  The task of olfactory search is ubiquitous in nature and in technology, from
animals in the quest of food or of a mating partner, to robots searching for
the source of hazardous fumes in a chemical plant. Here, we focus on the
algorithmic approach to this task: we systematically review the different
olfactory search strategies. Special emphasis is given to the formal
description as a Partially Observable Markov Decision Processes, which allows
the computation of optimal actions and helps clarifying the relationships
between several effective heuristic search strategies.
