DOCSLIB.ORG

Flock Theory: Cooperation and Decentralization

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Flock Theory: Cooperation and Decentralization FLOCK THEORY: COOPERATION AND DECENTRALIZATION IN COMMUNICATION NETWORKS A Dissertation Presented to the Faculty of the Graduate School of Cornell University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy by Devan Rosen August, 2007 © Devan Rosen FLOCK THEORY: COOPERATION AND DECENTRALIZATION IN COMMUNICATION NETWORKS Devan Rosen, Ph.D. Cornell University, 2007 Research has shown that decentralized organizations and groups perform better and have more satisfied members than centralized ones. Further, decentralized self- organizing groups are particularly superior when solving complex problems. Despite mounting research in support of decentralization, the means of how to foster and maintain a decentralized, coordinated group remains a particular problem for organizations. The current line of research proposes a theory of decentralized organizational communication, flock theory, and conducts preliminary tests of the theory. Grounded in literature from social networks, flock theory represents a theoretical model for the decentralized evolution of communicative systems. The flock model is then extended to integrate roadmap based flocking, bipartite networks, and findings from small world research to create a theory of cooperation, coordination, and navigation within decentralized communication networks. Empirical illustrations of flock theory are conducted via two studies on two different research-based organizations, as research organizations focus on complex problem solving and coordination of knowledge. Findings provide initial support for flock theory, confirm parallel research on decentralization, and indicate that research-based organizations may be different from traditional corporate organizations in several ways. BIOGRAPHICAL SKETCH Devan Rosen received his B.A. from the University at Buffalo in 1997 as a double major in Communication and Ethnomusicology, and a minor in Sociology. After working as an organizational communication consultant, he returned to the University at Buffalo, where he received is M.A. from the Department of Communication in 2000 with a focus in organizational and international communication networks. He then received his Ph.D. from the Department of Communication at Cornell University in 2007, with a focus in organizational and computer-mediated communication networks; his minor area was Sociology with a focus on collective action and self-organizing systems. iii ACKNOWLEDGEMENTS I committed myself to this work to increase our understanding of egalitarian interaction and to promote cooperation and understanding. Similarly, this work has been the result of much cooperation and understanding from many inspiring teachers, family members, and friends. First, I would like to thank my committee chair, Dr. Geri Gay, for her continuous guidance and friendship through times of epiphany and times of confusion, you have an even keel. I would also like to give specific thanks to my special committee. Dr. Michael Macy, thank you for showing me which windows to open and letting me discover the view, you have been one of my greatest teachers. Dr. Jim Shanahan, thank you for the great discussions and valuable feedback. Dr. Alex Susskind, thank you for being a consistent source of positivity and guidance. And Dr. Katherine McComas, thank you for being a great friend and mentor. I would also like to thank my earlier advisors, Dr. George Barnett and Dr. Dean Krikorian. George, you are now one of my oldest friends and one of the main reason I am where I am in life, you set my trajectory, and you will always be the Magister. Dean, you helped me shape my thoughts at a critical time, and for that I thank you. No words that can truly express my love and gratitude for my family, but I will try. To my mother and father, you have always given me the freedom to be who I am and have loved me always. You gave me the light and the energy. To Ari, you will always be my greatest hero; I love you with all of me. To Randi, thank you for being my big sister, and never giving me a hard time. To Dahlia and Asher, thank you for keeping my wilderness from burning, and the tight hugs filled with love. To my grandparents, Len, Bernice, Shirley, and Len thank you for being the source of iv everything. To all of my aunts, uncles, and cousins, thank you. To Stan, thank you for being a great friend, and for providing the canvas for my creativity. Many friends have become my brothers and sisters. To Rich, thank you for EVERYTHING! To Leon, thank you for the protection when I needed it most. To Mike, thank you being my co-captain through many journeys both on the water and in life, and thank you for staying. To boB, thank you for being boB and letting me be your brother. To Mirit, thank you for being the silent glue that held it all together. To Jorge, thank you for making me your first friend in your new home. To Brad, thank you for such muchness and much rejoicing. To Graff, thank you for inspiring me through your art. To Jamie, thank you for loosing the bet. To Mike Kuo, thank you for your style and class. To Meaghan, you are my balance and my best friend. You inspire me more than you can know, and your warmth and energy have become my home. I love you mucho muy. The following work, and my energy, is dedicated to you. v TABLE OF CONTENTS BIOGRAPHICAL SKETCH..................................................................................... iii ACKNOWLEDGEMENTS....................................................................................... iv TABLE OF CONTENTS .......................................................................................... vi LIST OF FIGURES .................................................................................................. ix LIST OF TABLES..................................................................................................... x CHAPTER I - INTRODUCTION .............................................................................. 1 Research Goals....................................................................................................... 3 The Structure of the Dissertation ............................................................................ 4 CHAPTER II - THEORY........................................................................................... 6 Social Networks......................................................................................................... 6 Centrality ............................................................................................................... 