Olfactory search
Antonio Celani, Emanuele Panizon
TL;DR
This paper reviews algorithmic strategies for olfactory search, emphasizing formal modeling as Partially Observable Markov Decision Processes to understand and optimize search behaviors in animals and robots.
Contribution
It provides a systematic review of olfactory search strategies with a focus on formalizing them as POMDPs for optimal decision-making.
Findings
Clarifies relationships between heuristic strategies
Highlights the use of POMDPs for optimal actions
Provides a comprehensive review of olfactory search algorithms
Abstract
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.
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Taxonomy
TopicsOlfactory and Sensory Function Studies