ABSTRACT Title of Thesis: SWARM INTELLIGENCE and STIGMERGY

Total Page:16

File Type:pdf, Size:1020Kb

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. Cárdenas-García, Co -chair Professor Amr M. Baz, Co -ch air Professor Balakumar Balachandran ©Copyright by Mark Russell Edelen 2003 Acknowledgements First and foremost, I would like to thank my heavenly Father and the Lord Jesus Christ, for making this possible. Without God’s supernatural intervention, I would never have persevered. All credit and praise belong to my God. I would like to thank Professors Amr Baz and Balakumar Balachandran for their assistance and support. A special thanks goes to Professor Jaime Cárdenas -García for all of his valuable advice and patience. I appreciate the freedom that he allowed me in pursuing a topic of my interest. That is a rare luxury, but he made it a reality. Lastly, thank you to everyone who played a role in proofreading this text, donating supplies, or simply supporting me while I labored. I want to thank you all for your understanding and selfless desire to help. ii Dedication You are my God, and I will give you thanks; You are my God, and I will exalt you. - Psalms 118:28 Whatever you do, work at it with all your heart, as working for the Lord, not for men, since you know that you will receive an inheritance from the Lord as a reward. It is the Lord Christ you are serving. - Colossians 3:23-24 Go to the ant, you sluggard! Consider her ways and be wise. - Proverbs 6:6 iii TABLE OF CONTENTS LIST OF TABLES ............................................................................................................ vii LIST OF FIGURES .........................................................................................................viii Chapter 1: Introduction....................................................................................................... 1 Chapter 2: Background – Swarm Robotics ......................................................................... 5 Section 2.1: Cooperative Mobile Robotics ............................................................. 5 Section 2.2: Ori gins of Swarm -Based Robotics ..................................................... 6 Section 2.3: Swarm Control Arc hitectures ............................................................. 8 Section 2.4: Communication................................................................................. 14 Section 2.5: Learning ............................................................................................ 16 Section 2.6: Morphologies .................................................................................... 17 Section 2.7: Cooperative Tasks in Mobile Ro botics ............................................. 23 Section 2.8: Conclu sion ........................................................................................ 34 Chapter 3: Biological Inspirations .................................................................................... 36 Section 3.1: Se lf -Organization in Biological Systems .......................................... 36 Section 3.2: Microscopic Organisms .................................................................... 43 Section 3.3: Social Insects .................................................................................... 44 Section 3.4: Birds, Fish, Mammals ....................................................................... 65 Section 3.5: Misconceptions of Self -Organization ............................................... 67 Chapter 4: Foraging Theory and System Modelling......................................................... 69 Section 4.1: Individ ual Agent Dynamics .............................................................. 69 Section 4.2: Foraging Theory and Task Efficiency .............................................. 71 iv Section 4.3: Mo deling Self -Organization ............................................................. 74 Section 4.4: Foraging Models ............................................................................... 78 Section 4.5: Model -Ba sed Predictions................................................................ 100 Chapter 5: Simulation ..................................................................................................... 102 Section 5.1: Why Simulate? ................................................................................ 102 Section 5.2: Introduction to StarLogo ................................................................. 103 Section 5.3: Program Description....................................................................... 104 Section 5.4: Simulated Scenarios........................................................................ 107 Sectio n 5.5: Results ............................................................................................. 110 Chapter 6: Experimental Design ..................................................................................... 150 Section 6.1: Experi mental Motivations and Goals ............................................. 150 Section 6.2: LEGO ® Mindstorms™ ................................................................... 153 Section 6.3: Disappearing Ink............................................................................. 158 Section 6.4: Mechanical Design ......................................................................... 161 Section 6.5: Behavior-based Programming ........................................................ 182 Section 6.6: Experimental Setup......................................................................... 190 Section 6.7: Experimental Cases ......................................................................... 195 Section 6.8: Results ............................................................................................. 197 Chapter 7: Analysis and Discussion ............................................................................... 211 Section 7.1: Disc ussion of Simulation Results ................................................... 211 Section 7.2: Discussion of Experimental Results ............................................... 218 Section 7.3: Compariso n of Results to Theoretical Predictions ......................... 224 Chapter 8: Conclusions................................................................................................... 228 v APPENDIX A – StarLogo Documentation .................................................................... 231 APPENDIX B – StarLogo Simulation Code .................................................................. 235 APPENDIX C – Bricx Command Center Documentation ............................................. 238 APPENDIX D – Not Quite C (NQC) Documentation.................................................... 239 APPENDIX E – NQC Program Code............................................................................. 247 APPENDIX F – LEGO Robot Assembly Instructions ................................................... 252 REFERENCES ............................................................................................................... 271 vi LIST OF TABLES Table 1. Robotic Locomotion Configurations ................................................................. 20 Table 2. Commercial Robot Platforms ............................................................................. 22 Table 3. Foraging Methods Used by Ants (adapted from [93], pp. 248-251) .................. 45 Table 4. Examples of self -organization in nature (adapted from [69], pp. 170 -171) ....... 66 Table 5. StarLogo Observer Procedures ......................................................................... 106 Table 6. Technical Specific ations for LEGO RCX [110]..............................................
