Use of the Multi-Agent Paradigm in Sustainable Tourism

Open access

Abstract

Complex systems are characterised by a huge amount of components, which are highly linked with each other. Tourism is one of the examples of complex systems collecting various activities leading to the enrichment of travellers in the view of receiving new experiences and increasing economic prosperity of specific destinations. The complex systems can be investigated with various bottom-up and top-down approaches. The multi-agent-based modelling is the bottom-up approach that is focused on the representation of individual entities for the exploration of possible interactions among them and their effects on surrounding environments. These systems are able to integrate knowledge of socio-cultural, economic, physical, biological or environmental systems for in-silico models development, which can be used for experimentation with a system. The main aim of the presented text is to introduce links between tourism, complexity and to advocate usefulness of the multi-agent-based systems for the exploration of tourism and its sustainability. The evaluation of suitability of the multi-agent systems in tourism is based on the investigation of fundamental characteristics of these two systems and on the review of specific applications of the multi-agent systems in sustainable tourism.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • Axelrod R. (2003). Advancing the Art of Simulation in the Social Sciences. Journal of the Japanese Society for Management Information Systems. Special Issue on Agent-Based Modeling 12(3) 16–2.

  • Baggio R. (2005). Complex Systems Information Technologies and Tourism: A Network Point of View. Information Technology and Tourism 8(1) 1529. DOI: 10.3727/109830506778193850.

  • Baggio R. (2014). Complex tourism systems: a visibility graph approach. Kybernetes 43(3/4) 445–461. DOI: 10.1108/K-12–2013–0266.

  • Baggio R. & Baggio J. A. (2013). Modeling Information Asymmetries in Tourism. In M. Kozak et al. (Eds.) Tourism Marketing: On Both Sides of the Country (pp. 154–174). Cambridge: Scholars Publishing.

  • Balbi S. Perez P. & Giupponi C. (2010). A spatial agent-based model to explore scenarios of adaptation to climate change in an alpine tourism destination. In A. Ernst & S. Kuhn (Eds.) 3rd World Congress on Social Simulation (pp. 700–707). Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1566861.

  • Borshchev A. (2013). The Big Book of Simulation Modeling: Multimethod Modeling with AnyLogic 6. AnyLogic North America. ISBN 978–0989573177.

  • Bustos F. López J. Julián V. & Rebollo M. (2009). STRS: Social Network Based Recommender System for Tourism Enhanced with Trust. In Proceedings of the International Symposium on Distributed Computing and Artificial Intelligence (pp. 71–79). Berlin Heidelberg: Springer-Verlag. DOI: 10.1007/978–3-540–85863–8_10.

  • Chao D. Furuta K. & Kanno T. (2011). A Framework for Agent-Based Simulation in Tourism Planning. Proceedings of the Human-Computer Interaction. Towards Mobile and Intelligent Interaction Environments (Part III pp. 280–287). Berlin Heidelberg: Springer-Verlag. DOI: 10.1007/978–3-642–21616–9_31.

  • Charbonneau D. Blonder B. & Dornhaus A. (2013). Social Insects: A Model System for Network Dynamics. In P. Holme & J. Saramäki (Eds.) Temporal Networks – Understanding Complex Systems (pp. 217–244). Berlin Heidelberg: Springer-Verlag. DOI: 10.1007/978–3-642–36461–7_11.

  • Collier N. Howe T. & North M. (2003). Onward and upward: The transition to Repast 2.0. In K. Carley (Ed.) Proceedings of the first annual North Americal Association for Computational Social and Organisational Science conference.

  • Das D. & Gupta S. (2014). Communication Cooperation Coordination and Cognition of a Multi-Agent System - A Literature Survey. International Journal of Latest Research in Science and Technology 3(2) 147–155. ISSN (Online): 2278–5299.

  • Derakhshan F. Parandeh M. & Moradnejad A. (2016). An Agent-Based Mobile Recommender System for Tourisms. In Proceedings of 8th Research World International Conference Berlin (pp. 30–34). ISBN 978–93–85973–05–5.

