Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Buchtitelbild

2018 | OriginalPaper | Buchkapitel

Introduction to Swarm Search

verfasst von : Yaniv Altshuler, Alex Pentland, Alfred M. Bruckstein

Erschienen in: Swarms and Network Intelligence in Search

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

This book aims to bring to the forefront innovative approaches for the design and analysis of Swarm Search systems and methodologies. Specifically, the book offers a comprehensive analysis of the theory and tools needed for the development of an efficient and robust infrastructure for the design of collaborative patrolling UAV swarms, focusing on its applications for tactic intelligence drones. This Introduction shortly reviews the analysis scope of the book, as well as provides a short summary of each of its chapters.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Nächstes Kapitel Cooperative “Swarm Cleaning” of Stationary Domains
Literatur
1.
F.R. Adler, D.M. Gordon, Information collection and spread by networks of partolling agents. Am. Nat. 140(3), 373–400 (1992)CrossRef
2.
N. Agmon, S. Kraus, G.A. Kaminka, Multi-robot perimeter patrol in adversarial settings, in IEEE International Conference on Robotics and Automation (ICRA 2008) (2008), pp. 2339–2345
3.
N. Agmon, V. Sadov, G.A. Kaminka, S. Kraus, The impact of adversarial knowledge on adversarial planning in perimeter patrol, in AAMAS ’08: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems (International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 2008), pp. 55–62
4.
A. Agogino, K. Tumer, Regulating air traffic flow with coupled agents, in AAMAS ’08: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems (International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 2008), pp. 535–542
5.
M.M. Aid, All glory is fleeting: sigint and the fight against international terrorism. Intell. Natl. Secur. 18(4), 72–120 (2003)CrossRef
6.
S. Aknine, O. Shehory, A feasible and practical coalition formation mechanism leveraging compromise and task relationships, in Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology (2006), pp. 436–439
7.
R. Alami, S. Fleury, M. Herrb, F. Ingrand, F. Robert, Multi-robot cooperation in the martha project. IEEE Robot. Autom. Mag. 5(1), 36–47 (1998)CrossRef
8.
Y. Altshuler, A.M. Bruckstein, The complexity of grid coverage by swarm robotics, in ANTS 2010 (LNCS, 2010), pp. 536–543
9.
Y. Altshuler, A.M. Bruckstein, Static and expanding grid coverage with ant robots: complexity results. Theoret. Comput. Sci. 412(35), 4661–4674 (2011)MathSciNetCrossRefMATH
10.
Y. Altshuler, A.M. Bruckstein, I.A. Wagner, Swarm robotics for a dynamic cleaning problem, in IEEE Swarm Intelligence Symposium (2005), pp. 209–216
11.
Y. Altshuler, V. Yanovski, I.A. Wagner, A.M. Bruckstein, The cooperative hunters - efficient cooperative search for smart targets using uav swarms, in Second International Conference on Informatics in Control, Automation and Robotics (ICINCO), the First International Workshop on Multi-Agent Robotic Systems (MARS) (2005), pp. 165–170
12.
Y. Altshuler, I.A. Wagner, A.M. Bruckstein, Shape factor‘s effect on a dynamic cleaners swarm, in Third International Conference on Informatics in Control, Automation and Robotics (ICINCO), the Second International Workshop on Multi-Agent Robotic Systems (MARS) (2006), pp. 13–21
13.
Y. Altshuler, V. Yanovsky, I. Wagner, A. Bruckstein, Swarm intelligence searchers, cleaners and hunters. Swarm Intelligent Systems (2006), pp. 93–132
14.
Y. Altshuler, I.A. Wagner, A.M. Bruckstein, On swarm optimality in dynamic and symmetric environments 7, 11 (2008)
15.
Y. Altshuler, V. Yanovsky, A.M. Bruckstein, I.A. Wagner, Efficient cooperative search of smart targets using uav swarms. ROBOTICA 26, 551–557 (2008)CrossRef
16.
