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.
Members of the department of Information Science 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.
- Dr Maryam Purvis
- Emeritus Professor Martin Purvis
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
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)