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The field of autonomous agents and multi-agent systems (AAMAS) can be seen as the study of human-inspired computational mechanisms. It is a diverse multidisciplinary field, drawing on disciplines such as Artificial Intelligence, Distributed and Autonomous Computing, Software Engineering, Economics, Human Computer Interaction and Psychology.

Multi-agent systems are a paradigm of choice for modelling complex systems that involve a large number of interacting entities which exhibit emergent global properties. There are many applications of multi-agent systems including production scheduling, simulation in a range of domains, energy production and distribution, transport logistics, crisis management, flexible manufacturing, air traffic control, and business process management.

Staff members are active in the following areas:
  • software agents that use human-like notions of autonomy, goals and plans to respond to changes and opportunities;
  • models of agent cooperation, and of organizations, based on human organizational principles;
  • mechanisms for computational societies based on norms, expectations and social laws;
  • work on the design and implementation of agent systems; and
  • work on infrastructure support (architectures, platforms and tools) for such systems.

Members

Core Members:

Adjunct Members:

Selected Recent Publications

  • Stephen Cranefield and Surangika Ranathunga, Handling agent perception in heterogeneous distributed systems: a policy-based approach, International Conference on Coordination Models and Languages, 2015.
  • Hoa Khanh Dam, Tony Savarimuthu, Daniel Avery, Aditya Ghose, "Mining Software Repositories for Social Norms", ICSE New Ideas and emerging results track, 2015.
  • Hoa Khanh Dam, Alexander Egyed, Michael Winikoff, Alexander Reder, and Roberto E. Lopez-Herrejon. Consistent merging of model versions. Journal of Systems and Software (available online 2015)
  • Yoosef Abushark, John Thangarajah, Tim Miller, James Harland, Michael Winikoff. Early detection of design faults relative to requirement specifications in agent-based models. In: Proceedings of the 14th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2015), May, Istanbul, Turkey, 2015.
  • Winikoff, M., & Cranefield, S. On the testability of BDI agent systems. Journal of Artificial Intelligence Research, 51, 71-131, 2014. doi: 10.1613/jair.4458
  • Akin Günay, Michael Winikoff, and Pinar Yolum. Dynamically Generated Commitment Protocols in Open Systems. Journal of Autonomous Agents and Multi-Agent Systems (JAAMAS). doi:10.1007/s10458-014-9251-7
  • Michael Winikoff. Novice Programmers' Errors & Faults in GOAL Programs: Empirical Observations and Lessons. In: Proceedings of the 13th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2014), May, Paris, France, 2014.
  • Sharmila Savarimuthu, Maryam Purvis, Martin K. Purvis and Bastin Tony Roy Savarimuthu, Gossip-Based Self-Organising Agent Societies and the Impact of False Gossip, Minds and Machines: Journal for Artificial Intelligence, Philosophy and Cognitive Science, ISSN 0924-6495, DOI 10.1007/s11023-013-9304-8, 2013.
  • Christopher Cheong and Michael Winikoff. A Comparison of Two Agent Interaction Design Approaches. Multi-agent and Grid Systems (an international journal), volume 9, pages 1-44, 2013.
  • Dam, H. K., & Winikoff, M. (2012).  Towards a next-generation AOSE methodology. Science of Computer Programming. http://dx.doi.org/10.1016/j.scico.2011.12.005
  • Ranathunga, S., Cranefield, S. & Purvis, M. (2012). Identifying Events Taking Place in Second Life Virtual Environments. Applied Artificial Intelligence, 26(1-2), 137–18. DOI 10.1080/08839514.2012.629559
  • Cranefield, S., & Winikoff, M. (2011).  Verifying social expectations by model checking truncated paths. Journal of Logic and Computation, 21(6), 1217-1256. http://dx.doi.org/10.1093/logcom/exq055 (Free access is available via this link)
  • Dam, H. K., & Winikoff, M. (2011).  An agent-oriented approach to change propagation in software maintenance. Autonomous Agent and Multi-Agent Systems,23(3), 384-452. http://dx.doi.org/10.1007/s10458-010-9163-0
  • Dastani, M., van Riemsdijk, M. B., & Winikoff, M. (2011).  Rich goal types in agent programming.  In K.Tumer, P. Yolum, L. Sonenburg, & P. Stone (Eds.), Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), pp. 405-412. http://www.aamas-conference.org/Proceedings/aamas2011/papers/B3_B54.pdf
  • Savarimuthu, B. T. R., & Cranefield, S. (2011).  Norm creation, spreading and emergence: A survey of simulation models of norms in multi-agent systems. Multiagent & Grid Systems,7(1), 21-54. http://dx.doi.org/10.3233/MGS-2011-0167 (A freely available copy can be found here)
  • Ebadi, T., Purvis, M., & Purvis, M. (2010).  A framework for facilitating cooperation in multi-agent systems. Journal of Supercomputing, 51(3), 393-417. http://dx.doi.org/10.1007/s11227-009-0372-8

Publications

Shamoug, A., Cranefield, S., & Dick, G. (2023). SEmHuS: A semantically embedded humanitarian space. Journal of International Humanitarian Action, 8(3).  doi: 10.1186/s41018-023-00135-4

Sharma, P. N., Savarimuthu, B. T. R., & Stanger, N. (2023). How are decisions made in open source software communities? Uncovering rationale from python email repositories. Journal of Software: Evolution & Process. Advance online publication. doi: 10.1002/smr.2526

Srivathsan, S., Cranefield, S., & Pitt, J. (2022). Reasoning about collective action in Markov logic: A case study from classical Athens.  In N. Ajmeri, A. M. Martin & B. T. R. Savarimuthu (Eds.), Coordination, organizations, institutions, norms, and ethics (COINE) for governance of multi-agent systems XV: Revised selected papers: Lecture notes in artificial intelligence (Vol. 13549). (pp. 201-212). Cham, Switzerland: Springer.  doi: 10.1007/978-3-031-20845-4_13

Ajmeri, N., Martin, A. M., & Savarimuthu, B. T. R. (Eds.). (2022). Coordination, organizations, institutions, norms, and ethics (COINE) for governance of multi-agent systems XV: Revised selected papers: Lecture notes in artificial intelligence (Vol. 13549). Cham, Switzerland: Springer, 241p. doi: 10.1007/978-3-031-20845-4

Srivathsan, S., Cranefield, S., & Pitt, J. (2022). A Bayesian model of information cascades.  In A. Theodorou, J. C. Nieves & M. De Vos (Eds.), Coordination, organizations, institutions, norms, and ethics for governance of multi-agent systems XIV: International workshop COINE 2021, revised selected papers: Lecture notes in artificial intelligence (Vol. 13239). (pp. 97-110). Cham, Switzerland: Springer.  doi: 10.1007/978-3-031-16617-4_7

Archive of Publications

http://www.secml.otago.ac.nz/dcsa/pubs.html (2009 and earlier)

Software Developed

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