Advertisement

Multi-agent Systems Meet Aggregate Programming: Towards a Notion of Aggregate Plan

  • Mirko Viroli
  • Danilo Pianini
  • Alessandro Ricci
  • Pietro Brunetti
  • Angelo Croatti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9387)

Abstract

Recent works foster the idea of engineering distributed situated systems by taking an aggregate stance: design and development are better conducted by abstracting away from individuals’ details, directly programming overall system behaviour instead. Concerns like interaction protocols, self-organisation, adaptation, and large-scaleness, are automatically hidden under the hood of the platform supporting aggregate programming. This paper aims at bridging the apparently significant gap between this idea and agent autonomy, paving the way towards an aggregate computing approach for multi-agent systems. Specifically, we introduce and analyse the idea of “aggregate plan”: a collective plan to be played by a dynamic team of cooperating agents.

Keywords

Multiagent System Autonomous Agent Tuple Space Dynamic Team Shared Plan 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Artikis, A., Sergot, M.J., Pitt, J.V.: Specifying norm-governed computational societies. ACM Trans. Comput. Log. 10(1) (2009)Google Scholar
  2. 2.
    Beal, J., Dulman, S., Usbeck, K., Viroli, M., Correll, N.: Organizing the aggregate: languages for spatial computing. In: Formal and Practical Aspects of Domain-Specific Languages: Recent Developments, chap. 16, pp. 436–501. IGI Global (2013). http://arxiv.org/abs/1202.5509
  3. 3.
    Beal, J., Pianini, D., Viroli, M.: Aggregate programming for the internet of things. IEEE Computer (2015)Google Scholar
  4. 4.
    Beal, J., Viroli, M.: Building blocks for aggregate programming of self-organising applications. In: IEEE Workshop on Foundations of Complex Adaptive Systems (FOCAS) (2014)Google Scholar
  5. 5.
    Beal, J., Viroli, M.: Space–time programming. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences 373(2046) (2015)Google Scholar
  6. 6.
    Bellifemine, F.L., Poggi, A., Rimassa, G.: Developing multi-agent systems with JADE. In: Castelfranchi, C., Lespérance, Y. (eds.) ATAL 2000. LNCS (LNAI), vol. 1986, p. 89. Springer, Heidelberg (2001) CrossRefGoogle Scholar
  7. 7.
    Boissier, O., Bordini, R.H., Hübner, J.F., Ricci, A., Santi, A.: Multi-agent oriented programming with jacamo. Science of Computer Programming 78(6), 747–761 (2013)CrossRefGoogle Scholar
  8. 8.
    Bratman, M.E.: Intention, Plans, and Practical Reason. Harvard University Press, Nov. 1987Google Scholar
  9. 9.
    Castelfranchi, C., Pezzulo, G., Tummolini, L.: Behavioral implicit communication (BIC): Communicating with smart environments via our practical behavior and its traces. International Journal of Ambient Computing and Intelligence 2(1), 1–12 (2010)CrossRefGoogle Scholar
  10. 10.
    Damiani, F., Viroli, M., Pianini, D., Beal, J.: Code mobility meets self-organisation: a higher-order calculus of computational fields. In: Graf, S., Viswanathan, M. (eds.) Formal Techniques for Distributed Objects, Components, and Systems. LNCS, vol. 9039, pp. 113–128. Springer, Heidelberg (2015) CrossRefGoogle Scholar
  11. 11.
    Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Communications of the ACM 51(1), 107–113 (2008)CrossRefGoogle Scholar
  12. 12.
    Elhage, N., Beal, J.: Laplacian-based consensus on spatial computers. In: 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010), pp. 907–914. IFAAMAS (2010)Google Scholar
  13. 13.
    Fernandez-Marquez, J.L., Serugendo, G.D.M., Montagna, S., Viroli, M., Arcos, J.L.: Description and composition of bio-inspired design patterns: a complete overview. Natural Computing 12(1), 43–67 (2013)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Gardelli, L., Viroli, M., Casadei, M., Omicini, A.: Designing self-organising environments with agents and artefacts: A simulation-driven approach. International Journal of Agent-Oriented Software Engineering 2(2), 171–195 (2008)CrossRefGoogle Scholar
  15. 15.
    Grosz, B.J., Hunsberger, L., Kraus, S.: Planning and acting together. AI Magazine 20(4), 23–34 (1999)Google Scholar
  16. 16.
    Grosz, B.J., Kraus, S.: Collaborative plans for complex group action. Artificial Intelligence 86(2), 269–357 (1996)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Kaelbling, L.P., Littman, M.L., Cassandra, A.R.: Planning and acting in partially observable stochastic domains. Artif. Intell. 101(1–2), 99–134 (1998)MathSciNetCrossRefMATHGoogle Scholar
  18. 18.
    Kalia, A.K., Singh, M.P.: Muon: designing multiagent communication protocols from interaction scenarios. Autonomous Agents and Multi-Agent Systems 29(4), 621–657 (2015)CrossRefGoogle Scholar