Recommended publications
  • Relatedness, Parentage, and Philopatry Within a Natterer's Bat
    Population Ecology (2018) 60:361–370 https://doi.org/10.1007/s10144-018-0632-7 ORIGINAL ARTICLE Relatedness, parentage, and philopatry within a Natterer’s bat (Myotis nattereri) maternity colony David Daniel Scott1 · Emma S. M. Boston2 · Mathieu G. Lundy1 · Daniel J. Buckley2 · Yann Gager1 · Callum J. Chaplain1 · Emma C. Teeling2 · William Ian Montgomery1 · Paulo A. Prodöhl1 Received: 20 April 2017 / Accepted: 26 September 2018 / Published online: 10 October 2018 © The Author(s) 2018 Abstract Given their cryptic behaviour, it is often difficult to establish kinship within microchiropteran maternity colonies. This limits understanding of group formation within this highly social group. Following a concerted effort to comprehensively sample a Natterer’s bat (Myotis nattereri) maternity colony over two consecutive summers, we employed microsatellite DNA profiling to examine genetic relatedness among individuals. Resulting data were used to ascertain female kinship, parentage, mating strategies, and philopatry. Overall, despite evidence of female philopatry, relatedness was low both for adult females and juveniles of both sexes. The majority of individuals within the colony were found to be unrelated or distantly related. How- ever, parentage analysis indicates the existence of a number of maternal lineages (e.g., grandmother, mother, or daughter). There was no evidence suggesting that males born within the colony are mating with females of the same colony. Thus, in this species, males appear to be the dispersive sex. In the Natterer’s bat, colony formation is likely to be based on the benefits of group living, rather than kin selection. Keywords Chiroptera · Kinship · Natal philopatry · Parentage assignment Introduction of the environment (Schober 1984).
    [Show full text]
  • Dispersal, Philopatry, and the Role of Fissionfusion Dynamics In
    MARINE MAMMAL SCIENCE, **(*): ***–*** (*** 2012) C 2012 by the Society for Marine Mammalogy DOI: 10.1111/j.1748-7692.2011.00559.x Dispersal, philopatry, and the role of fission-fusion dynamics in bottlenose dolphins YI-JIUN JEAN TSAI and JANET MANN,1 Reiss Science Building, Rm 406, Department of Biology, Georgetown University, Washington, DC 20057, U.S.A. ABSTRACT In this quantitative study of locational and social dispersal at the individual level, we show that bottlenose dolphins (Tursiops sp.) continued to use their na- tal home ranges well into adulthood. Despite substantial home range overlap, mother–offspring associations decreased after weaning, particularly for sons. These data provide strong evidence for bisexual locational philopatry and mother–son avoidance in bottlenose dolphins. While bisexual locational philopatry offers the benefits of familiar social networks and foraging habitats, the costs of philopatry may be mitigated by reduced mother–offspring association, in which the risk of mother–daughter resource competition and mother–son mating is reduced. Our study highlights the advantages of high fission–fusion dynamics and longitudinal studies, and emphasizes the need for clarity when describing dispersal in this and other species. Key words: bottlenose dolphin, Tursiops sp., association, juvenile, philopatry, locational dispersal, mother-offspring, ranging, site fidelity, social dispersal. Dispersal is a key life history process that affects population demography, spatial distribution, and genetic structure, as well as individual
    [Show full text]
  • Swarming (Bulletin #404) (PDF)
    Swarming Apiculture Bulletin #404 Updated: 10/15 Swarming is a natural method of honeybee colonies to reproduce, resulting in the creation of a new honeybee colony in addition to the established colony. Contributing factors to swarming • Crowding - too many bees, food stores and no cell space for the queen to lay eggs in. • Poor air circulation • April-May is swarming season and healthy colonies develop strong swarm impulse. • Inclement weather - crowded bees confined by cold, wet weather will build queen cells and swarm out on the first sunny, warm day. All colonies in similar condition will swarm as soon as weather becomes favorable. • Large amount of drone brood in early spring is a precursor to strong swarm impulse. Catching the swarm A swarm generally emerges from the hive between 11:00AM and 1:00PM and settles close to the apiary for several hours. Allow the swarm to cluster for at least 30 minutes before placing the swarm in a single super with frames, bottom board and hive cover. Leave the hive for the remainder of the day to allow all the bees to enter, before moving to a permanent location. Examine the new colony after a week and then every two weeks from mid May to late June. Look for disease, brood abundance, brood pattern and overall condition of the colony. Add room where necessary, cut queen cells, or divide, to prevent other swarms to develop. Hived swarms have no stored food reserves and may go hungry when there is little forage. When feeding sugar syrup, add antibiotics as a precautionary measure.
