Agent-Based Knowledge Analysis Framework in Disaster Management
Tóm tắt
Disaster Management (DM) is a complex set of interrelated activities. The activities are often knowledge-intensive and time sensitive. Timely sharing of the required knowledge is critical for DM. For recurring disasters (e.g. floods), developed countries tend to have dedicated document repositories of Disaster Management Plans (DISPLANs) that can be accessed as needs arise. However, accessing the appropriate plan in a timely manner, and sharing activities between plans, often requires significant domain knowledge and intimate understanding of the plans in the first place. This paper introduces an Agent-Based (AB) knowledge analysis method to convert DISPLANs into a collection of knowledge units that can be stored into a unified repository. The repository of DM actions then enables the mixing and matching of knowledge between different plans. The repository is structured as a layered abstraction according to Meta Object Facility (MOF). We use the flood DISPLANs plans used by SES (State Emergency Service), an authoritative DM agency in New South Wales (NSW) State of Australia (hereinafter referred to as SES NSW) to illustrate and give a preliminary validation of the approach. It is illustrated by using displans along the flood-prone Murrumbidgee river in central NSW.
Tài liệu tham khảo
Baral, C., & Giacomo, G. D. (2015). Knowledge representation and reasoning: what's hot. Paper presented at the Twenty-Ninth AAAI Conference on Artificial Intelligence.
Becken, S., Mahon, R., Rennie, H. G., & Shakeela, A. (2014). The tourism disaster vulnerability framework: An application to tourism in small island destinations. Natural Hazards, 71(1), 955–972.
Beydoun, G., Gonzalez-Perez, C., Henderson-Sellers, B., Low, G.C. (2006): Developing and evaluating a generic Metamodel for MAS work products. In: A. Garcia et. Al (ed.) software engineering for multi-agent systems IV: Research issues and practical applications, vol. LNCS 3914, pp. 126-142. Springer-Verlag, Berlin.
Beydoun, G., & Hoffmann, A. (2001). Theoretical basis for hierarchical incremental knowledge acquisition. International Journal of Human-Computer Studies, 54(3), 407–452.
Beydoun, G., Hoffmann, A., & Gill, A. (2017). Constructing enhanced default theories incrementally. Complex & Intelligent Systems, 1–10.
Beydoun, G., Low, G., Henderson-Sellers, B., Mouratidis, H., Gomez-Sanz, J. J., Pavon, J., & Gonzalez-Perez, C. (2009). FAML: A generic Metamodel for MAS development. IEEE Transactions on Software Engineering, 35(6), 841–863.
Beydoun, G., Low, G., Mouratidis, H., & Henderson-Sellers, B. (2009). A security-aware metamodel for multi-agent systems (MAS). Information and Software Technology, 51(5), 832–845.
Beydoun, G., Low, G., Tran, N., & Bogg, P. (2011). Development of a peer-to-peer information sharing system using ontologies. Expert Systems with Applications, 38(8), 9352–9364.
Briceño, S. (2015). What to expect after Sendai: Looking forward to more effective disaster risk reduction. International Journal of Disaster Risk Science, 6(2), 202–204.
Brown, R. B. K., Beydoun, G., Low, G., Tibben, W., Zamani, R., García-Sánchez, F., & Martinez-Bejar, R. (2016). Computationally efficient ontology selection in software requirement planning. Information Systems Frontiers, 2016(18), 349–358.
Cavallo, A., & Ireland, V. (2014). Preparing for complex interdependent risks: A system of systems approach to building disaster resilience. International Journal of Disaster Risk Reduction, 9(0), 181–193.
Chandrasegaran, S. K., Ramani, K., Sriram, R. D., Horváth, I., Bernard, A., Harik, R. F., & Gao, W. (2013). The evolution, challenges, and future of knowledge representation in product design systems. Computer-Aided Design, 45(2), 204–228.
Chen, N., Du, W., Song, F., & Chen, Z. (2015). FLCNDEMF: An event Metamodel for flood process information management under the sensor web environment. Remote Sensing, 7(6), 7231–7256.
