A Sequential Decision-Making Model for Perimeter Identification

TL;DR

This paper introduces a real-time, publicly accessible information-based sequential decision-making framework for perimeter identification, conceptualized as a game between an agent and environment, demonstrated through a real-world scenario.

Contribution

It proposes a novel game-theoretic sequential decision-making model for perimeter search that operates efficiently with minimal data and real-time constraints.

Findings

Effective perimeter identification in real-world scenarios

Model operates with publicly accessible information

Demonstrates adaptability and efficiency in perimeter search

Abstract

Perimeter identification involves ascertaining the boundaries of a designated area or zone, requiring traffic flow monitoring, control, or optimization. Various methodologies and technologies exist for accurately defining these perimeters; however, they often necessitate specialized equipment, precise mapping, or comprehensive data for effective problem delineation. In this study, we propose a sequential decision-making framework for perimeter search, designed to operate efficiently in real-time and require only publicly accessible information. We conceptualize the perimeter search as a game between a playing agent and an artificial environment, where the agent's objective is to identify the optimal perimeter by sequentially improving the current perimeter. We detail the model for the game and discuss its adaptability in determining the definition of an optimal perimeter. Ultimately, we showcase the model's efficacy through a real-world scenario, highlighting the identification of corresponding optimal perimeters.