DOCSLIB.ORG

A Survey on Efficient and Scalable Method for Learning Collective

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A Survey on Efficient and Scalable Method for Learning Collective 21705 Umesh B.Shingote et al./ Elixir Comp. Sci. & Engg. 67 (2014) 21705-21709 Available online at www.elixirpublishers.com (Elixir International Journal) Computer Science and Engineering Elixir Comp. Sci. & Engg. 67 (2014) 21705-21709 A survey on Efficient and Scalable method for Learning Collective Behavior Umesh B.Shingote* and Setu Chaturvedi Department of Computer Science & Engineering, Technocrats Institute of Technology, Bhopal, Madhya Pradesh, India. ARTICLE INFO ABSTRACT Article history: For the many people in world social networks are playing major role day to day life. Received: 27 January 2014; Depending on the user’s behavior and interaction with each other, the social networking Received in revised form: sites are reshaped. Growing interest and development of social network sites like Facebook, 7 February 2014; Twitter, Flicker, YouTube etc. imposing many research challenges. And hence this is Accepted: 13 February 2014; allowing researchers to do many research studies using data mining concepts. The main challenge of such online social networking websites is to find out the individuals behavior Keywords over social network. Understanding the user’s behavior on social networking websites is Social networks, called as collective behavior. There are many data mining techniques presented to identify Users Behavior, the behavior of individuals. Such methods of collective behavior allows to learn and predict Learning, the users online behavior and based on it assign the appropriate label to actor in network. Prediction of behavior, But the another main problem occurs in such methods is the networks scalability due to k-means variant, which this systems becomes poor in performance and many not be work if the network size Clustering. is too big. To overcome this problem we need to have scalable learning of collective behavior to deal with any size of social networks. Recently one such method presented, in this method an edge-centric clustering technique is presented to extract social network dimensions. With sparse social dimensions, the proposed approach can efficiently handle networks of any size. In this paper we are presenting the detailed discussion on this method. © 2014 Elixir All rights reserved Introduction normally of colossal size, involving hundreds of thousands of Social Media allowing more connectivity and interaction actors. The scale of these networks entails scalable learning of between web users and it encourages contributions and feedback models for collective behavior prediction. from anyone who is a member of any virtual community. Social To address the scalability issue, new method introduced an media will alter new mass cooperative behaviors that unlock the edge-centric clustering scheme to extract sparse social ability of the collective and deliver new ways to enterprise dimensions [1].With sparse social dimensions, the proposed results. Enterprises will use these collective behaviors because approach can efficiently handle networks of millions of actors the link between business worth and social media technologies. while demonstrating a comparable prediction performance to Enterprises will use them to look at a target community and other non-scalable methods. Social media facilitate people of all formulate new ways in which folks will move to realize walks of life to connect to each other. In this paper we are enterprise worth. In gift World Social media facilitate folks of presenting and discussing the all algorithms involved in this all walks of life to attach to every different. We tend to study study with its practical evaluation. however networks in social media will facilitate predict some Collective behavior refers to the behaviors of individuals in kinds of human behavior and individual preference. In social a social networking environment, but it is not simply the media, countless actors in an exceedingly network area unit the aggregation of individual behaviors. In a connected norm. With this large variety of actors, the scale cannot even be environment, individuals’ behaviors tend to be interdependent, command in memory, inflicting significant issue concerning the influenced by the behavior of friends. This leads to behavior quantifiability. As an example, standard content- sharing sites correlation between connected users. Take marketing as an like Del.icio.us, Flickr and YouTube enable users to transfer, tag example: if our friends buy something, there is a better-than- and comment differing types of contents (bookmarks, photos, average chance that we will buy it, too. This behavior videos).Users registered at these sites may become friends, an correlation can also be explained by homophile. The recent addict or follower of others. The prolific and swollen use of boom of social media enables us to study collective behavior on social media has flipped on-line interactions into a significant a a large scale. Here, behaviors include a broad range of actions: part of human expertise. joining a group, connecting to a person, clicking on an ad, In this work, we tend to study however networks in social becoming interested in certain topics, dating people of a certain media will facilitate predict some human behaviors and type, etc. In this work, we attempt to leverage the behavior individual preferences. Above all, given the behavior of some correlation presented in a social network in order to predict people in an exceedingly network, how can we infer the collective behavior in social media. In next section sections we behavior of other individuals in the same social network [1].This will discuss the algorithms those are presented to overcome the study will facilitate higher perceive activity patterns of users in limitations of existing methods by improving the network social media