Y. Altshuler, I.A. Wagner, A.M. Bruckstein, Collaborative exploration in grid domains, in Sixth International Conference on Informatics in Control, Automation and Robotics (ICINCO) (2009)
17.
Y. Altshuler, I.A. Wagner, V. Yanovski, A.M. Bruckstein, Multi-agent cooperative cleaning of expanding domains. Int. J. Robot. Res. 30, 1037–1071 (2010)CrossRef
18.
Y. Altshuler, S. Dolev, Y. Elovici, N. Aharony, Ttled random walks for collaborative monitoring, in NetSciCom 2010 (Second International Workshop on Network Science for Communication Networks), San Diego, CA, USA, vol. 3 (2010)
19.
Y. Altshuler, M. Fire, N. Aharony, Y. Elovici, A Pentland, How many makes a crowd? on the correlation between groups’ size and the accuracy of modeling, in International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction (Springer, 2012), pp. 43–52
20.
Y. Altshuler, E. Shmueli, G. Zyskind, O. Lederman, N. Oliver, A. Pentland, Campaign optimization through behavioral modeling and mobile network analysis. IEEE Trans. Comput. Soc. Syst. 1(2), 121–134 (2014)CrossRef
21.
Y. Altshuler, A. Sandy Pentland, G. Gordon, Social behavior bias and knowledge management optimization, in Social Computing, Behavioral-Cultural Modeling, and Prediction (Springer, 2015), pp. 258–263
22.
Y. Altshuler, E. Shmueli, G. Zyskind, O. Lederman, N. Oliver, A.S. Pentland, Campaign optimization through mobility network analysis. Geo-Intelligence and Visualization Through Big Data Trends (2015), pp. 33–74
23.
Y. Altshuler, A. Pentland, S. Bekhor, Y. Shiftan, A. Bruckstein, Optimal dynamic coverage infrastructure for large-scale fleets of reconnaissance uavs (2016), arXiv:​1611.​05735
24.
Y. Altshuler, R. Puzis, Y. Elovici, S. Bekhor, A. Sandy Pentland, On the rationality and optimality of transportation networks defense: a network centrality approach. Securing Transportation Systems (2015), pp. 35–63
25.
T. Arai, H. Ogata, T. Suzuki, Collision avoidance among multiple robots using virtual impedance, in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (1989), pp. 479–485
26.
R.C. Arkin, T. Balch, Aura: principles and practice in review. J. Exp. Theor. Artif. Intell. 9(2/3), 175–188 (1997)CrossRef
27.
T. Balch, R. Arkin, Behavior-based formation control for multi-robot teams. IEEE Trans. Robot. Autom. 14(6), 926–939 (1998)CrossRef
28.
D. Ball et al., Burma’s Military Secrets: Signals Intelligence (SIGINT) from 1941 to Cyber Warfare (White Lotus Press, 1998)
29.
M. Benda, V. Jagannathan, R. Dodhiawalla, On optimal cooperation of knowledge sources. Technical Report BCS-G2010-28, Boeing AI Center (1985)
30.
K. Bendjilali, F. Belkhouche, B. Belkhouche, Robot formation modelling and control based on the relative kinematics equations. Int. J. Robot. Autom. 24(1), 79–88 (2009)
31.
G. Beni, J. Wang, Theoretical problems for the realization of distributed robotic systems, in IEEE Internal Conference on Robotics and Automation (1991), pp. 1914–1920
32.
R.M. Bhatt, C.P. Tang, V.N. Krovi, Formation optimization for a fleet of wheeled mobile robots a geometric approach. Robot. Auton. Syst. 57(1), 102–120 (2009)CrossRef
33.
E. Bonabeau, M. Dorigo, G. Theraulaz, Swarm Intelligence: From Natural to Artificial Systems (Oxford University Press, US, 1999)MATH
34.
M. Brambilla, E. Ferrante, M. Birattari, M. Dorigo, Swarm robotics: a review from the swarm engineering perspective. Swarm Intell. 7(1), 1–41 (2013)CrossRef
35.