7 Strength of Ties...................................................................................................... 7 Structural Holes...................................................................................................... 8 Knowledge Networks............................................................................................. 9 Decentralization and Effects on Performance and Satisfaction.................................. 10 Foundational work on decentralization ................................................................. 11 Recent advances in decentralization research........................................................ 14 Work Groups.................................................................................................... 14 Organizations ................................................................................................... 18 Dimensions: ................................................................................................. 19 Model types for structuring employment relations: ....................................... 20 FLOCK THEORY ................................................................................................... 23 Contributing Literature......................................................................................... 25 Emergence ....................................................................................................... 25 Jamming........................................................................................................... 28 Emergence of Creativity................................................................................... 30 Autopoiesis and Self-Organizing Systems ........................................................ 32 Benefits and Limitations of Self-Organizing Systems ....................................... 34 Limitations ................................................................................................... 34 Benefits ........................................................................................................ 37 Boids................................................................................................................ 38 Flock Theory........................................................................................................ 40 vi Structural Distance ........................................................................................... 41 Collaboration.................................................................................................... 42 Decentralization ............................................................................................... 44 Rules of Engagement........................................................................................ 45 Norms .......................................................................................................... 45 Homophily ..................................................................................................
Load more

Recommended publications
  • AI, Robots, and Swarms: Issues, Questions, and Recommended Studies
    AI, Robots, and Swarms Issues, Questions, and Recommended Studies Andrew Ilachinski January 2017 Approved for Public Release; Distribution Unlimited. This document contains the best opinion of CNA at the time of issue. It does not necessarily represent the opinion of the sponsor. Distribution Approved for Public Release; Distribution Unlimited. Specific authority: N00014-11-D-0323. Copies of this document can be obtained through the Defense Technical Information Center at www.dtic.mil or contact CNA Document Control and Distribution Section at 703-824-2123. Photography Credits: http://www.darpa.mil/DDM_Gallery/Small_Gremlins_Web.jpg; http://4810-presscdn-0-38.pagely.netdna-cdn.com/wp-content/uploads/2015/01/ Robotics.jpg; http://i.kinja-img.com/gawker-edia/image/upload/18kxb5jw3e01ujpg.jpg Approved by: January 2017 Dr. David A. Broyles Special Activities and Innovation Operations Evaluation Group Copyright © 2017 CNA Abstract The military is on the cusp of a major technological revolution, in which warfare is conducted by unmanned and increasingly autonomous weapon systems. However, unlike the last “sea change,” during the Cold War, when advanced technologies were developed primarily by the Department of Defense (DoD), the key technology enablers today are being developed mostly in the commercial world. This study looks at the state-of-the-art of AI, machine-learning, and robot technologies, and their potential future military implications for autonomous (and semi-autonomous) weapon systems. While no one can predict how AI will evolve or predict its impact on the development of military autonomous systems, it is possible to anticipate many of the conceptual, technical, and operational challenges that DoD will face as it increasingly turns to AI-based technologies.
    [Show full text]
  • Individual Versus Collective Cognition in Social Insects
    Individual versus collective cognition in social insects Ofer Feinermanᴥ, Amos Kormanˠ ᴥ Department of Physics of Complex Systems, Weizmann Institute of Science, 7610001, Rehovot, Israel. Email: [email protected] ˠ Institut de Recherche en Informatique Fondamentale (IRIF), CNRS and University Paris Diderot, 75013, Paris, France. Email: [email protected] Abstract The concerted responses of eusocial insects to environmental stimuli are often referred to as collective cognition on the level of the colony.To achieve collective cognitiona group can draw on two different sources: individual cognitionand the connectivity between individuals.Computation in neural-networks, for example,is attributedmore tosophisticated communication schemes than to the complexity of individual neurons. The case of social insects, however, can be expected to differ. This is since individual insects are cognitively capable units that are often able to process information that is directly relevant at the level of the colony.Furthermore, involved communication patterns seem difficult to implement in a group of insects since these lack clear network structure.This review discusses links between the cognition of an individual insect and that of the colony. We provide examples for collective cognition whose sources span the full spectrum between amplification of individual insect cognition and emergent group-level processes. Introduction The individuals that make up a social insect colony are so tightly knit that they are often regarded as a single super-organism(Wilson and Hölldobler, 2009). This point of view seems to go far beyond a simple metaphor(Gillooly et al., 2010)and encompasses aspects of the colony that are analogous to cell differentiation(Emerson, 1939), metabolic rates(Hou et al., 2010; Waters et al., 2010), nutrient regulation(Behmer, 2009),thermoregulation(Jones, 2004; Starks et al., 2000), gas exchange(King et al., 2015), and more.