for applications like social advertising and scalability. Examples of Behavior, Joining a sports club, buying recommendation. A social-dimension-based approach has been some products, Becoming interested in a topic voting for a shown effective in addressing the heterogeneity of connections presidential candidate. presented in social media. But the networks in social media are Tele: E-mail addresses: [email protected] © 2014 Elixir All rights reserved 21706 Umesh B.Shingote et al./ Elixir Comp. Sci. & Engg. 67 (2014) 21705-21709 Material and Methodology • BiComponents is very efficient and scalable. There are many methods presented to learn and predict the Disadvantage: collective behavior of users over online social networks. In [1], • BiComponents gives output highly imbalanced communities. author presented all the related works and methods for learning • It fails to extract informative social dimension for collective behavior. Solution for the problem of scalability by classification. using the new data mining methods is provided. Below are some • This technique yields poor performance. of the methods presented previously [1].Classifications with Node Clustering (NodeCluster) networked instances are known as within-network classification, Note that social dimensions allow one actor to be involved or a special case of relational learning [2, 3]. But network tends in multiple affiliations. As a proof of concept, we also examine to present heterogeneous relations, and the Markov assumption the case when each actor is associated with only one affiliation. can only capture the local dependency. In [4, 5], there is model Essentially, we construct social dimensions based on node network connections or class labels based on latent groups. partition. A similar idea has been adopted in a latent group Similar idea is also adopted to differentiate heterogeneous model [21] for efficient inference. To be fair, we adopt k-means relations in a network by extracting social dimensions to clustering to partition nodes of a network into disjoint sets, and represent the potential affiliations of actors in a network [6]. convert the node clustering result into a set of social dimensions. Modularity Maximization (ModMax) Then, SVM is utilized for discriminative learning. For Some researchers presented methods to conduct soft convenience, this method is denoted as NodeCluster. clustering for graphs like modularity maximization [8]. Advantage Probabilistic methods are also developed [9, 10]. Special variant • SVM is used for discriminative learning. of modularity maximization is adopted to extract social Disadvantage dimension. Social dimension corresponds to the top • Each actor to be involved in only one affiliation, yielding in eigenvectors of a modularity matrix. A disadvantage with soft inferior performance than EdgeCluster. clustering such as modularity maximization is that the resultant Edge Clustering (EdgeCluster) social dimensions are dense, posing thorny computational Consequently, it is imperative to develop scalable Methods challenges for the extraction of extraction of social dimensions that can handle large-scale networks efficiently without and discriminative learning. The modularity maximization extensive memory requirements. Next, we elucidate on an edge- requires the computation of the top eigenvectors of a modularity centric clustering scheme to extract sparse social dimensions. matrix which is of size n × n where n is the number of actors in With such a scheme, we can also update the social dimensions a network. When the network scales to millions of actors, the efficiently when new nodes or new edges arrive. In a huge eigenvector computation becomes a daunting task. Though the network, large number
Load more

Recommended publications
  • A Complex Adaptive Systems Perspective to Appreciative Inquiry: a Theoretical Analysis Payam Saadat George Fox University
    http://journals.sfu.ca/abr ADVANCES IN BUSINESS RESEARCH 2015, Volume 6, pages 1-13 A Complex Adaptive Systems Perspective to Appreciative Inquiry: A Theoretical Analysis Payam Saadat George Fox University Appreciate inquiry is utilized to facilitate organizational change by encouraging stakeholders to explore positives and generative capacities within their organization. In the literature, analysis of the effectiveness of AI is confined to psychological and managerial explanations such as highlighting the promotion of positive mindset and collective organizational planning. This paper will discuss a complex adaptive systems (CAS) perspective and present a new model for understanding the functionality of AI. The emphasis of this paper is placed on exploring the effects of AI on the behavior and interactions of agents/employees related to how they cope with change. An analysis of AI’s functionality through the lens of CAS reveals two critical insights: a) AI enhances adaptability to change by strengthening communication among agents, which in turn fosters the emergence of effective team arrangements and a more rapid collective response to change and b) AI possesses the potential to generate a collective memory for social systems within an organization. Furthermore, a systematic analysis of AI indicates a close connection between this method and CAS-based styles of management. This paper concludes by suggesting that AI might represent a potential method with the capacity to place organizational teams at the edge of chaos. Keywords: appreciative inquiry, organizational change, management, complex adaptive systems, edge of chaos Introduction In today’s fast-paced world where the fluctuating preferences of consumers, the growth in the global web of interdependence, and technological advancements guide the co-evolving relationship between the dominant business environment and the social systems within its domain, an organization’s capacity to cope with change in a timely manner determines its survival (Hesselbein & Goldsmith, 2006; Macready & Meyer, 1999; Senge, 2006).