R.A. Brooks, A robust layered control system for a mobile robot. IEEE J. Robot. Autom. RA–2(1), 14–23 (1986)
36.
R.A. Brooks, A.M. Flynn, Fast, cheap and out of control, a robot invasion of the solar system. J. Br. Interplanet. Soc. 42, 478–485 (1989)
37.
A.M. Bruckstein, Why the ant trails look so straight and nice. Math. Intell. 15(2), 59–62 (1993)CrossRefMATH
38.
A.M. Bruckstein, C.L. Mallows, I.A. Wagner, Probabilistic pursuits on the integer grid. Am. Math. Mon. 104(4), 323–343 (1997)CrossRefMATH
39.
R.J. Bunker, Terrorist and insurgent unmanned aerial vehicles: Use, potentials, and military implications. Technical report, DTIC Document (2015)
40.
D.J. Bursac, Autonomous robotic weapons: Us army innovation for ground combat in the twenty-first century (Technical report, US Army School for Advanced Military Studies Fort Leavenworth United States, 2015)
41.
D. Byman, Why drones work. Foreign Aff. 92(4), 32–43 (2013)
42.
C. Candea, H. Hu, L. Iocchi, D. Nardi, M. Piaggio, Coordinating in multi-agent robocup teams. Robot. Auton. Syst. 36(2–3), 67–86 (2001)CrossRefMATH
43.
G. Chalkiadakis, C. Boutilier, Sequential decision making in repeated coalition formation under uncertainty, in Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems (2008), pp. 347–354
44.
G. Chalkiadakis, E. Markakis, C. Boutilier, Coalition formation under uncertainty: bargaining equilibria and the bayesian core stability concept, in AAMAS ’07: Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems (ACM, New York, 2007), pp. 1–8
45.
R. Chesney, Military-intelligence convergence and the law of the title 10/title 50 debate. J. Nat. Secur. Law Policy 5, 539 (2012)
46.
D. Chevallier, S. Payandeh, On kinematic geometry of multi-agent manipulating system based on the contact force information, in The Sixth International Conference on Intelligent Autonomous Systems (IAS-6) (2000), pp. 188–195
47.
R. Connaughton, P. Schermerhorn, M. Scheutz, Physical parameter optimization in swarms of ultra-low complexity agents, in AAMAS ’08: Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems (International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 2008), pp. 1631–1634
48.
S.A. DeLoach, M. Kumar, Multi-agent systems engineering: an overview and case study, Intelligence Integration in Distributed Knowledge Management (Idea Group Inc (IGI), 2008), pp. 207–224
49.
J. Deneubourg, S. Goss, G. Sandini, F. Ferrari, P. Dario, Self-organizing collection and transport of objects in unpredictable environments, in Japan-U.S.A. Symposium on Flexible Automation (1990), pp. 1093–1098
50.
M.B. Dias, A. Stentz, A market approach to multirobot coordination. Technical Report, CMU-RI - TR-01-26, Robotics Institute, Carnegie Mellon University (2001)
51.
A. Drogoul, J. Ferber, From tom thumb to the dockers: some experiments with foraging robots, in Proceedings of the Second International Conference on Simulation of Adaptive Behavior (1992), pp. 451–459
52.
G. Dudek, M.R.M. Jenkin, E. Milios, D. Wilkes, A taxonomy for multi-agent robotics. Auton. Robots J. 3(4), 375–397 (1996)CrossRef
53.
A. Efraim, D. Peleg, Distributed algorithms for partitioning a swarm of autonomous mobile robots, Structural Information and Communication Complexity, vol. 4474, Lecture Notes in Computer Science (2007), pp. 180–194
54.
A. Felner, Y. Shoshani, Y. Altshuler, A.M. Bruckstein, Multi-agent physical a* with large pheromones. J. Auton. Agents Multi-Agent Syst. 12(1), 3–34 (2006)CrossRef
55.
C. Ferrari, E. Pagello, J. Ota, T. Arai, Multirobot motion coordination in space and time. Robot. Auton. Syst. 25, 219–229 (1998)CrossRef
56.
