ABSTRACT
Multi-agent models have been used to simulate complex systems in many domains. In some models, the agents move in a physical/grid space and are constrained by their locations on the spatial space, e.g. Sugarscape. In others, the agents interact in a social multi-dimensional space and are bound to their knowledge and social positions, e.g. Construct. However, many real world problems require a mixed model containing both spatial and social features. This paper introduces such a multi agent system, Construct-Spatial, which simulates agent communication and movement simultaneously. It is an extended version of Construct, which is a multi-agent social model, and its extension is based on a multi-agent grid model, Sugarscape. To understand the impact of this integration of the two spaces, we run virtual experiments and compare the output from the combined space to those from each of the two spaces. The initial analysis reveals that the integration facilitates unbalanced knowledge distribution across the agents compared to the grid-only model and limits agent network connections compared to the social network model without spatial constraints. After the comparisons, we setup what-if scenarios where we varied the type of the threats faced by network and observe their emergent behaviors. From the what-if analyses, we locate the best destabilization scenario and find the propagation of the effects from the spatial space to the social network space. We believe that this model can be a conceptual model for assessing the efficiency and the robustness of team deployments, network node distributions, sensor distributions, etc.
- Buzing, P. C., Eiben, A. E., and Schut, M. C. (2003) Evolving Agent Societies with VUScape. Advances in Artificial Life, Proceedings of the 7th European Conferenceon Artificial Life (ECAL 2003), Lecture Notes in Artificial Intelligence, volume 2801, Springer Verlag, 2003, pp.434--441Google Scholar
- Carley, K. M. (2003) Dynamic network analysis. in Dynamic Social Network Modeling and Analysis: Workshop Summary and Papers, R. Breiger, K. Carley, & P. Pattison, (Eds.) Comitteeon Human Factors, National Research Council, Pp. 133--145Google Scholar
- Carley, K. M., Fridsma, D. B., Casman, E., Yahja, A., Altman, N., Li-Chiou C, Kaminsky, B., and Nave, D. (2006) BioWar: Scalable Agent-based Model of Bioattacks. IEEE Transactions on Systems, Man, and Cybernetics., Volume 36, Issue 2, pp 252--265 Google ScholarDigital Library
- Carley, K. M. and Hill, V. (2001) Structural change and Learning Within Organizations. MIT Press/AAAI Press/Live Oak.Google Scholar
- Carley, K. and Schreiber, C. (2002) Information Technology and Knowledge Distribution in C3I teams. Proceedings of the 2002 Command and Control Research and Technology Symposium, Naval Postgraduate School, Monterey, CAGoogle Scholar
- Epstein, J. and Axtell, R. (1997) Growing Artificial Societies. Boston, MA:MIT PressGoogle Scholar
- Gaston, M. and desJardins, M. (2005) Agent-Organized Networks for Dynamic Team Formation. In Proceedings of the Fourth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2005). Utrecht, Netherlands, July 2005. Google ScholarDigital Library
- Hollingshead, A. B. (2000) Perceptions of expertise and transactive memory in work relationships. Group Processes and Intergroup Relations, 3, 257--267Google ScholarCross Ref
- Kunz, J. C., Levitt, R. E., and Jin, Y. (1998) The Virtual Team Design: A Computational Simulation Model of Project Organizations, Communications of the Association for Computing Machinery, 41(11), pp 84--92 Google ScholarDigital Library
- Louie, M. A., and Carley, K. M. (2006) The Role of Multi-Agent Models of Socio-Political Systems in Policy, working paper, CASOS, Carnegie Mello UniversityGoogle Scholar
- McPherson, M, Smith-Lovin, L. and Cook, J. (2001) Birds of a Feather: Homophily in Social Networks. Annual Review of Sociology, 27, pp. 415--444.Google ScholarCross Ref
- Moon, I. and Carley, K. M. (2006) Estimating the near-term changes of an organization with simulations, AAAI Fall Symposium, Arlington, VA, Oct 12--15, 2006Google Scholar
- Schermerhorn, P. and Scheutz, M. (2006) Social Coordination without Communication in Multi-Agent Territory Exploration Tasks. In The Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS-06), Hakodate, Japan, May 2006. Google ScholarDigital Library
- Schreiber, C. and Carley, K. (2004) Going beyond the Data: Empirical Validation Leading to Grounded Theory, Computational and Mathematical Organization Theory, 10, pp 155--164 Google ScholarDigital Library
- Schreiber, C. and Carley, K. M. (2004) Key personnel: Identification and assessment of turnover risk, Proceedings of NAACSOS, Pittsburgh, PAGoogle Scholar
- Sukthankar, G. and Sycara, K. (2006) Robust Recognition of Physical Team Behaviors using Spatio-temporal Models, in Proceedings of Fifth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), May, 2006. Google ScholarDigital Library
- Tsvetovat, M. (2005) Social structure simulation and inference using artificial intelligence techniques. Ph. D. Thesis, Carnegie Mellon University. CMU-ISRI-05-115 Google ScholarDigital Library
- Louie, M. A., Carley, K. M., Haghshenass, L., Kunz, J. C. and Levitt, R. E. (2003) Model Comparisons: Docking ORGAHEAD and SimVision, presented at Proceedings of NAACSOS conference, Pittsburgh, PA, 2003Google Scholar
Index Terms
- Self-organizing social and spatial networks under what-if scenarios
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