    [Show full text]
  • Chapter 17: Social Change and Collective Behavior
    CHAPTER 17 SocialSocial ChangeChange andand CollectiveCollective BehaviorBehavior 566 U S Your Sections I Sociological N Imagination 1. Social Change G 2. Theoretical Perspectives on Social Change hen you see photos or films showing the Plains Indians of the 3. Collective Behavior WOld West—Sioux, Crow, and so forth—what do you think about the culture 4. Social Movements of those Native Americans? If you’re like most of us, you may assume that it had re- mained unchanged for many centuries—that these people dressed and acted in exactly Learning Objectives the same way as their ancestors. We often assume that nonindustrial soci- eties such as these stand still over time. After reading this chapter, you will be able to Actually, though, sociology teaches us that change comes to all societies. Whether by ❖ illustrate the three social processes that borrowing from other cultures, discovering contribute to social change. new ways of doing things, or creating inven- ❖ discuss how technology, population, nat- tions that ripple through society, all peoples ural environment, revolution, and war experience social change. cause cultures to change. Let’s return to the example of the Plains Indians. You may picture these tribes as ❖ describe social change as viewed by the fierce, buffalo-hunting warriors. Perhaps im- functionalist and conflict perspectives. ages of Sitting Bull and Crazy Horse astride ❖ discuss rumors, fads, and fashions. fast horses attacking Custer come to mind, ❖ compare and contrast theories of crowd leading you to think that their ancestors for centuries had also ridden horses. In fact, behavior. horses were a relatively recent introduction ❖ compare and contrast theories of social to Plains Indian culture in the 1800s.
    [Show full text]
  • Collective Behaviour
    COLLECTIVE BEHAVIOUR DEFINED COLLECTIVE BEHAVIOUR • The term "collective behavior" was first used by Robert E. Park, and employed definitively by Herbert Blumer, to refer to social processes and events which do not reflect existing social structure (laws, conventions, and institutions), but which emerge in a "spontaneous" way. Collective Behaviour defined • Collective behaviour is a meaning- creating social process in which new norms of behaviour that challenges conventional social action emerges. Examples of Collective Behaviour • Some examples of this type of behaviour include panics, crazes, hostile outbursts and social movements • Fads like hula hoop; crazes like Beatlemania; hostile outbursts like anti- war demonstrations; and Social Movements. • Some argue social movements are more sophisticated forms of collective behaviour SOCIAL MOVEMENTS • Social movements are a type of group action. They are large informal groupings of individuals and/or organizations focused on specific political or social issues, in other words, on carrying out, resisting or undoing a social change. CBs and SMs 19th C. ROOTS • Modern Western social movements became possible through education and the increased mobility of labour due to the industrialisation and urbanisation of 19th century societies Tilly’s DEFINITION SM • Charles Tilly defines social movements as a series of contentious performances, displays and campaigns by which ordinary people made collective claims on others [Tilly, 2004].]: Three major elements of SMs • For Tilly, social movements are a major vehicle for ordinary people's participation in public politics [Tilly, 2004:3]. • He argues that there are three major elements to a social movement [Tilly, 2004 THREE ELEMENTS OF SMs 1. Campaigns: a sustained, organized public effort making collective claims on target authorities; 2.
    [Show full text]
  • Threshold Models of Collective Behavior!
