Enhancing Multi-agent System Testing with Diversity-Guided Exploration and Adaptive Critical State Exploitation

Author:

Ma Xuyan1ORCID,Wang Yawen1ORCID,Wang Junjie1ORCID,Xie Xiaofei2ORCID,Wu Boyu1ORCID,Li Shoubin1ORCID,Xu Fanjiang1ORCID,Wang Qing1ORCID

Affiliation:

1. Institute of Software at Chinese Academy of Sciences, Beijing, China

2. Singapore Management University, Singapore, Singapore

Funder

National Natural Science Foundation of China Grant

Publisher

ACM

Reference44 articles.

1. Dan Amir and Ofra Amir. 2018. HIGHLIGHTS: Summarizing Agent Behavior to People. In Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, AAMAS 2018, Stockholm, Sweden, July 10-15, 2018, Elisabeth André, Sven Koenig, Mehdi Dastani, and Gita Sukthankar (Eds.). International Foundation for Autonomous Agents and Multiagent Systems Richland, SC, USA / ACM, 1168–1176.

2. Parasumanna Gokulan Balaji and Dipti Srinivasan. 2010. An introduction to multi-agent systems. Innovations in multi-agent systems and applications-1 1–27.

3. Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemyslaw Debiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Christopher Hesse, Rafal Józefowicz, Scott Gray, Catherine Olsson, Jakub Pachocki, Michael Petrov, Henrique Pondé de Oliveira Pinto, Jonathan Raiman, Tim Salimans, Jeremy Schlatter, Jonas Schneider, Szymon Sidor, Ilya Sutskever, Jie Tang, Filip Wolski, and Susan Zhang. 2019. Dota 2 with Large Scale Deep Reinforcement Learning. CoRR, abs/1912.06680 (2019).

4. Deep Reinforcement Learning for Truck-Drone Delivery Problem

5. A cooperative multi-agent robotics system: Design and modelling

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