Skip to main content
SpringerLink
Log in

On a Multi-agent Distributed Asynchronous Intelligence-Sharing and Learning Framework

  • Chapter
  • First Online:
Book cover Transactions on Computational Collective Intelligence XVIII

Part of the book series: Lecture Notes in Computer Science ((TCCI,volume 9240))

Abstract

The current digital era is flooded with devices having high processing and networking capabilities. Sharing of information, learning and adaptation in such highly distributed systems can greatly enhance their performance and utility. However, achieving the same in the presence of asynchronous entities is a complex affair. Multi-agent system paradigms possess intrinsic similarities with these distributed systems and thus provide a fitting platform to solve the problems within. Traditional approaches to efficient information sharing and learning among autonomous agents in distributed environments incur high communication overheads. Non-conventional tactics based on social insect colonies provide natural solutions for transfer of social information in highly distributed and dense populations. This paper portrays a framework to achieve distributed and asynchronous sharing of intelligence and consequent learning among the entities of a networked distributed system. This framework couples localized communication with the available multi-agent technologies to realize asynchronous intelligence-sharing and learning. The framework takes in a user-defined objective together with a learning algorithm as inputs and facilitates cooperative learning among the agents using the mechanisms embedded within. The proposed framework has been implemented using Typhon agent framework over a LAN. The results obtained from the experiments performed using both static and dynamic LANs, substantiate the applicability of the proposed framework in real distributed mobile computing environments.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Alonso, E.: Multi-agent learning. Auton. Agent. Multi-agent Syst. 15(1), 3–4 (2007). http://dx.doi.org/10.1007/s10458-007-0019-1

    Article  Google Scholar 

  2. Atzori, L., Iera, A., Morabito, G.: The internet of things: a survey. Comput. Netw. 54(15), 2787–2805 (2010). http://www.sciencedirect.com/science/article/pii/S1389128610001568

    Article  MATH  Google Scholar 

  3. Berenji, H., Vengerov, D.: Advantages of cooperation between reinforcement learning agents in difficult stochastic problems. In: The Ninth IEEE International Conference on Fuzzy Systems, 2000, FUZZ IEEE 2000, vol. 2, pp. 871–876 (2000)

    Google Scholar 

  4. Bode, M., Jha, S.S., Nair, S.B.: A mobile agent based autonomous partial green corridor discovery and maintenance mechanism for emergency services amidst urban traffic. In: Proceedings of the First International Conference on IoT in Urban Space, URB-IOT 2014, pp. 13–18. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), Brussels (2014). http://dx.doi.org/10.4108/icst.urb-iot.2014.257297

  5. Brenna, M., Falvo, M.C., Foiadelli, F., Martirano, L., Massaro, F., Poli, D., Vaccaro, A.: Challenges in energy systems for the smart-cities of the future. In: 2012 IEEE International on Energy Conference and Exhibition (ENERGYCON), pp. 755–762, September 2012

    Google Scholar 

  6. Busoniu, L., Babuska, R., De Schutter, B.: A comprehensive survey of multiagent reinforcement learning. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 38(2), 156–172 (2008)

    Article  Google Scholar 

  7. Cantú-Paz, E.: A survey of parallel genetic algorithms. Calculateurs Paralleles, Reseaux et Systems Repartis 10(2), 141–171 (1998)

    Google Scholar 

  8. Cao, J., Das, S.K.: Mobile Agents in Networking and Distributed Computing, vol. 3. Wiley, Hoboken (2012)

    Book  Google Scholar 

  9. Cicirello, V., Smith, S.: Wasp-like agents for distributed factory coordination. Auton. Agent. Multi-agent Syst. 8(3), 237–266 (2004). http://dx.doi.org/10.1023/B%3AAGNT.0000018807.12771.60

  10. Dukas, R.: Insect social learning. In: Moore, M.D.B. (ed.) Encyclopedia of Animal Behavior, pp. 176–179. Academic Press, Oxford (2010). http://www.sciencedirect.com/science/article/pii/B9780080453378000589

    Chapter  Google Scholar 

  11. Erdős, P., Rényi, A.: On the evolution of random graphs. Publ. Math. Inst. Hungar. Acad. Sci 5, 17–61 (1960)

    Google Scholar 

  12. Ferber, J.: Multi-agent Systems: An Introduction to Distributed Artificial Intelligence, 1st edn. Addison-Wesley Longman Publishing Co., Inc., Boston (1999)

    Google Scholar 

  13. Fisch, D., Jnicke, M., Kalkowski, E., Sick, B.: Learning from others: exchange of classification rules in intelligent distributed systems. Artif. Intell. 187188, 90–114 (2012). http://www.sciencedirect.com/science/article/pii/S0004370212000410