    Threshold Models of Collective Behavior! Mark Granovetter State University of New York at Stony Brook Models of collective behavior are developed for situations where actors have two alternatives and the costs and/or benefits of each depend on how many other actors choose which alternative. The key concept is that of "threshold": the number or proportion of others who must make one decision before a given actor does so; this is the point where net benefits begin to exceed net costs for that particular actor. Beginning with a frequency distribution of thresholds, the models allow calculation of the ultimate or "equilibrium" number making each decision. The stability of equilibrium results against various possible changes in threshold distributions is considered. Stress is placed on the importance of exact distributions for outcomes. Groups with similar average preferences may generate very different results; hence it is hazardous to infer individual dispositions from aggregate outcomes or to assume that behavior was directed by ultimately agreed-upon norms. Suggested applications are to riot behavior, innovation and rumor diffusion, strikes, voting, and migra­ tion. Issues of measurement, falsification, and verification are dis­ cussed. BACKGROUND AND DESCRIPTION OF THE MODELS Because sociological theory tends to explain behavior by institutionalized norms and values, the study of behavior inexplicable in this way occupies a peripheral position in systematic theory. Work in the subfields which embody this concern-deviance for individuals and collective behavior for groups-often consists of attempts to show what prevented the established patterns from exerting their usual sway. In the field of collective behavior, one such effort involves the assertion that new norms or beliefs "emerge" 1 This report is heavily indebted to three co-workers.
    [Show full text]
  • Information Socialtaxis and Efficient Collective Behavior Emerging In
    Information socialtaxis and efficient collective behavior emerging in groups of information-seeking agents Ehud D. Karpasa, Adi Shklarsha, and Elad Schneidmana,1 aDepartment of Neurobiology, Weizmann Institute of Science, Rehovot 76100, Israel Edited by Curtis G. Callan Jr., Princeton University, Princeton, NJ, and approved April 17, 2017 (received for review October 31, 2016) Individual behavior, in biology, economics, and computer science, is highly efficient in finding a sparsely signaling source in a tur- is often described in terms of balancing exploration and exploita- bulent environment. These infotaxis search trajectories resemble tion. Foraging has been a canonical setting for studying reward the paths of foraging insects, suggesting a biological implication seeking and information gathering, from bacteria to humans, of this model. mostly focusing on individual behavior. Inspired by the gradient- In nature, many species, including bacteria (15), amoebae (16), climbing nature of chemotaxis, the infotaxis algorithm showed insects (17, 18), fish (19), birds (3), and mammals (20, 21), dis- that locally maximizing the expected information gain leads to play complex group behavior, including foraging. The mecha- efficient and ethological individual foraging. In nature, as well as nisms that underlie group behavior have been of great experi- in theoretical settings, conspecifics can be a valuable source of mental and theoretical interest, focusing on the computation that information about the environment. Whereas the nature and role each individual performs (22–24) and emergent collective behav- of interactions between animals have been studied extensively, ior (24, 25). In terms of social behavior, such models have been the design principles of information processing in such groups used to describe exploration and exploitation of the environment, are mostly unknown.
    [Show full text]
  • Society As a Complex Adaptive System
    59. Society as a CompIex Adaptive System WALTER DUCKLEY WE HAVE ARGUED at some length in another place1 istic is its functioning to maintain the given struc- that the mechanical equilibrium model and the ture of the system within pre-established limits. It organismic homeostasis models of society that involves feedback loops with its environment, and have underlain most modern sociological theory possibly information as well as pure energy inter- have outlived their usefulness. A more viable changes, but these are geared principally to self- model, one much more faithful to the kind of regubtion (structure maintenance) rather than adap- system that society is more and more recognized tation (change of system structure). The complex to k,is in process of developing out of, or is in adaptive systems {species, psychological and socio- keeping with, the modern systems perspective cultural systems) are also open and negentropic. (which we use loosely here to refer to general But they are oppn "internally" us well as externally systems research, cybernetics, information and in that the interchanges among their components communication theory, and related fields). Society, may result in significant changr.s in the nature of or the sociocultural system, is not, then, principally the components theinselves with important con- an equilibrium system or a homeostatic system, sequences for the system as a whole. And the but what we shalt simply refer to as a complex energy level that may be mobilized by the system adaptive system. is subject to relatively wide fluctuation. Internal To summarize the argument in ove~lysimplified as well as external interchanges are mediated form: Equilibria1 systems are relatively closed and characteristically by infirmation flows (via chemi- entropic.