    [Show full text]
  • Stigmergic Landmark Foraging
    Stigmergic Landmark Foraging Nyree Lemmens Karl Tuyls Faculty of Humanities and Sciences, MICC, Faculty of Industrial Design, Eindhoven Maastricht University University of Technology P.O. Box 616, 6200 MD P.O. Box 513, 5600 MB Maastricht, The Netherlands Eindhoven, The Netherlands [email protected] [email protected] ABSTRACT through unfamiliar worlds. Instead, for navigation, they use a strat- In this paper, we describe a nature-inspired optimization algorithm egy named Path Integration (PI). Bees are able to compute their based on bee foraging behavior. This algorithm combines the high present location from their past trajectory continuously and, as a performance of bee path-integration navigation with ant-like stig- consequence, can return to their starting point by choosing the di- mergic behavior in the form of landmarks. More precisely, each rect route rather than retracing their outbound trajectory [15, 21]. individual landmark can be created at any walkable state in the For recruitment, bees employ a direct strategy by dancing in the environment and contains a collection of direction markers with nest. Their dance communicates distance and direction towards a which visiting agents can find their way in an unknown environ- destination [27]. ment. A landmark can either be represented by an agent or any In previous research, we introduced a foraging algorithm in- other information distributing object (e.g., a RFID). Essentially, we spired by bees and compared it to an Ant System [17]. Our compar- implement ant recruitment behavior based on pheromone. How- ison showed that the bee-inspired, non-pheromone-based algorithm ever, instead of using attracting or repelling pheromone in every clearly outperformed the ant-inspired, pheromone-based algorithm state of the environment, we only update directional information at in relatively unobstructed environments; more precisely, the bee al- key locations in the environment.
    [Show full text]
  • Review the Sensory Ecology of Ocean Navigation
    1719 The Journal of Experimental Biology 211, 1719-1728 Published by The Company of Biologists 2008 doi:10.1242/jeb.015792 Review The sensory ecology of ocean navigation Kenneth J. Lohmann*, Catherine M. F. Lohmann and Courtney S. Endres Department of Biology, University of North Carolina, Chapel Hill, NC 27599, USA *Author for correspondence (e-mail: [email protected]) Accepted 31 March 2008 Summary How animals guide themselves across vast expanses of open ocean, sometimes to specific geographic areas, has remained an enduring mystery of behavioral biology. In this review we briefly contrast underwater oceanic navigation with terrestrial navigation and summarize the advantages and constraints of different approaches used to analyze animal navigation in the sea. In addition, we highlight studies and techniques that have begun to unravel the sensory cues that underlie navigation in sea turtles, salmon and other ocean migrants. Environmental signals of importance include geomagnetic, chemical and hydrodynamic cues, perhaps supplemented in some cases by celestial cues or other sources of information that remain to be discovered. An interesting similarity between sea turtles and salmon is that both have been hypothesized to complete long-distance reproductive migrations using navigational systems composed of two different suites of mechanisms that function sequentially over different spatial scales. The basic organization of navigation in these two groups of animals may be functionally similar, and perhaps also representative of other long-distance ocean navigators. Key words: navigation, orientation, migration, magnetic, hydrodynamic, chemical, olfactory, sea turtle, fish, whale, salmon. Introduction animals navigating through the ocean have access to a suite of Considerable progress has been made in characterizing the navigational cues which differs from that of their terrestrial mechanisms of orientation and navigation used by diverse animals.