J. Fredslund, M.J. Mataric, Robot formations using only local sensing and control, in Proceedings of the International Symposium on Computational Intelligence in Robotics and Automation (IEEE CIRA 2001) (2001), pp. 308–313
57.
B.P. Gerkey, M.J. Mataric, Sold! market methods for multi-robot control (IEEE Trans. Robot. Autom. Spec, Issue Multi-robot Syst, 2002)
58.
D.M. Gordon, The expandable network of ant exploration. Anim. Behav. 50, 372–378 (1995)CrossRef
59.
N. Gordon, I.A. Wagner, A.M. Bruckstein, Discrete bee dance algorithms for pattern formation on a grid, in IEEE International Conference on Intelligent Agent Technology (IAT03) (2003), pp. 545–549
60.
S. Graham, Cities under siege: The new military urbanism (Verso Books, 2011)
61.
J. Hagelbäck, S.J. Johansson, Demonstration of multi-agent potential fields in real-time strategy games, in AAMAS ’08: Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems (International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 2008), pp. 1687–1688
62.
I. Harmatia, K. Skrzypczykb, Robot team coordination for target tracking using fuzzy logic controller in game theoretic framework. Robot. Auton. Syst. 57(1), 75–86 (2009)CrossRef
63.
T. Haynes, S. Sen, Adaptation and learning in multi-agent systems. Evolving Behavioral Strategies in Predators and Prey, vol. 1042, Lecture Notes in Computer Science (Springer, Berlin, 1986), pp. 113–126
64.
I. Henderson, Civilian intelligence agencies and the use of armed drones, in Yearbook of International Humanitarian Law-2010 (Springer, 2011), pp. 133–173
65.
S. Hettiarachchi, W. Spears, Moving swarm formations through obstacle fields, in International Conference on Artificial Intelligence (2005)
66.
M.G. Hinchey, R. Sterritt, C. Rouff, Swarms and swarm intelligence. Computer 40(4), 1 (2007)CrossRef
67.
G. Hollinger, S. Singh, J. Djugash, A. Kehagias, Efficient multi-robot search for a moving target. Int. J. Robot. Res. 28(2), 201–219 (2009)CrossRef
68.
D.E. Johnson, Military capabilities for hybrid war: Insights from the Israel Defense Forces in Lebanon and Gaza (Rand Corporation, 2014)
69.
Lt. Kendra L.B. Cook, The silent force multiplier: the history and role of uavs in warfare, in Aerospace Conference, 2007 IEEE (IEEE, 2007), pp. 1–7
70.
S. Kirkpatrick, J.J. Schneider, How smart does an agent need to be? Int. J. Mod. Phys. C 16, 139–155 (2005)CrossRefMATH
71.
T. Klos, G.J. van Ahee, Evolutionary dynamics for designing multi-period auctions, in AAMAS ’08: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems (International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 2008), pp. 1589–1592
72.
B. Koopman, Search and Screening: General Principles with Historical Applications (Pergamon Press, 1980)
73.
P.M. Krafft, J. Zheng, W. Pan, N. Della Penna, Y. Altshuler, E. Shmueli, J.B. Tenenbaum, A. Pentland, Human collective intelligence as distributed bayesian inference (2016), arXiv:​1608.​01987
74.
S. Kraus, O. Shehory, G. Taase, Coalition formation with uncertain heterogeneous information, in Proceedings of the second international joint conference on Autonomous agents and multiagent systems (2003), pp. 1–8
75.
S.M. LaValle, D. Lin, L.J. Guibas, J.C. Latombe, R. Motwani, Finding an unpredictable target in a workspace with obstacles, in Proceedings of the 1997 IEEE International Conference on Robotics and Automation (ICRA-97) (1997), pp. 737–742
76.
Y.-Y. Liu, J.C. Nacher, T. Ochiai, M. Martino, Y. Altshuler, Prospect theory for online financial trading. PLoS ONE 9(10), e109458 (2014)CrossRef
77.