  • Doscher C. Moore K. Smallman C. Wilson J. & Simmons D. G. (2011). An agent-based model of tourist movements in New Zealand: implications for spatial yield. In 19th International Congress on Modelling and Simulation. Modelling and Simulation (pp. 2908–2913). Society of Australia and New Zealand. ISBN 978–0-9872143–1-7.

  • Durlauf S. N. (1998). What Should Policymakers Know About Economic Complexity? The Washington Quarterly 21 155–165. Retrieved from https://www.santafe.edu/media/workingpapers/97–10–080.pdf.

  • Foote R. (2007). Mathematics and complex systems. Science 318 410–412. DOI: 10.1126/science.1141754.

  • Holloway J. C. Humphreys C. & Davidson R. (2009). The Business of Tourism. Pearson Education Limited 8th ed. ISBN 978–0-273–71710–2.

  • Horling B. & Lesser V. (2004). A Survey of Multi-Agent Organizational Paradigms. The Knowledge Engineering Review 19(4) 281–316. DOI: 10.1017/S0269888905000317.

  • Johnson P. A. & Sieber R. E. (2009). Agent-based modelling: A Dynamic Scenario Planning Approach to Tourism PSS. In S. Geertman & J. Stillwell (Eds.) Planning support systems: Best practices and new methods (pp. 211–226). Berlin Heidelberg: Springer-Verlag. DOI: 10.1007/978–1-4020–8952–7_11.

  • Kaur H. Kahlon K. S. & Virk R. S. (2014). Migration Dynamics in Artifical Agent Societies. International Journal of Advanced Research in Artificial Intelligence 3(2) 39–47. DOI: 10.14569/IJARAI.2014.030208.

  • Kravari K. & Bassiliades N. (2015). A Survey of Agent Platforms. Journal of Artificial Societies and Social Simulation 18(1). Retrieved from http://jasss.soc.surrey.ac.uk/18/1/11.html. DOI: 10.18564/jasss.2661.

  • Lin L. Carley K. M. & Cheng S. F. (2016). An Agent-Based Approach to Human Migration Movement. In T. M. K. Roeder P. I. Frazier R. Szechtman E. Zhou T. Huschka & S. E. Chick (Eds.) Proceedings of the 2016 Winter Simulation Conference (pp. 3510–3520). Arlington: Research Collection School of Information Systems. DOI: 10.1109/WSC.2016.7822380.

  • Lopez J. S. Bustos F. A. & Julian V. (2007). Tourism services using agent technology: A multi-agent approach. In INFOCOMP Journal of Computer Science (Special edition pp. 51–57).

  • Luke S. Cioffi-Revilla C. Panait L. Sullivan K. & Balan G. (2005). Mason: A multiagent simulation environment. Simulation: Transactions of the Society for Modeling and Simulation Simulation 82(7) 517–527. DOI: 10.1177/0037549705058073.

  • Marquez B. Y. Espinoza-Hernandez I. & Magdaleno-Palencia J. S. (2012). Sustainable System Modelling for Urban Development Using Distributed Agencies. In C. Ghenai (Ed.) Sustainable Development – Policy and Urban Development – Tourism Life Science Management and Environment. InTech. DOI: 10.5772/28898.

  • Mills A. (2010). Complexity Science: An introduction (and invitation) for actuaries. Society of Actuaries. Retrieved from https://www.soa.org/files/research/projects/research-complexity-report.pdf.

  • Perrin D. Ruskin H. J. Burns J. & Crane M. (2006 May). An agent-based approach to immune modelling. In International Conference on Computational Science and Its Applications (pp. 612–621). Berlin Heidelberg: Springer-Verlag. DOI: 10.1007/11751540_65.

  • Pizzitutti F. Carlos F. M. & Walsh S. J. (2014). Modelling Tourism in the Galapagos Islands: An Agent-Based Model Approach. Journal of Artificial Societies and Social Simulation 17(1) Retrieved from http://jasss.soc.surrey.ac.uk/17/1/14.html. DOI: 10.18564/jasss.2389.

