Biological Inspirations for Distributed Robotics
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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. -
Interactive Robots in Experimental Biology 3 4 5 6 Jens Krause1,2, Alan F.T
1 2 Interactive Robots in Experimental Biology 3 4 5 6 Jens Krause1,2, Alan F.T. Winfield3 & Jean-Louis Deneubourg4 7 8 9 10 11 12 1Leibniz-Institute of Freshwater Ecology and Inland Fisheries, Department of Biology and 13 Ecology of Fishes, 12587 Berlin, Germany; 14 2Humboldt-University of Berlin, Department for Crop and Animal Sciences, Philippstrasse 15 13, 10115 Berlin, Germany; 16 3Bristol Robotics Laboratory, University of the West of England, Coldharbour Lane, Bristol 17 BS16 1QY, UK; 18 4Unit of Social Ecology, Campus Plaine - CP 231, Université libre de Bruxelles, Bd du 19 Triomphe, B-1050 Brussels - Belgium 20 21 22 23 24 25 26 27 28 Corresponding author: Krause, J. ([email protected]), Leibniz Institute of Freshwater 29 Ecology and Inland Fisheries, Department of the Biology and Ecology of Fishes, 30 Müggelseedamm 310, 12587 Berlin, Germany. 31 32 33 1 33 Interactive robots have the potential to revolutionise the study of social behaviour because 34 they provide a number of methodological advances. In interactions with live animals the 35 behaviour of robots can be standardised, morphology and behaviour can be decoupled (so that 36 different morphologies and behavioural strategies can be combined), behaviour can be 37 manipulated in complex interaction sequences and models of behaviour can be embodied by 38 the robot and thereby be tested. Furthermore, robots can be used as demonstrators in 39 experiments on social learning. The opportunities that robots create for new experimental 40 approaches have far-reaching consequences for research in fields such as mate choice, 41 cooperation, social learning, personality studies and collective behaviour. -
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. -
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. -
Malte Andersson 30.10.2019
Social evolution, and levels of selection Malte Andersson 30.10.2019 1 Social evolution, Malte Andersson, 30.10. 2019 Fitness: an individual’s expected genetic contribution to the next generation Direct fitness: numbers of surviving own offspring A behaviour leading to higher fitness is favored by selection and will spread over the generations 2 1 Mobbing a raptor is risky for a crow 3 A honey bee dies after stinging an enemy 4 2 Costly helping: how can it evolve and persist? Does it benefit the donor of help via: 1) direct fitness (individual selection) ? 2) delayed direct fitness, by reciprocity ? 3) donor’s relatives: kin selection ? 4) a larger community: group selection ? These questions are debated. They were first asked by Darwin (1859), about sterile workers in eusocial insects. Some individuals can not reproduce, but offer their lives for the colony (for instance honey bee workers). Can natural selection lead to such behaviour? 5 Altruism (meaning in ecology): Helpful behavior that raises the recipient’s but lowers the donor’s direct fitness 6 3 Alarm signals - selfish or altruistic? Black-throated Shrike-Tanager, Springbok stotting, Kalahari Belize Stotting tells a predator it is detected Warning flock members, also using by a gazelle in good condition. Better the signal for own feeding advantage to hunt another prey (Caro 1986) 7 Is sentinel behavior in meerkats altruistic? Probably not. Sentinels are safer and have a direct benefit from their behavior. (Clutton-Brock et al. 1999) 8 4 0.5 0.4 0.3 Females 0.2 Males 0.1 0 Without close With With offspring genetic relatives nondescendant relatives Black-tailed prairie dogs Individuals give alarm calls mainly with relatives (offspring and others). -
Animating Predator and Prey Fish Interactions
Georgia State University ScholarWorks @ Georgia State University Computer Science Dissertations Department of Computer Science 5-6-2019 Animating Predator and Prey Fish Interactions Sahithi Podila Follow this and additional works at: https://scholarworks.gsu.edu/cs_diss Recommended Citation Podila, Sahithi, "Animating Predator and Prey Fish Interactions." Dissertation, Georgia State University, 2019. https://scholarworks.gsu.edu/cs_diss/149 This Dissertation is brought to you for free and open access by the Department of Computer Science at ScholarWorks @ Georgia State University. It has been accepted for inclusion in Computer Science Dissertations by an authorized administrator of ScholarWorks @ Georgia State University. For more information, please contact [email protected]. ANIMATING PREDATOR AND PREY FISH INTERACTIONS by SAHITHI PODILA Under the Direction of Ying Zhu, PhD ABSTRACT Schooling behavior is one of the most salient social and group activities among fish. They form schools for social reasons like foraging, mating and escaping from predators. Animating a school of fish is difficult because they are large in number, often swim in distinctive patterns that is they take the shape of long thin lines, squares, ovals or amoeboid and exhibit complex coordinated patterns especially when they are attacked by a predator. Previous work in computer graphics has not provided satisfactory models to simulate the many distinctive interactions between a school of prey fish and their predator, how does a predator pick its target? and how does a school of fish react to such attacks? This dissertation presents a method to simulate interactions between prey fish and predator fish in the 3D world based on the biological research findings. -