V.J. Lumelsky, K.R. Harinarayan, Decentralized motion planning for multiple mobile robots: the cocktail party model. Auton. Robots 4(1), 121–136 (1997)CrossRef
78.
D. MacKenzie, R. Arkin, J. Cameron, Multiagent mission specification and execution. Auton. Robots 4(1), 29–52 (1997)CrossRef
79.
E. Manisterski, R. Lin, S. Kraus, Understanding how people design trading agents over time, in AAMAS ’08: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems (International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 2008), pp. 1593–1596
80.
S. Mastellone, D.M. Stipanovi, C.R. Graunke, K.A. Intlekofer, M.W. Spong, Formation control and collision avoidance for multi-agent non-holonomic systems: theory and experiments. Int. J. Robot. Res. 27(1), 107–126 (2008)CrossRef
81.
M.J. Mataric, Designing emergent behaviors: From local interactions to collective intelligence, in Proceedings of the Second International Conference on Simulation of Adaptive Behavior, ed. by J. Meyer, H. Roitblat, S. Wilson (MIT Press, 1992), pp. 432–441
82.
M.J. Mataric, Interaction and Intelligent Behavior. Ph.D. thesis, Massachusetts Institute of Technology (1994)
83.
N. Michael, M.M. Zavlanos, V. Kumar, G.J. Pappas, Distributed multi-robot task assignment and formation control, in IEEE International Conference on Robotics and Automation, 2008 (ICRA 2008) (2008), pp. 128–133
84.
P.M. Morse, G.E. Kimball, Methods of Operations Research (MIT Press, Wiley, New York, 1951)MATH
85.
F. Naaz, Indo-israel military cooperation. Strateg. Anal. 24(5), 969–985 (2000)CrossRef
86.
R. Olfati-Saber, Flocking for multi-agent dynamic systems: algorithms and theory. IEEE Trans. Autom. Control 51(3), 401–420 (2006)MathSciNetCrossRefMATH
87.
D. Orfanus, E.P. de Freitas, F. Eliassen, Self-organization as a supporting paradigm for military uav relay networks. IEEE Commun. Lett. 20(4), 804–807 (2016)CrossRef
88.
E. Pagello, A.D. Angelo, F. Montesello, F. Garelli, C. Ferrari, Cooperative behaviors in multi-robot systems through implicit communication. Robot. Auton. Syst. 29(1), 65–77 (1999)CrossRef
89.
E. Pagello, A.D. Angelo, C. Ferrari, R. Polesel, R. Rosati, A. Speranzon, Emergent behaviors of a robot team performing cooperative tasks. Advanced Robotics (2002)
90.
W. Pan, Y. Altshuler, A. Pentland, Decoding social influence and the wisdom of the crowd in financial trading network, in Privacy, Security, Risk and Trust (PASSAT), 2012 International Conference on and 2012 International Confernece on Social Computing (SocialCom) (IEEE, 2012), pp. 203–209
91.
L.E. Parker, Alliance: an architecture for fault-tolerant multi-robot cooperation. IEEE Trans. Robot. Autom. 14(2), 220–240 (1998)MathSciNetCrossRef
92.
L.E. Parker, C. Touzet, Multi-robot learning in a cooperative observation task. Distrib. Auton. Robot. Syst. 4, 391–401 (2000)
93.
K. Passino, M. Polycarpou, D. Jacques, M. Pachter, Y. Liu, Y. Yang, M. Flint, M. Baum, Cooperative Control for Autonomous Air Vehicles, chapter Cooperative Control and Optimization (Kluwer Academic, Boston, 2002)
94.
M. Pfingsthorn, B. Slamet, A. Visser, A scalable hybrid multi-robot SLAM method for highly detailed maps. RoboCup 2007: Robot Soccer World Cup XI, vol. 5001 Lecture Notes in Computer Science (Springer, Berlin, 2008), pp. 457–464
95.
J.T.K. Ping, A.E. Ling, T.J. Quan, C. Yea Dat, Generic unmanned aerial vehicle (uav) for civilian application-a feasibility assessment and market survey on civilian application for aerial imaging, in 2012 IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (STUDENT) (IEEE, 2012), pp. 289–294
96.
