Thanks to recent technological advances, it is now possible to track with an
unprecedented precision and for long periods of time the movement patterns of
many living organisms in their habitat. The increasing amount of data available
on single trajectories offers the possibility of understanding how animals move
and of testing basic movement models. Random walks have long represented the
main description for micro-organisms and have also been useful to understand
the foraging behaviour of large animals. Nevertheless, most vertebrates, in
particular humans and other primates, rely on sophisticated cognitive tools
such as spatial maps, episodic memory and travel cost discounting. These
properties call for other modeling approaches of mobility patterns. We propose
a foraging framework where a learning mobile agent uses a combination of
memory-based and random steps. We investigate how advantageous it is to use
memory for exploiting resources in heterogeneous and changing environments. An
adequate balance of determinism and random exploration is found to maximize the
foraging efficiency and to generate trajectories with an intricate
spatio-temporal order. Based on this approach, we propose some tools for
analysing the non-random nature of mobility patterns in general.Comment: 14 pages, 4 figures, improved discussio