Crawford, L., Langston, C., & Bajracharya, B. (2013). Participatory project management for improved disaster resilience. International Journal of Disaster Resilience in the Built Environment, 4(3), 317–333.
Daniel, F., & Matera, M. (2014). Model-driven software development Concepts, Models and Architectures (pp. 71–93). Berlin: Springer Berlin Heidelberg.
Dominey-Howes, D., Michael, C., & Labbate, M. (2014). Why emergency management should be interested in the emergence of antibiotic resistance. Australian Journal of Emergency Management (AJEM), 29(3), 11–15.
Doyle, E. E. H., McClure, J., Paton, D., & Johnston, D. M. (2014). Uncertainty and decision making: Volcanic crisis scenarios. International Journal of Disaster Risk Reduction, 10, Part A(0), 75-101.
Ernstsen, J., & Villanger, D. (2014). Situation Awareness in Disaster Management: A study of a Norwegian collaboration exercise. (master of philosophy in work and Organisational psychology), University of Oslo.
García-Magariño, I., & Gutiérrez, C. (2013). Agent-oriented modeling and development of a system for crisis management. Expert Systems with Applications, 40(16), 6580–6592.
Gregor, S., & Hevner, A. R. (2013). Positioning and presenting design science research for maximum impact. MIS Quarterly, 37(2), 337–A355.
Hawe, G. I., Coates, G., Wilson, D. T., & Crouch, R. S. (2012). Agent-based simulation for large-scale emergency response: A survey of usage and implementation. ACM Computing Surveys, 45(1), 1–51.
Heard, J., Thakur, S., Losego, J., & Galluppi, K. (2014). Big board: Teleconferencing over maps for shared situational awareness. Computer Supported Cooperative Work (CSCW), 23(1), 51–74.
Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information system research. MIS Quarterly, 28(1), 75–105.
Hiwasaki, L., Luna, E., Syamsidik, & Shaw, R. (2014). Process for integrating local and indigenous knowledge with science for hydro-meteorological disaster risk reduction and climate change adaptation in coastal and small island communities. International Journal of Disaster Risk Reduction, 10(Part A), 15–27.
Hotz, L., Felfernig, A., Stumptner, M., Ryabokon, A., Bagley, C., & Wolter, K. (2014). Chapter 6 - configuration knowledge representation and reasoning. In Knowledge-based configuration (pp. 41–72). Boston: Morgan Kaufmann.
Inan, D. I., Beydoun, G., & Opper, S. (2015). Towards knowledge sharing in disaster management: An agent oriented knowledge analysis framework. In Paper presented at the proceedings of the 26th Australasian conference on information systems (ACIS2015). Adelaide: South Australia.
Inan, D. I., Beydoun, G., & Opper, S. (2016). Customising agent based analysis towards analysis of disaster management knowledge. In Paper presented at the the 27th Australasian conference on information systems (ACIS2016). Wollongong NSW: Australia.
Jakus, G., Milutinović, V., Omerović, S., & Tomažič, S. (2013). Concepts, Ontologies, and Knowledge Representation (pp. 47–62). New York: Springer New York.
Janssen, M., Lee, J., Bharosa, N., & Cresswell, A. (2010). Advances in multi-agency disaster management: Key elements in disaster research. Information Systems Frontiers, 12(1), 1–7.
Kaptan, K. (2014). An organizational Metamodel for hospital emergency departments. Disaster Medicine and Public Health Preparedness, 8(05), 436–444.
Lauras, M., Truptil, S., & Bénaben, F. (2015). Towards a better management of complex emergencies through crisis management meta-modelling. Disasters, 39(4), 687–714.
Lopez-Lorca, A. A., Beydoun, G., Valencia-Garcia, R., & Martinez-Bejar, R. (2016). Supporting agent oriented requirement analysis with ontologies. International Journal of Human-Computer Studies, 87(2016), 20–37.
Markman, A. B. (2013). Knowledge representation. New York: Psychology Press.
Miller, T., Lu, B., Sterling, L., Beydoun, G., & Taveter, K. (2014). Requirements elicitation and specification using the agent paradigm: The case study of an aircraft turnaround simulator. IEEE Transactions on Software Engineering., 40(10), 1007–1014.
