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Coordination models and languages: from parallel computing to self-organisation

Published online by Cambridge University Press:  07 February 2011

Andrea Omicini  and
Mirko Viroli
Andrea Omicini*
Affiliation:
DEIS–Dipartimento di Elettronica, Informatica e Sistemistica, Alma Mater Studiorum–Università di Bologna, via Venezia 52, 47521 Cesena, Italy; e-mail: andrea.omicini@unibo.it, mirko.viroli@unibo.it
Mirko Viroli*
Affiliation:
DEIS–Dipartimento di Elettronica, Informatica e Sistemistica, Alma Mater Studiorum–Università di Bologna, via Venezia 52, 47521 Cesena, Italy; e-mail: andrea.omicini@unibo.it, mirko.viroli@unibo.it
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Abstract

Starting from the pioneering work on Linda and Gamma, coordination models and languages have gone through an amazing evolution process over the years. From closed to open systems, from parallel computing to multi-agent systems and from database integration to knowledge-intensive environments, coordination abstractions and technologies have gained in relevance and power in those scenarios where complexity has become a key factor. In this paper, we outline and motivate 25 years of evolution of coordination models and languages, and discuss their potential perspectives in the future of artificial systems.

Type
Articles
Information
The Knowledge Engineering Review , Volume 26 , Special Issue 1: 25th Anniversary Issue , 07 February 2011 , pp. 53 - 59
Copyright
Copyright © Cambridge University Press 2011

