  Many species in nature demonstrate symbiotic relationships leading to
emergent behaviors through cooperation, which are sometimes beyond the scope of
the partnerships within the same species. These symbiotic relationships are
classified as mutualism, commensalism, and parasitism based on the benefit
levels involved. While these partnerships are ubiquitous in nature, it is
imperative to understand the benefits of collective behaviors in designing
heterogeneous multi-robot systems (HMRS). In this paper, we investigate the
impact of heterogeneity on the performance of HMRS applied to a search and
rescue problem. The groups consisting of searchers and rescuers, varied in the
individual robot behaviors with multiple degrees of functionality overlap and
group compositions, demonstrating various levels of heterogeneity. We propose a
new technique to measure heterogeneity in the agents through the use of
Behavior Trees and use it to obtain heterogeneity informatics from our Monte
Carlo simulations. The results show a positive correlation between the group's
heterogeneity measure and the rescue efficiency demonstrating benefits in most
of the scenarios. However, we also see cases where heterogeneity may hamper the
group's abilities pointing to the need for determining the optimal
heterogeneity in group required to maximally benefit from HMRS in real-world
applications.
