Distinguishing Adaptive Search from Random Search in Robots and T cells

ABSTRACT

In order to trigger an adaptive immune response, T cells move through lymph nodes (LNs) searching for dendritic cells (DCs) that carry antigens indicative of infection. We hypothesize that T cells adapt to cues in the (LN) environment to increase search efficiency. We test this hypothesis by identifying locations that are visited by T cells more frequently than a random model of search would suggest. We then test whether T cells that visit such locations have different movement patterns than other T cells. Our analysis suggests that T cells do adapt their movement in response to cues that may indicate the locations of DC targets. We test the ability of our method to identify frequently visited sites in T cells and in a swarm of simulated iAnt robots evolved to search using a suite of biologically-inspired behaviours. We compare the movement of T cells and robots that repeatedly sample the same locations in space with the movement of agents that do not resample space in order to understand whether repeated sampling alters movement. Our analysis suggests that specific environmental cues can be inferred from the movement of T cells. While the precise identity of these cues remains unknown, comparing adaptive search strategies of robots to the movement patterns of T cells lends insights into search efficiency in both systems.

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Index Terms

• Computer systems organization

  • Embedded and cyber-physical systems

    • Robotics

      • Robotic autonomy

• Theory of computation

  • Design and analysis of algorithms

    • Data structures design and analysis

      • Sorting and searching