Expert Assessment of Stigmergy: a Report for the Department of National Defence
Expert Assessment of Stigmergy: A Report for the Department of National Defence Contract No. W7714-040899/003/SV File No. 011 sv.W7714-040899 Client Reference No.: W7714-4-0899 Requisition No. W7714-040899 Contact Info. Tony White Associate Professor School of Computer Science Room 5302 Herzberg Building Carleton University 1125 Colonel By Drive Ottawa, Ontario K1S 5B6 (Office) 613-520-2600 x2208 (Cell) 613-612-2708 [email protected] http://www.scs.carleton.ca/~arpwhite Expert Assessment of Stigmergy Abstract This report describes the current state of research in the area known as Swarm Intelligence. Swarm Intelligence relies upon stigmergic principles in order to solve complex problems using only simple agents. Swarm Intelligence has been receiving increasing attention over the last 10 years as a result of the acknowledgement of the success of social insect systems in solving complex problems without the need for central control or global information. In swarm- based problem solving, a solution emerges as a result of the collective action of the members of the swarm, often using principles of communication known as stigmergy. The individual behaviours of swarm members do not indicate the nature of the emergent collective behaviour and the solution process is generally very robust to the loss of individual swarm members. This report describes the general principles for swarm-based problem solving, the way in which stigmergy is employed, and presents a number of high level algorithms that have proven utility in solving hard optimization and control problems. Useful tools for the modelling and investigation of swarm-based systems are then briefly described. -
The Ecology of Mutualism
Annual Reviews www.annualreviews.org/aronline AngRev. Ecol. Syst. 1982.13:315--47 Copyright©1982 by Annual Reviews lnc. All rightsreserved THE ECOLOGY OF MUTUALISM Douglas 1t. Boucher Departementdes sciences biologiques, Universit~ du Quebec~ Montreal, C. P. 8888, Suet. A, Montreal, Quebec, CanadaH3C 3P8 Sam James Departmentof Ecologyand Evolutionary Biology, University of Michigan, Ann Arbor, Michigan, USA48109 Kathleen H. Keeler School of Life Sciences, University of Nebraska,Lincoln, Nebraska,USA 68588 INTRODUCTION Elementaryecology texts tell us that organismsinteract in three fundamen- tal ways, generally given the namescompetition, predation, and mutualism. The third memberhas gotten short shrift (264), and even its nameis not generally agreed on. Terms that may be considered synonyms,in whole or part, are symbiosis, commensalism,cooperation, protocooperation, mutual aid, facilitation, reciprocal altruism, and entraide. Weuse the term mutual- by University of Kanas-Lawrence & Edwards on 09/26/05. For personal use only. ism, defined as "an interaction betweenspecies that is beneficial to both," Annu. Rev. Ecol. Syst. 1982.13:315-347. Downloaded from arjournals.annualreviews.org since it has both historical priority (311) and general currency. Symbiosis is "the living together of two organismsin close association," and modifiers are used to specify dependenceon the interaction (facultative or obligate) and the range of species that can take part (oligophilic or polyphilic). We make the normal apologies concerning forcing continuous variation and diverse interactions into simple dichotomousclassifications, for these and all subsequentdefinitions. Thus mutualism can be defined, in brief, as a -b/q- interaction, while competition, predation, and eommensalismare respectively -/-, -/q-, and -t-/0. There remains, however,the question of howto define "benefit to the 315 0066-4162/82/1120-0315 $02.00 Annual Reviews www.annualreviews.org/aronline 316 BOUCHER, JAMES & KEELER species" without evoking group selection. -
Basic Hunting Booklet
Basic Hunting Passing on a time-honored tradition Kentucky Department of Fish and Wildlife Resources From the Commissioner. The North American Model of Wildlife Management depends on hunters. This model incorporates principles of conservation from the past 100 years and was formally adopted in 2002. Over the years the number of hunters has decreased. Fish and Wildlife agencies from across the nation view this as a major problem. Many factors contrib- ute to this decline, including habitat loss, a growing urban population, and increased competition for leisure time. We do have one key ele- ment upon which we can rely: the people who enjoy the opportunity to step outside and enjoy shooting and hunting. Kentucky’s Department of Fish and Wildlife Resources has worked for several decades to give the shooter and hunter every pos- sible opportunity. We have developed shooting ranges, assisted land- owners in improving habitat, and enhanced the quality of the hunt for numerous species. In addition, we strive to provide Wildlife Manage- ment Areas for public use. In fact, the amount of land the department manages has more than doubled in the last 10 years. You and your family and friends are essential to the future of hunting. Each new hunter must have consistent support to continue to grow and expand his or her interest. I think you will find this expe- rience extremely rewarding for everyone involved. In a tribute to his father, Alan Jackson stated in a song about boating that it was “…a piece of my childhood that will never be forgotten.” I feel your involve- ment in hunting with your friends and family will fall in that “special memory” category. -
Interaction Ruling Animal Collective Behavior Depends on Topological Rather Than Metric Distance: Evidence from a Field Study
Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study M. Ballerini*†, N. Cabibbo‡§, R. Candelier‡¶, A. Cavagna*ʈ**, E. Cisbani†, I. Giardina*ʈ, V. Lecomte††‡‡, A. Orlandi*, G. Parisi*‡§**, A. Procaccini*‡, and M. Viale‡§§, and V. Zdravkovic* *Centre for Statistical Mechanics and Complexity (SMC), Consiglio Nazionale delle Ricerche-Istituto Nazionale per la Fisica della Materia, ‡Dipartimento di Fisica, and §Sezione Instituto Nazionale di Fisica Nucleare, Universita’ di Roma ‘‘La Sapienza,’’ Piazzale Aldo Moro 2, 00185 Roma, Italy; †Istituto Superiore di Sanita’, viale Regina Elena 299, 00161 Roma, Italy; ʈIstituto dei Sistemi Complessi (ISC), Consiglio Nazionale delle Ricerche, via dei Taurini 19, 00185 Roma, Italy; and ††Laboratoire Matie`re et Syste`mes Complexes, (Centre National de la Recherche Scientifique Unite Mixte de Recherche 7057), Universite´Paris VII, 10 rue Alice Domon et Le´onie Duquet, 75205 Paris Cedex 13, France Contributed by G. Parisi, December 4, 2007 (sent for review September 25, 2007) Numerical models indicate that collective animal behavior may no bird remains isolated, and soon the flock reforms as whole. emerge from simple local rules of interaction among the individ- The question we want to answer is ‘‘what kind of interaction uals. However, very little is known about the nature of such maintains cohesion in such a robust way?’’ interaction, so that models and theories mostly rely on aprioristic To grant cohesion, models make the sound assumption that assumptions. By reconstructing the three-dimensional positions of individuals align and attract each other, and that such interaction individual birds in airborne flocks of a few thousand members, we decays with increasing distance between individuals. -
Stigmergy As a Universal Coordination Mechanism: Components, Varieties and Applications
to appear in T. Lewis & L. Marsh (Eds.), Human Stigmergy: Theoretical Developments and New Applications. Springer. Stigmergy as a Universal Coordination Mechanism: components, varieties and applications Francis Heylighen Evolution, Complexity and Cognition group Vrije Universiteit Brussel Abstract: The concept of stigmergy has been used to analyze self-organizing activities in an ever-widening range of domains, including social insects, robotics, web communities, and human society. Yet, it is still poorly understood, and as such its full power remains underappreciated. The present paper clarifies the issue by defining stigmergy as a mechanism of indirect coordination in which the trace left by an action in a medium stimulates a subsequent action. It then analyses the fundamental concepts used in the definition: action, agent, medium, trace and coordination. Stigmergy enables complex, coordinated activity without any need for planning, control, communication, simultaneous presence, or even mutual awareness. This makes the concept applicable to a very broad variety of cases, from chemical reactions to individual cognition and Internet-supported collaboration in Wikipedia. The paper classifies different varieties of stigmergy according to general aspects (number of agents, scope, persistence, sematectonic vs. marker-based, and quantitative vs. qualitative), while emphasizing the fundamental continuity between these cases. This continuity can be understood from a non-linear dynamics that lets more complex forms of coordination evolve out of simpler ones. The paper concludes with two specifically human applications, cognition and cooperation, suggesting that without stigmergy these phenomena may never have evolved in the first place. Past, present and future of the “stigmergy” concept The concept of stigmergy was introduced by the French entomologist Pierre-Paul Grassé (Grassé, 1959) to describe a mechanism of coordination used by insects. -
Diffusion of Individual Birds in Starling Flocks
Diffusion of individual birds in starling flocks A. Cavagna♯,♭, S. M. Duarte Queir´os♯, I. Giardina♯,♭, F. Stefanini† and M. Viale♯,♭ ♯ Istituto dei Sistemi Complessi, UOS Sapienza, CNR, via dei Taurini 19, 00185 Roma, Italy ♭ Dipartimento di Fisica, Universit`aSapienza, P.le Aldo Moro 2, 00185 Roma, Italy † Institute of Neuroinformatics, University of Zurich and ETH Zurich, Winterthurerstrasse, 190 CH-8057, Zurich, Switzerland Abstract Flocking is a paradigmatic example of collective animal behaviour, where global order emerges out of self-organization. Each individual has a tendency to align its flight direction with those of neighbours, and such a simple form of interaction produces a state of collective motion of the group. As compared to other cases of collective ordering, a crucial feature of animal groups is that the interaction network is not fixed in time, as each individual moves and continuously changes its neighbours. The possibility to exchange neighbours strongly enhances the stability of global ordering and the way information is propagated through the group. Here, we assess the relevance of this mechanism in large flocks of starlings (Sturnus vulgaris). We find that birds move faster than Brownian walkers both with respect to the centre of mass of the flock, and with respect to each other. Moreover, this behaviour is strongly anisotropic with respect to the direction of motion of the flock. We also measure the amount of neighbours reshuffling and find that neighbours change in time exclusively as a consequence of the random fluctuations in the individual motion, so that no specific mechanism to keep one’s neighbours seems to be enforced.