2021/06/12 by Grace Cai, Wendy Wu, Cai, Grace +7
Agricultural and Biological Sciences · Biochemistry, Genetics and Molecular Biology · #Animal Behavior and Reproduction #FOS: Computer and information sciences #Insect and Arachnid Ecology and Behavior #Multiagent Systems (cs.MA) #Plant and animal studies
paper · pdf · doi:10.48550/arxiv.2106.06867
openalex publication_date 2021/06/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Ant species such as Temnothorax albipennis select a new nest site in a distributed fashion that, if modeled correctly, can serve as useful information for site selection algorithms for robotic swarms and other applications. Studying and replicating the ants' house hunting behavior will also illuminate useful distributed strategies that have evolved in nature. Many of the existing models of househunting behaviour for T. albipennis make the assumption that all candidate nest sites are equally distant from the ants' home nest, or that an ant has an equal probability of finding each candidate nest site. However, realistically this is not the case, as nests that are further away from the home nest and nests that are difficult to access are less likely to be found, even if they are of higher quality. We extend previous house-hunting models to account for a pairwise distance metric between nests, compare our results to those of real colonies, and use our results to examine the effects of house hunting in nests of different spatial orientations. Our incorporation of distances in the ant model appear to match empirical data in situations where a distance-quality tradeoff between nests is relevant. Furthermore, the model continues to be on par with previous house-hunting models in experiments where all candidate nests are equidistant from the home nest, as is typically assumed.