    [Show full text]
  • Biological Inspirations for Distributed Robotics
    Biological Inspirations for Distributed Robotics Dr. Daisy Tang Outline Biological inspirations Understand two types of biological parallels Understand key ideas for distributed robotics obtained from study of biological systems Understand concept of stigmergy Understand use of stigmergy for tasks in collective robotics Biology vs. Multi-Robot Teams Movies of Some Animal Collectives School of fish http://www.youtube.com/watch?v=_tGOKngtkt4&feature=related Flock of birds http://www.youtube.com/watch?v=TL8diH-I9EQ Etc. Why Biological Systems? Key reasons: Animal behavior defines intelligence Animal behavior provides existence proof that intelligence is achievable Typical objects of study: Ants Bees Birds Fish Herding animals A Broad Classification of Animal Societies (Tinbergen, 1953) Societies that Differentiate Innate differentiation of blood relatives Strict division of work and social interaction Individuals: Exist for the good of society Are totally dependent on society Examples: Bees Ants, termites Stay Together A Typical Bee Colony Societies that Integrate Depend on the attraction of individual animals Exhibit loose division of labor Individuals: Integrate ways of behavior Thrive on support provided by society Are motivated by selfish interests Examples: Wolf, hunting dogs, etc. Bird colonies Come Together Parallels to Cooperative Robotics Which Approach To Choose? Differentiating approach : For tasks that require numerous repetitions of same activity over a fairly large area Examples: Waxing floor Removing barnacles off ships Collecting rock samples on Mars Integrating approach : For tasks that require several distinct subtasks Examples: Search and rescue Security, surveillance, or reconnaissance Key Ideas from Biological Inspiration Communication Auditory, chemical, tactile, electrical Direct, indirect, explicit, implicit Roles Strict division vs.
    [Show full text]
  • Flocking Boids with Geometric Vision, Perception and Recognition
    Flocking Boids with Geometric Vision, Perception and Recognition James Holland Sudhanshu Kumar Semwal Department of Computer Science Department of Computer Science University of Colorado at Colorado Springs University of Colorado at Colorado Springs Colorado Springs, CO, 80933-7150 Colorado Springs, CO, 80933-7150 [email protected] [email protected] ABSTRACT In the natural world, we see endless examples of the behavior known as flocking. From the perspective of graphics simulation, the mechanics of flocking has been reduced down to a few basic behavioral rules. Reynolds coined the term Boid to refer to any simulated flocking, and simulated flocks by collision avoidance, velocity matching, and flock centering. Though these rules have been given other names by various researchers, implementing them into a simulation generally yields good flocking behavior. Most implementations of flocking use a forward looking visual model in which the boids sees everything around it. Our work creates a more realistic model of avian vision by including the possibility of a variety of geometric vision ranges and simple visual recognition based on what boids can see. In addition, a perception algorithm has been implemented which can determine similarity between any two boids. This makes it possible to simulate different boids simultaneously. Results of our simulations are summarized. Keywords Flocking simulation, geometry, Simple perception and recognition algorithms. formations and then create algorithms that apply the 1. INTRODUCTION factors. From a biological view, flocking, schooling and herding behaviors arise from a variety of Flocking is a method for generating animations that motivations. By forming large groups, the average realistically mimic the movements of animals in number of encounters with predators for each nature.
    [Show full text]
  • Observing Animal Behaviour This Book Is Dedicated to the Memory of Niko Tinbergen (1907–1988) Observing Animal Behaviour