    [Show full text]
  • Swarms and Swarm Intelligence
    SOFTWARE TECHNOLOGIES homogeneous. Intelligent swarms can also comprise heterogeneous elements Swarms from the outset, reflecting different capabilities as well as a possible social structure. and Swarm Researchers have used agent swarms as a computer modeling tech- nique and as a tool to study complex systems. Simulation examples include Intelligence bird swarms and business, economics, and ecological systems. In swarm sim- Michael G. Hinchey, Loyola College in Maryland ulations, each agent tries to maximize Roy Sterritt, University of Ulster its given parameters. Chris Rouff, Lockheed Martin Advanced Technology Laboratories In terms of bird swarms, each bird tries to find another to fly with, and then flies slightly higher to one side to reduce drag, with the birds eventually forming a flock. Other types of swarm simulations exhibit unlikely emergent behaviors, which are sums of simple Intelligent swarm technologies solve individual behaviors that form com- complex problems that traditional plex and often unexpected behaviors approaches cannot. when aggregated. Swarm intelligence Gerardo Beni and Jing Wang intro- duced the term swarm intelligence in a 1989 article (“Swarm Intelligence,” Proc. 7th Ann. Meeting of the Robotics e are all familiar with working together to achieve a goal Society of Japan, RSJ Press, 1989, pp. swarms in nature. The and produce significant results. 425-428). Swarm intelligence tech- word swarm conjures Swarms may operate on or under the niques (note the difference from intel- up images of large Earth’s surface, under water, or on ligent swarms) are population-based W groups of small insects other planets. stochastic methods used in combina- in which each member performs a torial optimization problems in which simple role, but the action produces SWARMS AND INTELLIGENCE the collective behavior of relatively sim- complex behavior as a whole.
    [Show full text]
  • What Is a Social Spider? “Sub”-Social Spiders Eusociality Social Definitions Natural History
    What is a Social Spider? • Generally accepted as living in colonies while having generational overlap and exhibiting cooperative brood care and nest maintenance Scott Trageser • Can also have reproductive division of labor and exhibit swarming behavior (Achaearanea wau) • Social hierarchies arise • Species and population dependant • Arguable if certain species qualify as eusocial “Sub”-Social Spiders Eusociality • Sociality also classified by territoriality and • There is cooperative brood-care so it is permanence not each one caring for their own offspring • Can be social on a seasonal basis and • There is an overlapping of generations so have an obligate solitary phase that the colony will sustain for a while, • Individuals can have established allowing offspring to assist parents during territories within the nest or can move their life freely • That there is a reproductive division of • Can have discrete webs connected to labor, i.e. not every individual reproduces other webs in the colony (cooperative?) equally in the group Social Definitions Natural History • Solitary: Showing none of the three features mentioned in the previous slide (most insects) • 23 of over 39,000 spider species are • Sub-social: The adults care for their own young for “social” some period of time (cockroaches) • Many other species are classified as • Communal: Insects use the same composite nest without cooperation in brood care (digger bees) “sub”-social • Quasi-social: Use the same nest and also show cooperative brood care (Euglossine bees and social • Tropical origin. Latitudinal and elevational spiders) distribution constrictions due to prey • Semi-social: in addition to the features in type/abundance quasisocial, they also have a worker caste (Halictid bees) • Emigrate (swarm) after courtship and • Eusocial: In addition to the features of semisocial, copulation but prior to oviposition there is overlap in generations (Honey bees).