    Article  Google Scholar 

  14. Franks, N.R., Richardson, T.: Teaching in tandem-running ants. Nature 439(7073), 153–153 (2006)

    Article  Google Scholar 

  15. Garland, A., Alterman, R.: Autonomous agents that learn to better coordinate. Auton. Agent. Multi-agent Syst. 8(3), 267–301 (2004). http://dx.doi.org/10.1023/B%3AAGNT.0000018808.95119.9e

  16. Ghavamzadeh, M., Mahadevan, S., Makar, R.: Hierarchical multi-agent reinforcement learning. Auton. Agent. Multi-agent Syst. 13(2), 197–229 (2006). http://dx.doi.org/10.1007/s10458-006-7035-4

    Article  Google Scholar 

  17. Giannetti, L., Maturana, F.P., Discenzo, F.M.: Agent-based control of a municipal water system. In: Pěchouček, M., Petta, P., Varga, L.Z. (eds.) CEEMAS 2005. LNCS (LNAI), vol. 3690, pp. 500–510. Springer, Heidelberg (2005). http://dx.doi.org/10.1007/11559221_50

    Chapter  Google Scholar 

  18. Godfrey, W.W., Jha, S.S., Nair, S.B.: On a mobile agent framework for an internet of things. In: 2013 International Conference on Communication Systems and Network Technologies (CSNT), pp. 345–350, April 2013

    Google Scholar 

  19. Godfrey, W.W., Jha, S.S., Nair, S.B.: On stigmergically controlling a population of heterogeneous mobile agents using cloning resource. In: Nguyen, N.T. (ed.) TCCI XIV 2014. LNCS, vol. 8615, pp. 49–70. Springer, Heidelberg (2014). http://dx.doi.org/10.1007/978-3-662-44509-9_3

    Google Scholar 

  20. Harrison, C.G., Chess, D.M., Kershenbaum, A.: Mobile Agents: Are They a Good Idea?. IBM TJ Watson Research Center Yorktown Heights, New York (1995)

    Google Scholar 

  21. Holland, O.E.: Multiagent systems: lessons from social insects and collective robotics. In: The 1996 AAAI Spring Symposium on Adaptation, Coevolution and Learning in Multiagent Systems, pp. 57–62 (1996)

    Google Scholar 

  22. Ilarri, S., Mena, E., Illarramendi, A.: Using cooperative mobile agents to monitor distributed and dynamic environments. Inf. Sci. 178(9), 2105–2127 (2008). http://www.sciencedirect.com/science/article/pii/S002002550700583X

    Article  Google Scholar 

  23. Jerne, N.K.: Towards a network theory of the immune system. Annales d’immunologie 125, 373–389 (1974)

    Google Scholar 

  24. Jha, S.S., Godfrey, W.W., Nair, S.B.: Stigmergy-based synchronization of a sequence of tasks in a network of asynchronous nodes. Cybern. Syst. 45(5), 373–406 (2014). http://dx.doi.org/10.1080/01969722.2014.917235

    Article  Google Scholar 

  25. Jha, S.S., Nair, S.B.: Orchestrating the sequential execution of tasks by a heterogeneous set of asynchronous mobile agents. In: Müller, J.P., Weyrich, M., Bazzan, A.L.C. (eds.) MATES 2014. LNCS, vol. 8732, pp. 103–120. Springer, Heidelberg (2014)

    Google Scholar 

  26. Jha, S.S., Shrivastava, K., Nair, S.B.: On emulating real-world distributed intelligence using mobile agent based localized idiotypic networks. In: Prasath, R., Kathirvalavakumar, T. (eds.) MIKE 2013. LNCS, vol. 8284, pp. 487–498. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  27. Kennedy, J.: Particle swarm optimization. In: Gass, S.I., Fu, M.C. (eds.) Encyclopedia of Machine Learning, pp. 760–766. Springer, New York (2010)

    Google Scholar 

  28. Konstantinidis, A., Yang, K., Zhang, Q., Zeinalipour-Yazti, D.: A multi-objective evolutionary algorithm for the deployment and power assignment problem in wireless sensor networks. Comput. Netw. 54(6), 960–976 (2010). http://www.sciencedirect.com/science/article/pii/S1389128609002679, new Network Paradigms

    Article  MATH  Google Scholar 

  29. Korst, P., Velthuis, H.: The nature of trophallaxis in honeybees. Insectes Soc. 29(2), 209–221 (1982). http://dx.doi.org/10.1007/BF02228753

    Article  Google Scholar 

  30. Leadbeater, E., Chittka, L.: Social learning in insects from miniature brains to consensus building. Curr. Biol. 17(16), R703–R713 (2007)