  19. 19.
    Lesser, V., Decker, K., Wagner, T., Carver, N., Garvey, A., Horling, B., Neiman, D., Podorozhny, R., Prasad, M., Raja, A., Vincent, R., Xuan, P., Zhang, X.: Evolution of the GPGP/TAEMS domain-independent coordination framework. Autonomous Agents and Multi-Agent Systems 9(1–2), 87–143 (2004)CrossRefGoogle Scholar
  20. 20.
    Levesque, H.J., Cohen, P.R., Nunes, J.H.T.: On acting together. In: Proceedings of the Eighth National Conference on Artificial Intelligence, AAAI 1990, vol. 1, pp. 94–99. AAAI Press (1990)Google Scholar
  21. 21.
    Madden, S.R., Szewczyk, R., Franklin, M.J., Culler, D.: Supporting aggregate queries over ad-hoc wireless sensor networks. In: Workshop on Mobile Computing and Systems Applications (2002)Google Scholar
  22. 22.
    Mallyak, A.U., Singh, M.P.: An algebra for commitment protocols. Autonomous Agents and Multi-Agent Systems 14(2), 143–163 (2007)CrossRefGoogle Scholar
  23. 23.
    Mamei, M., Zambonelli, F.: Programming pervasive and mobile computing applications: The TOTA approach. ACM Trans. on Software Engineering Methodologies 18(4), 1–56 (2009)CrossRefGoogle Scholar
  24. 24.
    MIT Proto. http://proto.bbn.com/ (retrieved on January 1, 2012)
  25. 25.
    Nagpal, R.: Programmable Self-Assembly: Constructing Global Shape using Biologically-inspired Local Interactions and Origami Mathematics. Ph.D. thesis, MIT (2001)Google Scholar
  26. 26.
    Omicini, A., Ossowski, S.: Objective versus subjective coordination in the engineering of agent systems. In: Klusch, M., Bergamaschi, S., Edwards, P., Petta, P. (eds.) Intelligent Information Agents. LNCS (LNAI), vol. 2586, pp. 179–202. Springer, Heidelberg (2003) CrossRefGoogle Scholar
  27. 27.
    Omicini, A., Ricci, A., Viroli, M.: Artifacts in the A&A meta-model for multi-agent systems. Autonomous Agents and Multi-Agent Systems 17(3), June 2008Google Scholar
  28. 28.
    Pianini, D., Montagna, S., Viroli, M.: Chemical-oriented simulation of computational systems with Alchemist. Journal of Simulation (2013)Google Scholar
  29. 29.
    Pianini, D., Viroli, M., Beal, J.: Protelis: Practical aggregate programming. In: Proceedings of ACM SAC 2015, pp. 1846–1853. ACM, Salamanca, Spain, (2015)Google Scholar
  30. 30.
    Ricci, A., Omicini, A., Viroli, M., Gardelli, L., Oliva, E.: Cognitive stigmergy: towards a framework based on agents and artifacts. In: Weyns, D., Van Dyke Parunak, H., Michel, F. (eds.) E4MAS 2006. LNCS (LNAI), vol. 4389, pp. 124–140. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  31. 31.
    Shoham, Y.: Agent-oriented programming. Artif. Intell. 60(1), 51–92 (1993)MathSciNetCrossRefGoogle Scholar
  32. 32.
    Stevenson, G., Ye, J., Dobson, S., Pianini, D., Montagna, S., Viroli, M.: Combining self-organisation, context-awareness and semantic reasoning: the case of resource discovery in opportunistic networks. In: ACM SAC, pp. 1369–1376. ACM (2013)Google Scholar
  33. 33.
    Tambe, M.: Towards flexible teamwork. J. Artif. Int. Res. 7(1), 83–124 (1997)Google Scholar
  34. 34.
    Taylor, M.E., Jain, M., Kiekintveld, C., Kwak, J., Yang, R., Yin, Z., Tambe, M.: Two decades of multiagent teamwork research: past, present, and future. In: Guttmann, C., Dignum, F., Georgeff, M. (eds.) CARE 2009 / 2010. LNCS, vol. 6066, pp. 137–151. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  35. 35.
    Viroli, M., Beal, J., Damiani, F., Pianini, D.: Efficient engineering of complex self-organising systems by self-stabilising fields. In: IEEE Conference on Self-Adaptive and Self-Organising Systems (SASO 2015) (2015)Google Scholar
  36. 36.
    Viroli, M., Casadei, M., Omicini, A.: A framework for modelling and implementing self-organising coordination. In: Proceedings of ACM SAC 2009, volume III, pp. 1353–1360. ACM, March 8–2, 2009Google Scholar
  37. 37.
    Viroli, M., Pianini, D., Montagna, S., Stevenson, G., Zambonelli, F.: A coordination model of pervasive service ecosystems. Science of Computer Programming, June 18, 2015Google Scholar
  38. 38.
    Weyns, D., Omicini, A., Odell, J.: Environment as a first class abstraction in multiagent systems. Autonomous Agents and Multi-Agent Systems 14(1), 5–30 (2007)CrossRefGoogle Scholar
  39. 39.
    Zambonelli, F., Viroli, M.: A survey on nature-inspired metaphors for pervasive service ecosystems. International Journal of Pervasive Computing and Communications 7(3), 186–204 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Mirko Viroli
    • 1
  • Danilo Pianini
    • 1
  • Alessandro Ricci
    • 1
  • Pietro Brunetti
    • 1
  • Angelo Croatti
    • 1
  1. 1.University of BolognaBolognaItaly

Personalised recommendations