    Observing Animal Behaviour This book is dedicated to the memory of Niko Tinbergen (1907–1988) Observing Animal Behaviour Design and analysis of quantitative data Marian Stamp Dawkins Mary Snow Fellow in Biological Sciences Somerville College Oxford and Animal Behaviour Research Group Department of Zoology University of Oxford 1 3 Great Clarendon Street, Oxford OX2 6DP Oxford University Press is a department of the University of Oxford. It furthers the University’s objective of excellence in research, scholarship, and education by publishing worldwide in Oxford New York Auckland Cape Town Dar es Salaam Hong Kong Karachi Kuala Lumpur Madrid Melbourne Mexico City Nairobi New Delhi Shanghai Taipei Toronto With offices in Argentina Austria Brazil Chile Czech Republic France Greece Guatemala Hungary Italy Japan Poland Portugal Singapore South Korea Switzerland Thailand Turkey Ukraine Vietnam Oxford is a registered trade mark of Oxford University Press in the UK and in certain other countries Published in the United States by Oxford University Press Inc., New York © Marian Stamp Dawkins 2007 The moral rights of the author have been asserted Database right Oxford University Press (maker) First published 2007 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, without the prior permission in writing of Oxford University Press, or as expressly permitted by law, or under terms agreed with the appropriate reprographics rights organization. Enquiries concerning
    [Show full text]
  • ABSTRACT Title of Thesis: SWARM INTELLIGENCE and STIGMERGY
    ABSTRACT Title of Thesis: SWARM INTELLIGENCE AND STIGMERGY: ROBOTIC IMPLEMENTATION OF FORAGING BEHAVIO R Mark Russell Edelen, Master of Science, 2003 Thesis directed by: Professor Jaime F. Cárdenas -García Professor Amr M. Baz Dep artment of Mechanical Engineering Swarm intelligence in multi -robot systems has become an important area of research within collective robotics. Researchers have gained inspiration from biological systems and proposed a variety of industrial , commercial , and military robotics applications. In order to bridge the gap between theory and application, a strong focus is required on robotic implementation of swarm intelligence. To date, theoretical research and computer simulations in the field have dominate d, with few successful demonstrations of swarm -intelligent robotic systems. In this thesis , a study of intelligent foraging behavior via indirect communication between simple individual agents is presented . Models of foraging are reviewed and analyzed with respect to the system dynamics and dependence on important parameters . Computer simulations are also conducted to gain an understanding of foraging behavior in systems with large populations. Finally, a novel robotic implementation is presented. The experiment successfully demonstrates cooperative group foraging behavior without direct communication. Trail -laying and trail -following are employed to produce the required stigmergic cooperation. Real robots are shown to achieve increased task efficien cy, as a group, resulting from indirect interactions. Experimental results also confirm that trail -based group foraging systems can adapt to dynamic environments. SWARM INTELLIGENCE AND STIGMERGY: ROBOTIC IMPLEMENTATION OF FORAGING BEHAVIO R by Mark Russell Edelen Thesis submitted to the Faculty of the Graduate School of the University of Maryland, College Park in partial fulfillment of the requirements for the degree of Master of Science 2003 Advisory Committee: Professor Jaime F.
    [Show full text]
  • Collective Animal Navigation and Migratory Culture: from Theoretical Models to Empirical Evidence
    bioRxiv preprint doi: https://doi.org/10.1101/230219; this version posted December 7, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Manuscript SUBMITTED TO Philosophical TRANSACTIONS B Collective ANIMAL NAVIGATION AND MIGRATORY CULTURe: FROM THEORETICAL MODELS TO EMPIRICAL EVIDENCE AndrEW M. BerDAHL1,2*†, Albert B. Kao3* †, AndrEA Flack4,5, Peter A. H. WESTLEY6, EdwarD A. Codling7, IAIN D. Couzin5,8,9, Anthony I. Dell10,11, DorA BirO12* *For CORRespondence: [email protected] (AMB); 1Santa Fe Institute, Santa Fe, NM 87501, USA; 2School OF Aquatic & Fishery Sciences, [email protected] (ABK); University OF Washington, Seattle, WA 98195, USA; 3Department OF OrGANISMIC AND [email protected] (DB) Evolutionary Biology, HarvarD University, Cambridge, MA 02138, USA; 4Department OF †These AUTHORS CONTRIBUTED EQUALLY MigrATION AND Immuno-Ecology, Max Planck INSTITUTE FOR Ornithology, 78315 Radolfzell, TO THIS WORK Germany; 5Department OF Biology, University OF Konstanz, 78457 Konstanz, Germany; 6Department OF Fisheries, University OF Alaska Fairbanks, Fairbanks, AK 99775, USA; 7Department OF Mathematical Sciences, University OF Essex, Colchester, CO4 3SQ, UK; 8Department OF Collective Behaviour, Max Planck INSTITUTE FOR Ornithology, Konstanz, Germany; 9Chair OF Biodiversity & Collective Behaviour, University OF Konstanz, 78457 Konstanz, Germany; 10National GrEAT Rivers ResearCH AND Education Center, Alton, IL 62024, USA; 11Department OF Biology, WASHINGTON University IN St. Louis, St. Louis, MO 63130, USA.; 12Department OF Zoology, University OF Oxford, Oxford, OX1 3PS, UK AbstrACT Animals OFTEN TRAVEL IN GRoups, AND THEIR NAVIGATIONAL DECISIONS CAN BE INflUENCED BY SOCIAL INTERactions.