OMG. (2013). OMG Meta Object Facility (MOF) Core Specification Version 2.4.1: Object Management Group (OMG).
Oramas, S., Espinosa-Anke, L., Sordo, M., Saggion, H., & Serra, X. (2016). Information extraction for knowledge base construction in the music domain. Data & Knowledge Engineering, 106, 70–83.
Othman, S. H., & Beydoun, G. (2013). Model-driven disaster management. Information Management, 50(5), 218–228.
Othman, S. H., Beydoun, G., & Sugumaran, V. (2014). Development and validation of a disaster management Metamodel (DMM). Information Processing and Management, 50(2), 235–271.
Padgham, L., Horne, R., Singh, D., & Moore, T. (2014). Planning for sandbagging as a response to flooding: A tool and case study. The Australian Journal of Emergency Management (AJEM), 29(4), 26–31.
Ramete, G. M., Lamothe, J., Lauras, M., & Benaben, F. (2012). A road crisis management metamodel for an information decision support system. Paper presented at the 6th IEEE international conference on digital ecosystems technologies (DEST) 2012.
Sackmann, S., Hofmann, M., & Betke, H. (2013). Towards a model-based analysis of place-related information in disaster response workflows. In Paper presented at the proceedings of the 10th international conference on information Systems for Crisis Response and Management (ISCRAM). Germany: Baden Baden.
Scerri, D., Hickmott, S., Padgham, L., & Bosomworth, K. (2012). Using modular simulation and agent based modelling to explore emergency management scenarios. The Australian Journal of Emergency Management, 27(3), 44–48.
SES NSW Australia. (2006). WAGGA WAGGA local flood plan. State Emergency Service NSW Australia.
SES NSW Australia. (2010). LOCAL FLOOD EMERGENCY SUB template. State Emergency Service (SES): NSW.
SES NSW Australia. (2016). FloodSafe - State Emergency Service NSW. Retrieved from http://www.floodsafe.com.au/.
Sterling, L., & Taveter, K. (2009). The art of agent-oriented modeling. Cambridge: The MIT Press.
Sword-Daniels, V., Eriksen, C., Hudson-Doyle, E. E., Alaniz, R., Adler, C., Schenk, T., & Vallance, S. (2016). Embodied uncertainty: Living with complexity and natural hazards. Journal of Risk Research, 1–18.
Syriani, E., Gray, J., & Vangheluwe, H. (2013). Modeling a model transformation language. In I. Reinhartz-Berger, A. Sturm, T. Clark, S. Cohen, & J. Bettin (Eds.), Domain Engineering (pp. 211-237): Springer: Berlin.
Tran, N., Beydoun, G., & Low, G. (2007). Design of a Peer-to-Peer Information Sharing MAS using MOBMAS (ontology-centric agent oriented methodology). In W. Wojtkowski, W. G. Wojtkowski, J. Zupancic, G. Magyar, & G. Knapp (Eds.), Advances in information systems development: New methods and practice for the networked society (pp. 63–76). Boston: Springer US.
Weichselgartner, J., & Pigeon, P. (2015). The role of knowledge in disaster risk reduction. International Journal of Disaster Risk Science, 6(2), 107–116.
Wex, F., Schryen, G., Feuerriegel, S., & Neumann, D. (2014). Emergency response in natural disaster management: Allocation and scheduling of rescue units. European Journal of Operational Research, 235(3), 697–708.
Whittle, J., Hutchinson, J., & Rouncefield, M. (2014). The state of practice in model-driven engineering. IEEE Software, 31(3), 79–85.
Winikoff, M., & Padgham, L. (2013). Agent-oriented software engineering. In G. Weiss (Ed.), Multiagent Systems, 2nd Edition, Ch. 15. Cambridge: MIT press.
Xu, D., Wijesooriya, C., Wang, X.-G., & Beydoun, G. (2011). Outbound logistics exception monitoring: A multi-perspective ontologies’ approach with intelligent agents. Expert Systems with Applications, 38(11), 13604–13611.