    Article  Google Scholar 

  31. Matani, J., Nair, S.B.: Typhon - a mobile agents framework for real world emulation in prolog. In: Sombattheera, C., Agarwal, A., Udgata, S.K., Lavangnananda, K. (eds.) MIWAI 2011. LNCS, vol. 7080, pp. 261–273. Springer, Heidelberg (2011). http://dx.doi.org/10.1007/978-3-642-25725-4_23

    Chapter  Google Scholar 

  32. Miller, K., Mansingh, G.: Towards a distributed mobile agent decision support system for optimal patient drug prescription. In: 2013 Third International Conference on Innovative Computing Technology (INTECH), pp. 233–238, August 2013

    Google Scholar 

  33. Minar, N., Kramer, K., Maes, P.: Cooperating mobile agents for dynamic network routing. In: Hayzelden, A., Bigham, J. (eds.) Software Agents for Future Communication Systems, pp. 287–304. Springer, Berlin Heidelberg (1999). http://dx.doi.org/10.1007/978-3-642-58418-3_12

    Chapter  Google Scholar 

  34. Oldewurtel, F., Sturzenegger, D., Morari, M.: Importance of occupancy information for building climate control. Appl. Energy 101, 521–532 (2013). http://www.sciencedirect.com/science/article/pii/S0306261912004564, sustainable Development of Energy, Water and Environment Systems

    Article  Google Scholar 

  35. Outtagarts, A.: Mobile agent-based applications: a survey. Int. J. Comput. Sci. Netw. Secur. 9(11), 331–339 (2009)

    Google Scholar 

  36. Panait, L., Luke, S.: Cooperative multi-agent learning: the state of the art. Auton. Agent. Multi-agent Syst. 11(3), 387–434 (2005). http://dx.doi.org/10.1007/s10458-005-2631-2

    Article  Google Scholar 

  37. Papaioannou, T., Edwards, J.: Building agile systems with mobile code. Auton. Agent. Multi-agent Syst. 4(4), 293–310 (2001). http://dx.doi.org/10.1023/A%3A1012758908423

  38. Queloz, P.A., Villazn, A.: Composition of services with mobile code. Auton. Agent. Multi-agent Syst. 4(4), 311–337 (2001). http://dx.doi.org/10.1023/A%3A1012711025262

  39. Rajkumar, R.R., Lee, I., Sha, L., Stankovic, J.: Cyber-physical systems: the next computing revolution. In: Proceedings of the 47th Design Automation Conference, DAC 2010, pp. 731–736. ACM, New York (2010). http://doi.acm.org/10.1145/1837274.1837461

  40. Ren, W., Beard, R., Atkins, E.: A survey of consensus problems in multi-agent coordination. In: American Control Conference, 2005, Proceedings of the 2005, vol. 3, pp. 1859–1864, June 2005

    Google Scholar 

  41. Santos, A., Delbem, A., London, J.B.A., Bretas, N.: Node-depth encoding and multiobjective evolutionary algorithm applied to large-scale distribution system reconfiguration. IEEE Trans. Power Syst. 25(3), 1254–1265 (2010)

    Article  Google Scholar 

  42. Stone, P., Veloso, M.: Multiagent systems: a survey from a machine learning perspective. Auton. Robots 8(3), 345–383 (2000). http://dx.doi.org/10.1023/A%3A1008942012299

  43. Van Dyke Parunak, H.: “Go to the ant": engineering principles from natural multi-agent systems. Ann. Oper. Res. 75, 69–101 (1997). http://dx.doi.org/10.1023/A%3A1018980001403

  44. Zhao, P., Suryanarayanan, S., Simoes, M.: An energy management system for building structures using a multi-agent decision-making control methodology. IEEE Trans. Ind. Appl. 49(1), 322–330 (2013)

    Article  Google Scholar 

Download references

Acknowledgements

The first author would like to acknowledge Tata Consultancy Services (TCS) and Ministry of Human Resource Development, Govt. of India for the support rendered during the research reported in this paper.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Guwahati, Guwahati, 781039, India

    Shashi Shekhar Jha & Shivashankar B. Nair

Authors
  1. Shashi Shekhar Jha
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shivashankar B. Nair
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shashi Shekhar Jha .

Editor information

Editors and Affiliations

  1. Wroclaw University of Technology, Department of Information Systems, Wroclaw, Poland

    Ngoc Thanh Nguyen

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Jha, S.S., Nair, S.B. (2015). On a Multi-agent Distributed Asynchronous Intelligence-Sharing and Learning Framework. In: Nguyen, N. (eds) Transactions on Computational Collective Intelligence XVIII. Lecture Notes in Computer Science(), vol 9240. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48145-5_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-662-48145-5_9

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-48144-8

  • Online ISBN: 978-3-662-48145-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation