Exploring Dynamic Environments Using Stochastic Search Strategies

TL;DR

This paper reviews stochastic search strategies inspired by animal behavior, focusing on their application in exploring dynamic environments and assessing their effectiveness for uncertainty and perceptual limitations.

Contribution

It provides a comprehensive overview of bio-inspired stochastic search methods, analyzing their mechanisms and potential for use in highly dynamic, uncertain environments.

Findings

Stochastic search strategies effectively handle environmental uncertainty.

Animal-inspired models improve exploration in dynamic settings.

Optimal foraging concepts offer valuable insights for search efficiency.

Abstract

In this paper, we conduct a literature review of laws of motion based on stochastic search strategies which are mainly focused on exploring highly dynamic environments. In this regard, stochastic search strategies represent an interesting alternative to cope with uncertainty and reduced perceptual capabilities. This study aims to present an introductory overview of research in terms of directional rules and searching methods mainly based on bio-inspired approaches. This study critically examines the role of animal searching behavior applied to random walk models using stochastic rules and kinesis or taxis. The aim of this study is to examine existing techniques and to select relevant work on random walks and analyze their actual contributions. In this regard, we cover a wide range of displacement events with an orientation mechanism given by a reactive behavior or a source-seeking behavior. Finally, we conclude with a discussion concerning the usefulness of using optimal foraging strategies as a reliable methodology.