An Exploration in Stigmergy-Based Navigation Algorithms
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Intelligent Agents
Intelligent Agents Authors: Dr. Gerhard Weiss, SCCH GmbH, Austria Dr. Lars Braubach, University of Hamburg, Germany Dr. Paolo Giorgini, University of Trento, Italy Outline Abstract ..................................................................................................................2 Key Words ..............................................................................................................2 1 Foundations of Intelligent Agents.......................................................................... 3 2 The Intelligent Agents Perspective on Engineering .............................................. 5 2.1 Key Attributes of Agent-Oriented Engineering .................................................. 5 2.2 Summary and Challenges................................................................................ 8 3 Architectures for Intelligent Agents ...................................................................... 9 3.1 Internal Agent Architectures .......................................................................... 10 3.2 Social Agent Architectures............................................................................. 11 3.3 Summary & Challenges ................................................................................. 12 4 Development Methodologies................................................................................ 13 4.1 Overall Characterization ................................................................................ 13 4.2 Selected AO Methodologies.......................................................................... -
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. -
Intelligent Agents - Catholijn M
ARTIFICIAL INTELLIGENCE – Intelligent Agents - Catholijn M. Jonker and Jan Treur INTELLIGENT AGENTS Catholijn M. Jonker and Jan Treur Vrije Universiteit Amsterdam, Department of Artificial Intelligence, Amsterdam, The Netherlands Keywords: Intelligent agent, Website, Electronic Commerce Contents 1. Introduction 2. Agent Notions 2.1 Weak Notion of Agent 2.2 Other Notions of Agent 3. Primitive Agent Concepts 3.1 External primitive concepts 3.2 Internal primitive concepts 3.3. An Example Analysis 4. Business Websites 5. A Generic Multi-Agent Architecture for Intelligent Websites 6. Requirements for the Website Agents 6.1 Characteristics and Requirements for the Website Agents 6.2 Characteristics and Requirements for the Personal Assistants 7. The Internal Design of the Information Broker Agents 7.1 A Generic Information Broker Agent Architecture 7.2 The Website Agent: Internal Design 7.3 The Personal Assistant: Internal Design Glossary Bibliography Biographical Sketches Summary In this article first an introduction to the basic concepts of intelligent agents is presented. The notion of weak agent is briefly described, and a number of external and internal primitive agent concepts are introduced. Next, as an illustration, application of intelligent agents to business Websites is addressed. An agent-based architecture for intelligentUNESCO Websites is introduced. Requirements – EOLSS of the agents that play a role in this architecture are discussed. Moreover, their internal design is discussed. 1. IntroductionSAMPLE CHAPTERS The term agent has become popular, and has been used for a wide variety of applications, ranging from simple batch jobs and simple email filters, to mobile applications, to intelligent assistants, and to large, open, complex, mission critical systems (such as systems for air traffic control). -
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. -
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) -
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. -
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. -
Multi-Agent Reinforcement Learning: an Overview∗
Delft University of Technology Delft Center for Systems and Control Technical report 10-003 Multi-agent reinforcement learning: An overview∗ L. Bus¸oniu, R. Babuska,ˇ and B. De Schutter If you want to cite this report, please use the following reference instead: L. Bus¸oniu, R. Babuska,ˇ and B. De Schutter, “Multi-agent reinforcement learning: An overview,” Chapter 7 in Innovations in Multi-Agent Systems and Applications – 1 (D. Srinivasan and L.C. Jain, eds.), vol. 310 of Studies in Computational Intelligence, Berlin, Germany: Springer, pp. 183–221, 2010. Delft Center for Systems and Control Delft University of Technology Mekelweg 2, 2628 CD Delft The Netherlands phone: +31-15-278.24.73 (secretary) URL: https://www.dcsc.tudelft.nl ∗This report can also be downloaded via https://pub.deschutter.info/abs/10_003.html Multi-Agent Reinforcement Learning: An Overview Lucian Bus¸oniu1, Robert Babuskaˇ 2, and Bart De Schutter3 Abstract Multi-agent systems can be used to address problems in a variety of do- mains, including robotics, distributed control, telecommunications, and economics. The complexity of many tasks arising in these domains makes them difficult to solve with preprogrammed agent behaviors. The agents must instead discover a solution on their own, using learning. A significant part of the research on multi-agent learn- ing concerns reinforcement learning techniques. This chapter reviews a representa- tive selection of Multi-Agent Reinforcement Learning (MARL) algorithms for fully cooperative, fully competitive, and more general (neither cooperative nor competi- tive) tasks. The benefits and challenges of MARL are described. A central challenge in the field is the formal statement of a multi-agent learning goal; this chapter re- views the learning goals proposed in the literature. -
Markets Not Capitalism Explores the Gap Between Radically Freed Markets and the Capitalist-Controlled Markets That Prevail Today
