Building Distribution Networks Using Cooperating Agents

Handbook of Research on Nature-Inspired Computing for Economics and Management ◽

10.4018/978-1-59140-984-7.ch037 ◽

2007 ◽

pp. 557-571

Author(s):

N. Urquhart

Keyword(s):

Evolutionary Algorithms ◽

Social Interactions ◽

Real World ◽

Social Systems ◽

Dynamic Environment ◽

Distribution Networks ◽

Real World Data ◽

Agent Based ◽

World Data ◽

Cooperating Agents

This chapter examines the use of emergent computing to optimize solutions to logistics problems. The chapter initially explores the use of agents and evolutionary algorithms to optimise postal distribution networks. The structure of the agent community and the means of interaction between agents is based on social interactions previously used to solve these problems. The techniques developed are then adapted for use in a dynamic environment planning the despatch of goods from a supermarket. These problems are based on real-world data in terms of geography and constraints. The author hopes that this chapter will inform researchers as to the suitability of emergent computing in real-world scenarios and the abilities of agent-based systems to mimic social systems.

Download Full-text

An Agent-Based Model of Lost Person Dynamics for Enabling Wilderness Search and Rescue

Volume 2: Modeling and Control of Engine and Aftertreatment Systems; Modeling and Control of IC Engines and Aftertreatment Systems; Modeling and Validation; Motion Planning and Tracking Control; Multi-Agent and Networked Systems; Renewable and Smart Energy Systems; Thermal Energy Systems; Uncertain Systems and Robustness; Unmanned Ground and Aerial Vehicles; Vehicle Dynamics and Stability; Vibrations: Modeling, Analysis, and Control ◽

10.1115/dscc2019-9222 ◽

2019 ◽

Author(s):

Amanda Hashimoto ◽

Nicole Abaid

Keyword(s):

Real World ◽

Random Variable ◽

Fixed Time ◽

Search And Rescue ◽

Agent Based Model ◽

Real World Data ◽

Agent Based ◽

World Data ◽

Analogous Data ◽

Behavior Strategies

Abstract In this paper, we introduce an agent-based model of lost person behavior that may be used to improve current methods for wilderness search and rescue (SAR). The model defines agents moving on a landscape with behavior considered as a random variable. The behavior uses a distribution of four known lost person behavior strategies in order to simulate possible trajectories for the agent. We simulate all possible distributions of behaviors in the model and compute distributions of horizontal distances traveled in a fixed time. By comparing these results to analogous data from a database of lost person cases, we explore the model’s validity with respect to real-world data.

Download Full-text

An agent-based model of sign language persistence informed by real-world data

Language Dynamics and Change ◽

10.1163/22105832-bja10010 ◽

2020 ◽

Vol 10 (2) ◽

pp. 158-187

Author(s):

Katie Mudd ◽

Connie de Vos ◽

Bart de Boer

Keyword(s):

Sign Language ◽

Real World ◽

Cultural Transmission ◽

Model Development ◽

Agent Based Modeling ◽

Real World Data ◽

Sign Languages ◽

Genetic Transmission ◽

Agent Based ◽

World Data

Abstract As evidence from sign languages is increasingly used to investigate the process of language emergence and evolution, it is important to understand the conditions that allow for sign languages to persist. We build on a mathematical model of sign language persistence (i.e. protection from loss) which takes into account the genetic transmission of deafness, the cultural transmission of sign language and marital patterns (Aoki & Feldman, 1991). We use agent-based modeling techniques and draw inspiration from the wealth of genetic and cultural data on the sign language Kata Kolok to move towards a less abstract model of sign language persistence. In a set of experiments we explore how sign language persistence is affected by language transmission types, the distribution of deaf alleles, population size and marital patterns. We highlight the value of using agent-based modeling for this type of research, which allows for the incorporation of real-world data into model development.

Download Full-text