S. Premvuti, S. Yuta, Consideration on the cooperation of multiple autonomous mobile robots, in Proceedings of the IEEE International Workshop of Intelligent Robots and Systems (1990), pp. 59–63
97.
R. Puzis, Y. Altshuler, Y. Elovici, S. Bekhor, Y. Shiftan, A.S. Pentland, Augmented betweenness centrality for environmentally-aware traffic monitoring in transportation networks
98.
E. Regev, Y. Altshuler, A.M. Bruckstein, The cooperative cleaners problem in stochastic dynamic environments (2012), arXiv:​1201.​6322
99.
M. Rehak, M. Pechoucek, P. Celeda, V. Krmicek, M. Grill, K. Bartos, Multi-agent approach to network intrusion detection, in AAMAS ’08: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems (International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 2008), pp. 1695–1696
100.
I.M. Rekleitis, G. Dudek, E. Milios, Experiments in free-space triangulation using cooperative localization, in IEEE/RSJ/GI International Conference on Intelligent Robots and Systems (IROS) (2003)
101.
W. Ren, N. Sorensena, Distributed coordination architecture for multi-robot formation control. Robot. Auton. Syst. 56(4), 324–333 (2008)CrossRefMATH
102.
C. Rouff, W. Truszkowski, J. Rash, M. Hinchey, Formal approaches to intelligent swarms, in Software Engineering Workshop, 2003. Proceedings. 28th Annual NASA Goddard (IEEE, 2003), pp. 51–57
103.
C.A. Rouff, W.F. Truszkowski, J.L. Rash, M.G. Hinchey, A survey of formal methods for intelligent swarms (NASA Goddard Space Flight Center, Greenbelt, MD, 2005)
104.
R. Sanders, An israeli military innovation: Uavs. Technical report, DTIC Document (2003)
105.
S. Sariel, T. Balch, Real time auction based allocation of tasks for multi-robot exploration problem in dynamic environments, in Proceedings of the AAAI-05 Workshop on Integrating Planning into Scheduling (2005), pp. 27–33
106.
S. Savarimuthu, M. Purvis, M. Purvis, Tag based model for knowledge sharing in agent society, discussion paper 2009/01. Discussion paper, Department of Information Science, University of Otago, Dunedin, New Zealand (2009)
107.
R. Sawhney, K.M. Krishna, K. Srinathan, M. Mohan, On reduced time fault tolerant paths for multiple uavs covering a hostile terrain, in AAMAS ’08: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems (International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 2008), pp. 1171–1174
108.
N. Schörnig, Unmanned warfare: towards a neo-interventionist era? in The Armed Forces: Towards a Post-Interventionist Era? (Springer, 2013), pp. 221–235
109.
A.C. Schultz, L.E. Parker, Multi-robot Systems: From Swarms to Intelligent Automata: Proceedings from the 2002 NRL Workshop on Multi-robot Systems (Springer Science & Business Media, 2013)
110.
H.A. Simon, The Sciences of the Artificial, 2nd edn. (MIT Press, 1981)
111.
P. Stone, M. Veloso, Task decomposition, dynamic role assignment, and low-bandwidth communication for real-time strategic teamwork. Artif. Intell. 110(2), 241–273 (1999)CrossRefMATH
112.
H. Su, X. Wang, Z. Lin, Flocking of multi-agents with a virtual leader. IEEE Trans. Autom. Control 54(2), 293–307 (2009)MathSciNetCrossRef
113.
P. Svestka, M.H. Overmars, Coordinated path planning for multiple robots. Robot. Auton. Syst. 23(3), 125–152 (1998)CrossRef
114.
M.T. Tedesco, Countering the unmanned aircraft systems threat. Mil. Rev. 95(6), 64 (2015)
115.
A. Thorndike, Summary of antisubmarine warfare operations in world war ii (Summary report, NDRC Summary Report, 1946)
116.