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References

Banătre, J.-P., Fradet, P., Le Métayer, D. 2001. Gamma and the chemical reaction model: fifteen years after. In Multiset Processing. Mathematical, Computer Science, and Molecular Computing Points of View, Calude, C. S., Păun, G., Rozenberg, G. & Salomaa, A. (eds). Lecture Notes in Computer Science 2235, 17–44. Springer.CrossRefGoogle Scholar
Berry, G. 1992. The chemical abstract machine. Theoretical Computer Science 96(1), 217–248.CrossRefGoogle Scholar
Bravetti, M., Latella, D., Loreti, M., Massink, M., Zavattaro, G. 2009. Combining timed coordination primitives and probabilistic tuple spaces. In Trustworthy Global Computing, Kaklamanis, C. & Nielson, F. (eds). Lecture Notes in Computer Science 5474, 52–68. Springer.CrossRefGoogle Scholar
Brogi, A., Ciancarini, P. 1991. The concurrent language, Shared Prolog. ACM Transactions on Programming Languages and Systems (TOPLAS) 13(1), 99–123.CrossRefGoogle Scholar
Busi, N., Ciancarini, P., Gorrieri, R., Zavattaro, G. 2001. Coordination models: a guided tour. In Coordination of Internet Agents: Models, Technologies, and Applications, Omicini, A., Zambonelli, F., Klusch, M. & Tolksdorf, R. (eds). Springer, 6–24.Google Scholar
Cabri, G., Leonardi, L., Zambonelli, F. 2000. MARS: a programmable coordination architecture for mobile agents. IEEE Internet Computing 4(4), 26–35.CrossRefGoogle Scholar
Casadei, M., Omicini, A. 2009. Situated tuple centres in ReSpecT. In 24th Annual ACM Symposium on Applied Computing (SAC 2009), Shin, S. Y., Ossowski, S., Menezes, R. & Viroli, M. (eds). ACM, Honolulu, Hawai'i, USA.Google Scholar
Corkill, D. 1991. Blackboard systems. Journal of AI Expert 9(6), 40–47.Google Scholar
De Nicola, R., Ferrari, G., Pugliese, R. 1998. KLAIM: a kernel language for agent interaction and mobility. IEEE Transaction on Software Engineering 24(5), 315–330.CrossRefGoogle Scholar
Freeman, E., Hupfer, S., Arnold, K. 1999. JavaSpaces Principles, Patterns, and Practice: Principles, Patterns and Practices. The Jini Technology Series, Addison-Wesley Longman.Google Scholar
Gelernter, D. 1985. Generative communication in Linda. ACM Transactions on Programming Languages and Systems 7(1), 80–112.CrossRefGoogle Scholar
Gelernter, D., Carriero, N. 1992. Coordination languages and their significance. Communications of the ACM 35(2), 97–107.CrossRefGoogle Scholar
Grassé, P.-P. 1959. La reconstruction du nid et les coordinations interindividuelles chez bellicositermes natalensis et cubitermes sp. la théorie de la stigmergie: Essai d'interprétation du comportement des termites constructeurs. Insectes Sociaux 6(1), 41–80.CrossRefGoogle Scholar
Mamei, M., Zambonelli, F. 2004. Programming pervasive and mobile computing applications with the TOTA middleware. In Pervasive Computing and Communications. 2nd IEEE Annual Conference (PerCom 2004), Orlando, FL, USA, 263–273.Google Scholar
Mamei, M., Zambonelli, F. 2006. Field-based Coordination for Pervasive Multiagent Systems. Models, Technologies, and Applications, Springer Series in Agent Technology, Springer.Google Scholar
Minsky, N. H., Ungureanu, V. 2000. Law-governed interaction: a coordination and control mechanism for heterogeneous distributed systems. ACM Transactions on Software Engineering and Methodology (TOSEM) 9(3), 273–305.CrossRefGoogle Scholar
Murphy, A. L., Picco, G. P., Roman, G.-C. 2006. Lime: a coordination model and middleware supporting mobility of hosts and agents. ACM Transactions on Software Engineering and Methodology 15(3), 279–328.CrossRefGoogle Scholar
Nardini, E., Viroli, M., Panzavolta, E. 2010. Coordination in open and dynamic environments with TuCSoN semantic tuple centres. In 25th Annual ACM Symposium on Applied Computing (SAC 2010), Shin, S. Y., Ossowski, S., Schumacher, M., Palakal, M., Hung, C.-C. & Shin, D. (eds). III, ACM, Sierre, Switzerland, 2037–2044.Google Scholar
Nixon, L., Simperl, E., Krummenacher, R., Martin-recuerda, F. 2008. Tuplespace-based computing for the Semantic Web: a survey of the state-of-the-art. Knowledge Engineering Review 23(2), 181–212.CrossRefGoogle Scholar
Omicini, A. 2001. Coordination models and languages: state of the art. Introduction. Omicini, A. et al. (eds). Springer, 3–5.Google Scholar
Omicini, A., Denti, E. 2001. From tuple spaces to tuple centres. Science of Computer Programming 41(3), 277–294.CrossRefGoogle Scholar
Omicini, A., Zambonelli, F., Klusch, M., Tolksdorf, R. (eds). 2001. Coordination of Internet Agents: Models, Technologies, and Applications. Springer-Verlag.CrossRefGoogle Scholar
Omicini, A., Ricci, A., Viroli, M., Castelfranchi, C., Tummolini, L. 2004. Coordination artifacts: Environment-based coordination for intelligent agents. In 3rd international Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2004), Jennings, N. R., Sierra, C., Sonenberg, L. & Tambe, M. (eds). 1, ACM, New York, USA, 286–293.Google Scholar
Papadopoulos, G. A., Arbab, F. 1998. Coordination models and languages. In The Engineering of Large Systems, Vol. 46 of Advances in Computers, Zelkowitz, M. V. (ed.). Academic Press, 329–400.Google Scholar
Ricci, A., Viroli, M., Omicini, A. 2005. Environment-based coordination through coordination artifacts. In Environments for Multi-Agent Systems, Weyns, D., Parunak, H. V. D. & Michel, F. (eds). Lecture Notes in Artificial Intelligence 3374, 190–214. Springer.CrossRefGoogle Scholar
Rossi, D., Cabri, G., Denti, E. 2001. Tuple-based technologies for coordination. In Coordination of Internet Agents: Models, Technologies, and Applications, Omicini, A., Zambonelli, F., Klusch, M. & Tolksdorf, R. (eds). Springer, 83–109.Google Scholar
Tolksdorf, R., Menezes, R. 2004. Using swarm intelligence in Linda systems. In Engineering Societies in the Agents World IV, Omicini, A., Petta, P. & Pitt, J. (eds). Lecture Notes in Computer Science 3071, 49–65. Springer.CrossRefGoogle Scholar
Tolksdorf, R., Nixon, L., Simperl, E. 2008. Towards a tuplespace-based middleware for the Semantic Web. Web Intelligence and Agent Systems 6(3), 235–251.CrossRefGoogle Scholar
Viroli, M., Zambonelli, F. 2010. A biochemical approach to adaptive service ecosystems. Information Sciences 180(10), 1876–1892.CrossRefGoogle Scholar
Viroli, M., Casadei, M., Omicini, A. 2009. A framework for modelling and implementing self-organising coordination. In 24th Annual ACM Symposium on Applied Computing (SAC 2009), Shin, S. Y., Ossowski, S., Menezes, R. & Viroli, M. (eds). III, ACM, Honolulu, Hawai'i, USA, 1353–1360.Google Scholar
Viroli, M., Casadei, M., Nardini, E., Omicini, A. 2010, Towards a chemical-inspired infrastructure for self-* pervasive applications. In Self-Organizing Architectures, Weyns, D., Malek, S., de Lemos, R. & Andersson, J. (eds). Lecture Notes in Computer Science 6090, chapter 8, 152–176. Springer.CrossRefGoogle Scholar
Wegner, P. 1997. Why interaction is more powerful than algorithms. Communications of the ACM 40(5), 80–91.CrossRefGoogle Scholar
Wyckoff, P., McLaughry, S. W., Lehman, T. J., Ford, D. A. 1998. T Spaces. IBM Journal of Research and Development 37(3 – Java Techonology), 454–474.Google Scholar