    [Show full text]
  • Evolving Collective Behavior in an Artificial Ecology
    Evolving Collective Behavior in an Christopher R. Ward University of Nottingham Artificial Ecology School of Computer Science Fernand Gobet∗ University of Nottingham School of Psychology Graham Kendall University of Nottingham Abstract Collective behavior refers to coordinated group School of Computer Science motion, common to many animals. The dynamics of a group can be seen as a distributed model, each “animal” applying the same rule set. This study investigates the use of evolved sensory controllers to produce schooling behavior. A set of artificial creatures “live” in an artificial world with hazards Keywords and food. Each creature has a simple artificial neural network ALife, artificial ecology, coexistence brain that controls movement in different situations. A of prey and predator, evolution, fish, flocking, schooling, sensor chromosome encodes the network structure and weights, which may be combined using artificial evolution with another chromosome, if a creature should choose to mate. Prey and predators coevolve without an explicit fitness function for schooling to produce sophisticated, nondeterministic, behavior. The work highlights the role of species’ physiology in understanding behavior and the role of the environment in encouraging the development of sensory systems. 1 Introduction Collective behaviors such as conformity, obedience, and leadership have been studied for years in social psychology. Yet we can see primitive characteristics of these behav- iors in animals, such as fish schools, insect swarms, and bird flocks. Even across such a diverse set of species, the dynamics of the group are very similar. Partridge [18] states that collective behavior occurs when animals move “in unison, more like a single organ- ism than a collection of individuals” (p.
    [Show full text]
  • Linking Individual and Collective Behavior in Adaptive Social Networks
    week ending PRL 116, 128702 (2016) PHYSICAL REVIEW LETTERS 25 MARCH 2016 Linking Individual and Collective Behavior in Adaptive Social Networks Flávio L. Pinheiro,1,2,3 Francisco C. Santos,1,3 and Jorge M. Pacheco2,3,4 1INESC-ID and Instituto Superior Técnico, Universidade de Lisboa, 2744-016 Porto Salvo, Portugal 2Centro de Biologia Molecular e Ambiental da Universidade do Minho, 4710-057 Braga, Portugal 3ATP-group, P-2744-016 Porto Salvo, Portugal 4Departamento de Matemática e Aplicações da Universidade do Minho, 4710-057 Braga, Portugal (Received 11 December 2015; published 24 March 2016) Adaptive social structures are known to promote the evolution of cooperation. However, up to now the characterization of the collective, population-wide dynamics resulting from the self-organization of individual strategies on a coevolving, adaptive network has remained unfeasible. Here we establish a (reversible) link between individual (micro)behavior and collective (macro)behavior for coevolutionary processes. We demonstrate that an adaptive network transforms a two-person social dilemma locally faced by individuals into a collective dynamics that resembles that associated with an N-person coordination game, whose characterization depends sensitively on the relative time scales between the entangled behavioral and network evolutions. In particular, we show that the faster the relative rate of adaptation of the network, the smaller the critical fraction of cooperators required for cooperation to prevail, thus establishing a direct link between network adaptation and the evolution of cooperation. The framework developed here is general and may be readily applied to other dynamical processes occurring on adaptive networks, notably, the spreading of contagious diseases or the diffusion of innovations.
    [Show full text]
  • Research Statement Radhika Nagpal
    Research Statement Radhika Nagpal My research interest is developing programming paradigms for robust collective behavior, inspired by biology. Cells with identical DNA cooperate to form complex structures, such as ourselves, with incredible reliability in the face of constantly dying and replacing parts. Emerging technologies are making it possi- ble to bulk-manufacture and embed millions of tiny computing and sensing agents into materials and the environment. We would like to build novel applications from these technologies, like smart materials and self-reconfiguring robots, that achieve the kind of complexity and reliability that biological systems achieve. This requires solving two problems: How do we take a high-level description of global cooperative goals and translate it into local instruc- • tions for identically-programmed agents? How do we achieve robust and predictable behavior from vast numbers of unreliable parts? • I am interested in using morphogenesis and developmental biology as a source of algorithms and general principles for organizing complex behavior from locally interacting individuals. My approach is to formalize these general principles as programming languages — with explicit primitives, means of combination, and means of abstraction — thus providing a framework for the design and analysis of self-assembling systems. My PhD dissertation is an example of using this approach for shape formation on an intelligent recon- figurable surface composed of thousands of identically-programmed agents. I developed a programming language for specifying the desired global shape at an “abstract” level, as a folding construction on a contin- uous sheet of paper, which is then automatically compiled to produce the program run by an agent.