    [Show full text]
  • The Influence of Density in Population Dynamics with Strong and Weak Allee Effect
    Keya et al. J Egypt Math Soc (2021) 29:4 https://doi.org/10.1186/s42787-021-00114-x Journal of the Egyptian Mathematical Society ORIGINAL RESEARCH Open Access The infuence of density in population dynamics with strong and weak Allee efect Kamrun Nahar Keya1, Md. Kamrujjaman2* and Md. Shafqul Islam3 *Correspondence: [email protected] Abstract 2 Department In this paper, we consider a reaction–difusion model in population dynamics and of Mathematics, University of Dhaka, Dhaka 1000, study the impact of diferent types of Allee efects with logistic growth in the hetero- Bangladesh geneous closed region. For strong Allee efects, usually, species unconditionally die out Full list of author information and an extinction-survival situation occurs when the efect is weak according to the is available at the end of the article resource and sparse functions. In particular, we study the impact of the multiplicative Allee efect in classical difusion when the sparsity is either positive or negative. Nega- tive sparsity implies a weak Allee efect, and the population survives in some domain and diverges otherwise. Positive sparsity gives a strong Allee efect, and the popula- tion extinct without any condition. The infuence of Allee efects on the existence and persistence of positive steady states as well as global bifurcation diagrams is presented. The method of sub-super solutions is used for analyzing equations. The stability condi- tions and the region of positive solutions (multiple solutions may exist) are presented. When the difusion is absent, we consider the model with and without harvesting, which are initial value problems (IVPs) and study the local stability analysis and present bifurcation analysis.
    [Show full text]
  • Global Participatory Acts and the State
    STRENGTHENING THE STATE THROUGH DISSENT: GLOBAL PARTICIPATORY ACTS AND THE STATE By Patricia Camilien, université Quisqueya INTRODUCTION In Alvin Toffler‟s Future Shock (1970), three types of men coexist on the planet. Men of the present, living in the here and now as they are carried away by the waves and currents of the world; they are rather vulnerable to "future shock". Men of the past, who have remained in a past time long gone, are living in the twentieth century but are still in the twelfth. Men of the future, informed, insightful and perceptive, who are already surfing on the major trends of tomorrow. In today's world, these men of the future are part of a transnational and nomadic elite helped by the increasing liberalization of trade and the disappearance of borders – at the least for them. Transnational companies like oil brokerage firm Trafigura i – made globally (in) famous in 2010 by a journalist‟s tweet about a file hosted on WikiLeaks – exemplify this (future) global world. They enjoy the best of corporate law around the world and diversify their operations accordingly. This use of different national laws according to the benefits they offer is not limited to transnational corporations and their owners, but now extends to individuals who, while they are not billionaires in dollars, are so in reticular connections. Using global participatory acts (heretofore participactions), they seem to be the ones to help the people move from the present to the future. Moral and social entrepreneurs, they are the vanguard of the future changes in society they are trying to drive through networked actions as relayed by mass self- communication.
    [Show full text]
  • Inference of Causal Information Flow in Collective Animal Behavior Warren M
    IEEE TMBMC SPECIAL ISSUE 1 Inference of Causal Information Flow in Collective Animal Behavior Warren M. Lord, Jie Sun, Nicholas T. Ouellette, and Erik M. Bollt Abstract—Understanding and even defining what constitutes study how the microscopic interactions scale up to give rise animal interactions remains a challenging problem. Correlational to the macroscopic properties [26]. tools may be inappropriate for detecting communication be- The third of these goals—how the microscopic individual- tween a set of many agents exhibiting nonlinear behavior. A different approach is to define coordinated motions in terms of to-individual interactions determine the macroscopic group an information theoretic channel of direct causal information behavior—has arguably received the most scientific attention flow. In this work, we present an application of the optimal to date, due to the availability of simple models of collective causation entropy (oCSE) principle to identify such channels behavior that are easy to simulate on computers, such as the between insects engaged in a type of collective motion called classic Reynolds [27], Vicsek [26], and Couzin [28] models. swarming. The oCSE algorithm infers channels of direct causal inference between insects from time series describing spatial From these kinds of studies, a significant amount is known movements. The time series are discovered by an experimental about the nature of the emergence of macroscopic patterns protocol of optical tracking. The collection of channels infered and ordering in active, collective systems [29]. But in argu- by oCSE describes a network of information flow within the ing that such simple models accurately describe real animal swarm. We find that information channels with a long spatial behavior, one must implicitly make the assumption that the range are more common than expected under the assumption that causal information flows should be spatially localized.