G. Trajkovski, S.G. Collins, Handbook of Research on Agent-Based Societies: Social and Cultural Interactions (Idea Group Inc (IGI), 2009)
117.
U. Visser et al., RoboCup 2007: Robot Soccer World Cup XI, vol. 5001 (Lecture Notes in Computer Science (Springer, Berlin, 2008)CrossRef
118.
I.A. Wagner, A.M. Bruckstein, Row straightening via local interactions, in Technical report CIS-9406, Center for Intelligent Systems, Technion, Haifa (1994)
119.
I.A. Wagner, A.M. Bruckstein, Ants: agents, networks, trees and subgraphs. Future Gener. Comput. Syst. J. 16(8), 915–926 (2000)CrossRef
120.
I.A. Wagner, A.M. Bruckstein, From ants to a(ge)nts: a special issue on ant–robotics. Ann. Math. Artif. Intell. Spec. Issue Ant Robot. 31(1–4), 1–6 (2001)
121.
I.A. Wagner, M. Lindenbaum, A.M. Bruckstein, Efficiently searching a graph by a smell-oriented vertex process. Ann. Math. Artif. Intell. 24, 211–223 (1998)MathSciNetCrossRefMATH
122.
I.A. Wagner, Y. Altshuler, V. Yanovski, A.M. Bruckstein, Cooperative cleaners: a study in ant robotics. Int. J. Robot. Res. (IJRR) 27(1), 127–151 (2008)CrossRef
123.
P.K.C. Wang, Navigation strategies for multiple autonomous mobile robots, in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (1989), pp. 486–493
124.
A. Weitzenfeld, A prey catching and predator avoidance neural-schema architecture for single and multiple robots. J. Intell. Rob. Syst. 51(2), 203–233 (2008)CrossRef
125.
M.P. Wellman, P.R. Wurman, Market-aware agents for a multiagent world. Robot. Auton. Syst. 24, 115–125 (1998)CrossRef
126.
B.A. Whitmore, Evolution of unmanned aerial warfare: a historical look at remote airpower-a case study in innovation. Technical report, US Army Command and General Staff College Fort Leavenworth United States (2016)
127.
H. Work, E. Chown, T. Hermans, J. Butterfield, Robust team-play in highly uncertain environments, in AAMAS ’08: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems (International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, 2008), pp. 1199–1202
128.
A. Yamashita, M. Fukuchi, J. Ota, T. Arai, H. Asama, Motion planning for cooperative transportation of a large object by multiple mobile robots in a 3d environment, in In Proceedings of IEEE International Conference on Robotics and Automation (2000), pp. 3144–3151
129.
X.-S. Yang, Z. Cui, R. Xiao, A. Hossein Gandomi, M. Karamanoglu, Swarm Intelligence and Bio-Inspired Computation: Theory and Applications (Newnes, 2013)
130.
V. Yanovski, I.A. Wagner, A.M. Bruckstein, Vertex-ants-walk: a robust method for efficient exploration of faulty graphs. Ann. Math. Artif. Intell. 31(1–4), 99–112 (2001)MathSciNetCrossRefMATH
131.
V. Yanovski, I.A. Wagner, A.M. Bruckstein, A distributed ant algorithm for efficiently patrolling a network. Algorithmica 37, 165–186 (2003)MathSciNetCrossRefMATH
132.
X. Zheng, S. Koenig, Reaction functions for task allocation to cooperative agents, in Proceedings of the 7th International Joint Conference on Autonomous Agents and Multiagent Systems (2008), pp. 559–566
133.
R. Zlot, A. Stentz, M.B. Dias, S. Thayer, Multi-robot exploration controlled by a market economy, in Proceedings of the IEEE International Conference on Robotics and Automation (2002)
Metadaten
Titel
Introduction to Swarm Search
verfasst von
Yaniv Altshuler
Alex Pentland
Alfred M. Bruckstein
Copyright-Jahr
2018
Buch
Swarms and Network Intelligence in Search

Print ISBN: 978-3-319-63602-3

Electronic ISBN: 978-3-319-63604-7

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-319-63604-7_1