  • Pons-Pons M. Johnson P. A. Rosas-Casals M. Sureda B. & Jover È. (2012). Modeling climate change effects on winter ski tourism in Andorra. Climate research 54(3) 197–207. DOI: 10.3354/cr01117.

  • Pouyan A. A. Beigi A. H. & Kadkhoda M. (2006). An Agent-Based Model for Virtual Tourism Using Object Petri Nets. In Proceedings of the 5th WSEAS Int. Conf. on Circuits Systems Electronics Control and Signal Processing (pp. 149–154). Dallas: USA. ISBN: 960–8457–55–6.

  • San Miguel M. Johnson J. H. Kertesz J. Kaski K. Díaz-Guilera A. MacKay R. S. & Helbing D. (2012). Challenges in complex systems science. arXiv:1204.4928v1 [nlin.AO]. The European Physical Journal Special Topics 214(1) 245–271. Retrieved from https://arxiv.org/abs/1204.4928.

  • Scott N. R. Cooper C. P. & Baggio R. (2007). Use of network analysis in tourism research. In L. Andreu J. Gnoth & M. Kozak (Eds.) Advances in Tourism Marketing Conference Valencia: Spain. Retrieved from http://www.iby.it/turismo/papers/baggio-ATMC2007–1.pdf.

  • Sebastia L. Garcia I. Onaindia E. & Guzman C. (2009). e-Tourism: a tourist recommendation and planning application. International Journal on Artificial Intelligence Tools 18(5) 717–738. DOI: 10.1109/ICTAI.2008.18.

  • Sebastia L. Giret A. & Garcia I. (2010). A Multi Agent Architecture for Tourism Recommendation. In Proceedings of the 8th International Conference on Practical Applications of Agents and Multiagent Systems (pp. 547–554). Berlin Heidelberg: Springer-Verlag. DOI: 10.1142/S0218213009000378.

  • Sheard S. Cook S. Honour E. Hybertson D. Krupa J. McEver J. & Singer J. (2015). A Complexity Primer for Systems Engineers. INCOSE Complex Systems Working Group White Paper. Retrieved from: https://www.aiaa.org/uploadedFiles/Events/Complexity%20Primer%20for%20SE%20July%202015.pdf.

  • Shoham Y. & Leyton-Brown K. (2008). Multiagent Systems: Algorithmic Game-Theoretic and Logical Foudnations. Cambridge: University Press 1st ed. ISBN 978–0521899437

  • Smeral E. (1993). Aspects to justify pubic tourism promotion: An economic perspective. Tourism Review 61(3) 6–14. DOI: 10.1108/eb058474.

  • United Nations Environment Programme & United Nations World Tourism Organisation. (2005). Making Tourism More Sustainable – A Guide for Policy Makers. UNEP UNWTO. http://www.unep.fr/scp/publications/details.asp?id=DTI/0592/PA. Accessed August 2005

  • Varfolomeyev A. Korzun D. Ivanovs A. Soms H. & Petrina O. (2015). Smart space based recommendation service for historical tourism. Procedia Computer Science 77 85–91. DOI: 10.1016/j.procs.2015.12.363.

  • Vig L. & Adams J. A. (2006). Multi-Robot Coalition Formation. IEEE Transactions On Robotics 22(4) 637–649. DOI: 10.1109/TRO.2006.878948.

  • Weiss G. (2000). Multi-agent Systems: A Modern Approach to Distributed Artificial Intelligence. England Cambridge Massechusetts: MIT Press. ISBN 978–0262731317.

  • Wilensky U. (1999). NetLogo (and NetLogo User Manual). Center for Connected Learning and Computer-Based Modeling Northwestern University. Retrieved from http://ccl.northwestern.edu/netlogo/.

  • Woodridge M. & Jennings N. R. (1995). Intelligent agents: theory and practice. The Knowledge Engineering Review 10(2) 115–152.

  • Wooldridge M. J. (2013). Intelligent Agents. In G. Weiss (Ed.) Multi-Agent Systems (2nd edn. pp. 3–50). England: MIT Press. ISBN 978–0-262–01889–0.

Search
Journal information
Cited By
Metrics
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 191 191 5
PDF Downloads 167 167 11