    [Show full text]
  • Multi‑Agent Foraging: State‑Of‑The‑Art and Research Challenges Ouarda Zedadra, Nicolas Jouandeau, Hamid Seridi, Giancarlo Fortino
    Multi‑Agent Foraging: state‑of‑the‑art and research challenges Ouarda Zedadra, Nicolas Jouandeau, Hamid Seridi, Giancarlo Fortino To cite this version: Ouarda Zedadra, Nicolas Jouandeau, Hamid Seridi, Giancarlo Fortino. Multi‑Agent Foraging: state‑of‑the‑art and research challenges. Complex Adaptive Systems Modeling, 2017, 5 (1), 10.1186/s40294-016-0041-8. hal-02317268 HAL Id: hal-02317268 https://hal.archives-ouvertes.fr/hal-02317268 Submitted on 17 Oct 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Zedadra et al. Complex Adapt Syst Model (2017) 5:3 DOI 10.1186/s40294-016-0041-8 REVIEW Open Access Multi‑Agent Foraging: state‑of‑the‑art and research challenges Ouarda Zedadra1* , Nicolas Jouandeau2, Hamid Seridi1 and Giancarlo Fortino3 *Correspondence: [email protected] Abstract 1 LabSTIC, 8 May 1945 Background: The foraging task is one of the canonical testbeds for cooperative robot- University, P.O. Box 401, 24000 Guelma, Algeria ics, in which a collection of robots has to search and transport objects to specific stor- Full list of author information age point(s). In this paper, we investigate the Multi-Agent Foraging (MAF) problem from is available at the end of the several perspectives that we analyze in depth.
    [Show full text]
  • Homing and Straying by Anadromous Salmonids: a Review of Mechanisms and Rates
    Rev Fish Biol Fisheries (2014) 24:333–368 DOI 10.1007/s11160-013-9334-6 REVIEWS Homing and straying by anadromous salmonids: a review of mechanisms and rates Matthew L. Keefer • Christopher C. Caudill Received: 10 July 2013 / Accepted: 16 November 2013 / Published online: 22 November 2013 Ó Springer Science+Business Media Dordrecht 2013 Abstract There is a long research history addressing Reported salmonid stray rates indicate that the olfactory imprinting, natal homing, and non-natal behavior varies among species, among life-history straying by anadromous salmon and trout (Salmoni- types, and among populations within species. Most dae). In undisturbed populations, adult straying is a strays enter sites near natal areas, but long-distance fundamental component of metapopulation biology, straying also occurs, especially in hatchery popula- facilitating genetic resilience, demographic stability, tions that were outplanted or transported as juveniles. recolonization, and range expansion into unexploited A majority of past studies has estimated straying as habitats. Unfortunately, salmonid hatcheries and other demographic losses from donor populations, but some human actions worldwide have affected straying in have estimated straying into recipient populations. ways that can negatively affect wild populations Most recipient-based estimates have substantiated through competitive interactions, reduced productiv- concerns that wild populations are vulnerable to ity and resiliency, hybridization and domestication swamping by abundant hatchery and farm-raised effects, and outbreeding depression. Reduced adult strays. straying is therefore an objective for many managed populations. Currently, there is considerable uncer- Keywords Imprinting Á Olfaction Á tainty about the range of ‘natural’ stray rates and about Oncorhynchus Á Orientation Á Philopatry Á Salmo which mechanisms precipitate straying in either wild or human-influenced fish.
    [Show full text]
  • UAV Deployment Using Two Levels of Stigmergy for Unstructured Environments
    applied sciences Article UAV Deployment Using Two Levels of Stigmergy for Unstructured Environments Fidel Aznar *,† , Maria del Mar Pujol López † and Ramón Rizo † Department of Computer Science and Artificial Intelligence, University of Alicante, San Vicente del Raspeig, 03690 Alicante, Spain; [email protected] (M.d.M.P.L.); [email protected] (R.R.) * Correspondence: fi[email protected] † These authors contributed equally to this work. Received: 29 September 2020; Accepted: 27 October 2020; Published: 30 October 2020 Abstract: This article will present two swarming behaviors for deployment in unstructured environments using unmanned aerial vehicles (UAVs). These behaviors will use stigmergy for communication. We found that there are currently few realistic deployment approaches that use stigmergy, due mainly to the difficulty of building transmitters and receivers for this type of communication. In this paper, we will provide the microscopic design of two behaviors with different technological and information requirements. We will compare them and also investigate how the number of agents influences the deployment. In this work, these behaviors will be exhaustively analyzed, taking into account different take-off time interval strategies, the number of collisions, and the time and energy required by the swarm. Numerous simulations will be conducted using unstructured maps generated at random, which will enable the establishment of the general functioning of the behaviors independently of the map used. Finally, we will show how both behaviors are capable of achieving the required deployment task in terms of covering time and energy consumed by the swarm. We will discuss how, depending on the type of map used, this task can be performed at a lower cost without using a more informed (but expensive) robotic swarm.
    [Show full text]