individualist anarchism against bosses, inequality, corporate power, and structural poverty Edited by Gary Chartier & Charles W. Johnson Individualist anarchists believe in mutual exchange, not economic privilege. They believe in freed markets, not capitalism. They defend a distinctive response to the challenges of ending global capitalism and achieving social justice: eliminate the political privileges that prop up capitalists. Massive concentrations of wealth, rigid economic hierarchies, and unsustainable modes of production are not the results of the market form, but of markets deformed and rigged by a network of state-secured controls and privileges to the business class. Markets Not Capitalism explores the gap between radically freed markets and the capitalist-controlled markets that prevail today. It explains how liberating market exchange from state capitalist privilege can abolish structural poverty, help working people take control over the conditions of their labor, and redistribute wealth and social power. Featuring discussions of socialism, capitalism, markets, ownership, labor struggle, grassroots privatization, intellectual property, health care, racism, sexism, and environmental issues, this unique collection brings together classic essays by Cleyre, and such contemporary innovators as Kevin Carson and Roderick Long. It introduces an eye-opening approach to radical social thought, rooted equally in libertarian socialism and market anarchism. “We on the left need a good shake to get us thinking, and these arguments for market anarchism do the job in lively and thoughtful fashion.” – Alexander Cockburn, editor and publisher, Counterpunch “Anarchy is not chaos; nor is it violence. This rich and provocative gathering of essays by anarchists past and present imagines society unburdened by state, markets un-warped by capitalism. -
Self-Organized Flocking with a Heterogeneous Mobile Robot Swarm
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by PUblication MAnagement Self-Organized Flocking with a Heterogeneous Mobile Robot Swarm Alessandro Stranieri Eliseo Ferrante Ali Emre Turgut Vito Trianni Carlo Pinciroli Mauro Birattari Marco Dorigo Universite´ Libre de Bruxelles, 1050, Belgium fastranie, eferrante, aturgut, vtrianni, cpinciro, mbiro, [email protected] Abstract orientation of neighboring robots or, as explained in this pa- per, a communication device. Therefore, understanding if a In this paper, we study self-organized flocking in a swarm of behaviorally heterogeneous mobile robots: aligning and non- swarm can achieve flocking with only a few aligning robots aligning robots. Aligning robots are capable of agreeing on can support the design of swarms with minimal hardware a common heading direction with other neighboring aligning requirements. robots. Conversely, non-aligning robots lack this capability. We conduct simulation-based experiments and we mea- Studying this type of heterogeneity in self-organized flock- sure self-organized flocking performance in terms of the de- ing is important as it can support the design of a swarm with minimal hardware requirements. Through systematic simu- gree of group order, group cohesiveness and average group lations, we show that a heterogeneous group of aligning and speed. With respect to these criteria, we found that the non-aligning robots can achieve good performance in flock- swarm achieves good flocking performance when the pro- ing behavior. We further show that the performance is af- portion of aligning robots is high. Conversely, this perfor- fected not only by the proportion of aligning robots, but also mance decreases as the proportion gets lower. -
Stigmergic Epistemology, Stigmergic Cognition Action Editor: Ron Sun Leslie Marsh A,*, Christian Onof B,C
CORE Metadata, citation and similar papers at core.ac.uk Provided by Cognitive Sciences ePrint Archive ARTICLE IN PRESS Cognitive Systems Research xxx (2007) xxx–xxx www.elsevier.com/locate/cogsys Stigmergic epistemology, stigmergic cognition Action editor: Ron Sun Leslie Marsh a,*, Christian Onof b,c a Centre for Research in Cognitive Science, University of Sussex, United Kingdom b Faculty of Engineering, Imperial College London, United Kingdom c School of Philosophy, Birkbeck College London, United Kingdom Received 13 May 2007; accepted 30 June 2007 Abstract To know is to cognize, to cognize is to be a culturally bounded, rationality-bounded and environmentally located agent. Knowledge and cognition are thus dual aspects of human sociality. If social epistemology has the formation, acquisition, mediation, transmission and dissemination of knowledge in complex communities of knowers as its subject matter, then its third party character is essentially stigmer- gic. In its most generic formulation, stigmergy is the phenomenon of indirect communication mediated by modifications of the environment. Extending this notion one might conceive of social stigmergy as the extra-cranial analog of an artificial neural network providing epi- stemic structure. This paper recommends a stigmergic framework for social epistemology to account for the supposed tension between individual action, wants and beliefs and the social corpora. We also propose that the so-called ‘‘extended mind’’ thesis offers the requisite stigmergic cognitive analog to stigmergic knowledge. Stigmergy as a theory of interaction within complex systems theory is illustrated through an example that runs on a particle swarm optimization algorithm. Ó 2007 Elsevier B.V. All rights reserved. -
Flocks, Herds, and Schools: a Distributed Behavioral Model 1
Published in Computer Graphics, 21(4), July 1987, pp. 25-34. (ACM SIGGRAPH '87 Conference Proceedings, Anaheim, California, July 1987.) Flocks, Herds, and Schools: A Distributed Behavioral Model 1 Craig W. Reynolds Symbolics Graphics Division [obsolete addresses removed 2] Abstract The aggregate motion of a flock of birds, a herd of land animals, or a school of fish is a beautiful and familiar part of the natural world. But this type of complex motion is rarely seen in computer animation. This paper explores an approach based on simulation as an alternative to scripting the paths of each bird individually. The simulated flock is an elaboration of a particle system, with the simulated birds being the particles. The aggregate motion of the simulated flock is created by a distributed behavioral model much like that at work in a natural flock; the birds choose their own course. Each simulated bird is implemented as an independent actor that navigates according to its local perception of the dynamic environment, the laws of simulated physics that rule its motion, and a set of behaviors programmed into it by the "animator." The aggregate motion of the simulated flock is the result of the dense interaction of the relatively simple behaviors of the individual simulated birds. Categories and Subject Descriptors: 1.2.10 [Artificial Intelligence]: Vision and Scene Understanding; 1.3.5 [Computer Graphics]: Computational Geometry and Object Modeling; 1.3.7 [Computer Graphics]: Three Dimensional Graphics and Realism-Animation: 1.6.3 [Simulation and Modeling]: Applications. General Terms: Algorithms, design.b Additional Key Words, and Phrases: flock, herd, school, bird, fish, aggregate motion, particle system, actor, flight, behavioral animation, constraints, path planning.