In Mechanical Engineering

1. Shimon Whiteson
2. Bei Peng

Citations in the corpus

• RODE: Learning Roles to Decompose Multi-Agent Tasks (2020, background): “However, roles and the associated responsibilities (or subtask-specific rewards (Sun et al., 2020)) are prede-fined using prior knowledge in these systems (Lhaksmana et al., 2018).”
• ROMA: Multi-Agent Reinforcement Learning with Emergent Roles (2020, background): “However, these works exploit prior domain knowledge to decompose tasks and predefine the responsibilities of each role, which prevents role-based MAS from being dynamic and adaptive to uncertain environments.”