Effect of cooperative team size on coordination in adaptive multi-agent systems

D. E. Asher; S. L. Barton; E. Zaroukian; N. R. Waytowich

doi:10.1117/12.2518626

10 May 2019 Effect of cooperative team size on coordination in adaptive multi-agent systems

D. E. Asher, S. L. Barton, E. Zaroukian, N. R. Waytowich

Proceedings Volume 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications; 110060Z (2019) https://doi.org/10.1117/12.2518626
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

In recent work, we utilized convergent cross mapping (CCM) to quantify coordination in a multi-agent reinforcement learning (MARL) paradigm by measuring causal influence between pairs of agents in a joint task. CCM was originally developed to detect causal influences within ecological systems, and as we previously demonstrated, it can be used to measure causal dependencies between pairs of time-series data. While this work has provided important insight into the coordination between 2 teammates, it is not clear how such coordination scales with the number of agents working together with a shared goal. Within a predator-prey pursuit environment, the current study investigates the influence that an incremental increase in number of predator agents has on the inherently causal relationship between predators working together to pursue a single prey. We hypothesize that averaged CCM values will decrease with increasing number of predators due to a redistribution of coordination across all predator agents. This work provides a quantitative assessment for the fundamental influence that number of cooperative agents has on the causal relationship between agents working together on a joint task, and insight into coordinated group behaviors.

Conference Presentation

Citation Download Citation

D. E. Asher, S. L. Barton, E. Zaroukian, and N. R. Waytowich "Effect of cooperative team size on coordination in adaptive multi-agent systems", Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 110060Z (10 May 2019); https://doi.org/10.1117/12.2518626

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