    [Show full text]
  • 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]
  • Swarm Intelligence
    Swarm Intelligence Leen-Kiat Soh Computer Science & Engineering University of Nebraska Lincoln, NE 68588-0115 [email protected] http://www.cse.unl.edu/agents Introduction • Swarm intelligence was originally used in the context of cellular robotic systems to describe the self-organization of simple mechanical agents through nearest-neighbor interaction • It was later extended to include “any attempt to design algorithms or distributed problem-solving devices inspired by the collective behavior of social insect colonies and other animal societies” • This includes the behaviors of certain ants, honeybees, wasps, cockroaches, beetles, caterpillars, and termites Introduction 2 • Many aspects of the collective activities of social insects, such as ants, are self-organizing • Complex group behavior emerges from the interactions of individuals who exhibit simple behaviors by themselves: finding food and building a nest • Self-organization come about from interactions based entirely on local information • Local decisions, global coherence • Emergent behaviors, self-organization Videos • https://www.youtube.com/watch?v=dDsmbwOrHJs • https://www.youtube.com/watch?v=QbUPfMXXQIY • https://www.youtube.com/watch?v=M028vafB0l8 Why Not Centralized Approach? • Requires that each agent interacts with every other agent • Do not possess (environmental) obstacle avoidance capabilities • Lead to irregular fragmentation and/or collapse • Unbounded (externally predetermined) forces are used for collision avoidance • Do not possess distributed tracking (or migration)
    [Show full text]
  • Minding the Body Interacting Socially Through Embodied Action
    Linköping Studies in Science and Technology Dissertation No. 1112 Minding the Body Interacting socially through embodied action by Jessica Lindblom Department of Computer and Information Science Linköpings universitet SE-581 83 Linköping, Sweden Linköping 2007 © Jessica Lindblom 2007 Cover designed by Christine Olsson ISBN 978-91-85831-48-7 ISSN 0345-7524 Printed by UniTryck, Linköping 2007 Abstract This dissertation clarifies the role and relevance of the body in social interaction and cognition from an embodied cognitive science perspective. Theories of embodied cognition have during the past two decades offered a radical shift in explanations of the human mind, from traditional computationalism which considers cognition in terms of internal symbolic representations and computational processes, to emphasizing the way cognition is shaped by the body and its sensorimotor interaction with the surrounding social and material world. This thesis develops a framework for the embodied nature of social interaction and cognition, which is based on an interdisciplinary approach that ranges historically in time and across different disciplines. It includes work in cognitive science, artificial intelligence, phenomenology, ethology, developmental psychology, neuroscience, social psychology, linguistics, communication, and gesture studies. The theoretical framework presents a thorough and integrated understanding that supports and explains the embodied nature of social interaction and cognition. It is argued that embodiment is the part and parcel of social interaction and cognition in the most general and specific ways, in which dynamically embodied actions themselves have meaning and agency. The framework is illustrated by empirical work that provides some detailed observational fieldwork on embodied actions captured in three different episodes of spontaneous social interaction in situ.
    [Show full text]
  • Wolf Family Values
    Wolf family values The exquisitely balanced social life of the wolf has implications far beyond the pack, says Sharon Levy ORDON HABER was tracking a wolf pack Wolf Project. Despite many thousands of he had known for over 40 years when his hours spent in the field, Haber published G plane crashed on a remote stretch of the little peer-reviewed documentation of his Toklat river in Denali national park, Alaska, work. Now, however, in the months following last October. The fatal accident silenced one his sudden death, Smith and other wolf of the most outspoken and controversial biologists have reported findings that support advocates for wolf protection. Haber, an some of Haber’s ideas. independent biologist, had spent a lifetime Once upon a time, folklore shaped our studying the behaviour and ecology of wolves thinking about wolves. It is only in the past and his passion for the animals was obvious. two decades that biologists have started to “I am still in awe of what I see out there,” he build a clearer picture of wolf ecology (see wrote on his website. “Wolves enliven the “Beyond myth and legend”, page 42). Instead northern mountains, forests, and tundra like of seeing rogue man-eaters and savage packs, no other creature, helping to enrich our stay we now understand that wolves have evolved on the planet simply by their presence as other to live in extended family groups that include Few places remain highly advanced societies in our midst.” a breeding pair – typically two strong, where wolves can live His opposition to hunting was equally experienced individuals – along with several as nature intended intense.
    [Show full text]