    [Show full text]
  • Collective Irrationality and Positive Feedback
    Collective Irrationality and Positive Feedback Stamatios C. Nicolis1*, Natalia Zabzina1, Tanya Latty2, David J. T. Sumpter1 1 Mathematics Department, Uppsala University, Uppsala, Sweden, 2 School of Biological Sciences, University of Sydney, Sydney, New South Wales, Australia Abstract Recent experiments on ants and slime moulds have assessed the degree to which they make rational decisions when presented with a number of alternative food sources or shelter. Ants and slime moulds are just two examples of a wide range of species and biological processes that use positive feedback mechanisms to reach decisions. Here we use a generic, experimentally validated model of positive feedback between group members to show that the probability of taking the best of n options depends crucially on the strength of feedback. We show how the probability of choosing the best option can be maximized by applying an optimal feedback strength. Importantly, this optimal value depends on the number of options, so that when we change the number of options the preference of the group changes, producing apparent ‘‘irrationalities’’. We thus reinterpret the idea that collectives show "rational" or "irrational" preferences as being a necessary consequence of the use of positive feedback. We argue that positive feedback is a heuristic which often produces fast and accurate group decision-making, but is always susceptible to apparent irrationality when studied under particular experimental conditions. Citation: Nicolis SC, Zabzina N, Latty T, Sumpter DJT (2011) Collective Irrationality and Positive Feedback. PLoS ONE 6(4): e18901. doi:10.1371/journal. pone.0018901 Editor: Yamir Moreno, University of Zaragoza, Spain Received December 21, 2010; Accepted March 11, 2011; Published April 26, 2011 Copyright: ß 2011 Nicolis et al.
    [Show full text]
  • Collective Information Processing and Pattern Formation in Swarms, Flocks and Crowds
    Collective information processing and pattern formation in swarms, flocks and crowds Mehdi Moussaid1, 2, Simon Garnier2, Guy Theraulaz2, and Dirk Helbing1 1ETH Zurich, Swiss Federal Institute of Technology, Chair of Sociology, UNO D11, Universitätstrasse 41, 8092 Zurich, Switzerland. Phone +41 44 632 88 81; Fax +41 44 632 17 67. 2Centre de Recherches sur la Cognition Animale, UMR-CNRS 5169, Université Paul Sabatier, Bât 4R3, 118 Route de Narbonne, 31062 Toulouse cedex 9, France. Phone: +33 5 61 55 64 41 ; Fax: +33 5 61 55 61 54. Keywords: Self-organization - Social interactions - Information transfer - Living beings - Distributed cognition - Collective behaviors. 1 Abstract The spontaneous organization of collective activities in animal groups and societies has attracted a considerable amount of attention over the last decade. This kind of coordination often permits group-living species to achieve collective tasks that are far beyond single individuals capabilities. In particular, a key benefit lies in the integration of partial knowledge of the environment at the collective level. In this contribution we discuss various self-organization phenomena in animal swarms and human crowds from the point of view of information exchange among individuals. In particular, we provide a general description of collective dynamics across species and introduce a classification of these dynamics not only with respect to the way information is transferred among individuals, but also with regard to the knowledge processing at the collective level. Finally, we highlight the fact that the individuals’ ability to learn from past experiences can have a feedback effect on the collective dynamics, as experienced with the development of behavioral conventions in pedestrian crowds.
    [Show full text]
  • Swarm Robotics: Collective Behavior Inspired by Nature
    Scienc er e & ut S p y s m t o e m C f s Journal of o B l i a Tan, J Comput Sci Syst Biol 2013, 6:6 o n l o r g u y o DOI: 10.4172/jcsb.1000e106 J Computer Science & Systems Biology ISSN: 0974-7230 EditorialResearch Article OpenOpen Access Access Swarm Robotics: Collective Behavior Inspired by Nature Ying Tan* Department of Intelligence Science, School of Electronic Engineering and Computer Science, Peking University, Beijing, China Definition of Swarm Robotics mining task or agricultural foraging task. The swarm robotics can be also involved in the tasks that require large space and time cost, and are Swarm robotics is a new approach to the coordination of multi- dangerous to the human being or the robots themselves, such as post- robot systems which consist of a number of simple physical robots disaster relief, target searching, military applications, etc. [1]. It is supposed that a desired collective behavior emerges from the interaction between the robots and the interaction of robots with The study of robotics application in target search has grown the environment. This approach emerged in the field of artificial substantially in the recent years. It is more preferable for the dangerous swarm intelligence as well as the biological study of insects, ants or inaccessible working area. The problems involved in swarm robotics and other fields in the nature, where a swarm behavior occurs. research can be classified into two classes. One class of the problems The aim of researches of swarm robotics is to study the design of large is mainly based on the patterns, such as aggregation, cartography, amount of relatively simple robots, their physical bodies and their migration, self-organizing grids, deployment of distributed agents and controlling behaviors.
    [Show full text]