Collective behaviour in nature provides a source of inspiration to engineer artificial collective adaptive systems, due to their mechanisms favouring adaptation to environmental changes and enabling complex emergent behaviour to arise from a relatively simple behaviour of individual entities. As part of our ongoing research, we study the social amoeba Dictyostelium discoideum to derive agent-based models and mechanisms that we can then exploit in artificial systems, in particular in swarm robotics. In this paper, we present a selection of agent-based models of the aggregation phase of D. discoideum, their corresponding biological illustrations and how we used them as an inspiration for transposing this behaviour into swarms of Kilobots. We focus on the stream-breaking phenomenon occurring during the aggregation phase of the life cycle of D. discoideum. Results show that the breakup of aggregation streams depends on cell density, motility, motive force and the concentration of cAMP and CF. The breakup also comes with the appearance of late centres. Our computational results show similar behaviour to our biological experiments, using Ax2(ka) strain. For swarm robotics experiments, we focus on signalling and aggregation towards a centre.

By studying and modelling the behaviour of Dictyostelium discoideum, we aim at deriving mechanisms useful for engineering collective artificial intelligence systems. This paper discusses a selection of agent-based models reproducing second-order behaviour of Dictyostelium discoideum, occurring during the migration phase; their corresponding biological illustrations; and how we used them as an inspiration for transposing this behaviour into swarms of Kilobots. For the models, we focus on: (1) the transition phase from first- to second-order emergent behaviour; (2) slugs’ uniform distribution around a light source; and (3) the relationship between slugs’ speed and length occurring during the migration phase of the life cycle of D. discoideum. Results show the impact of the length of the slug on its speed and the effect of ammonia on the distribution of slugs. Our computational results show similar behaviour to our biological experiments, using Ax2(ka) strain. For swarm robotics experiments, we focus on the transition phase, slugs’ chaining, merging and moving away from each other.