Abstract
In this paper, we propose virtual agent vision perception techniques to approximate realistic vision while maintaining low execution time. We discuss virtual agent vision in large scale multi-agent based simulations where agents are situated in open environments (i.e., inaccessible, non-deterministic, dynamic, continuous). When dealing with open environments, the efficiency of agent vision algorithms is of great importance since every agent’s perception must be calculated in simulated real-time. We discuss optimizations for vision algorithms in DIVAs, a large scale multi-agent based simulation framework.



























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Araujo, F., Al-Zinati, M., Valente, J., Kuiper, D., & Zalila-Wenkstern, R. (2013). Divas 4.0: A framework for the development of situated multi-agent based simulation systems. In Proceedings of the 12th international conference on autonomous agents and multiagent systems (AAMAS), Demo Paper, Best Demo Award (pp. 1351–1352). Saint Paul, MN: IFAAMAS.
Atchison, D., Smith, G., & Smith, G. (2000). Optics of the human eye. Oxford: Butterworth-Heinemann.
Bandini, S., Federici, M., Manzoni, S., & Vizzari, G. (2007). Pedestrian and crowd dynamics simulation: Testing sca on paradigmatic cases of emerging coordination in negative interaction conditions. Parallel Computing Technologies, 4671, 360–369.
Banerjee, B., Abukmail, A., & Kraemer, L. (2008). Advancing the layered approach to agent-based crowd simulation. Proceedings of IEEE workshop on parallel and distributed simulation (pp. 185–192). Rome.
Behrens, T. M., Hindriks, K. V., & Dix, J. (2011). Towards an environment interface standard for agent platforms. Annals of Mathematics and Artificial Intelligence, 61(4), 261–295.
Braun, A., Bodmann, B., & Musse, S. (2005). Simulating virtual crowds in emergency situations. In Proceedings of the ACM symposium on virtual reality software and technology (pp. 244–252). ACM.
Chen, S. (2011). Kalman filter for robot vision: A survey. IEEE Transactions on Industrial Electronics, 59(11), 4409. doi:10.1109/TIE.2011.2162714.
Dorst, L., Fontijne, D., & Mann, S. (2009). Geometric algebra for computer science (revised edition): An object-oriented approach to geometry. Burlington, MA: Morgan Kaufmann.
Gregory, J., Lander, J., & Whiting, M. (2009). Game engine architecture. Wellesley, MA: AK Peters.
Jander, K., Braubach, L., & Pokahr, A. (2010). Envsupport: A framework for developing virtual environments. In Seventh international workshop from agent theory to agent implementation (AT2AI-7).
Kaup, D., Clarke, T., Oleson, R., Malone, L., & Jentsch, F. (2008). Introducing age-based parameters into simulations of crowd dymanics. In Simulation conference, 2008. WSC 2008. winter (pp. 895–902). IEEE.
Koh, W. L., & Zhou, S. (2007). An extensible collision avoidance model for realistic self-driven autonomous agents. Proceedings of the 11th IEEE international symposium on distributed simulation and real-time applications (pp. 7–14). Chania.
Kuiper, D. (2012). Agent perception in multi-agent based simulation systems. Ph.D. thesis, University of Texas at Dallas.
Kuiper, D., & Wenkstern, R. (2011). Virtual agent perception in large scale multi-agent based simulation systems. In The 10th IEEE/ACM international conference on autonomous agents and multiagent systems (Vol. 3, pp. 1235–1236). International Foundation for Autonomous Agents and Multiagent Systems (AAMAS 2011).
Kuipers, J. B. (1999). Quaternions and rotation sequences. Princeton: Princeton University Press.
Kutz, M., & Herpers, R. (2008). Urban traffic simulation for games: a general approach for simulation of urban actors. Proceedings of conference on future play: Research, play, share (pp. 181–184). Toronto.
Liu, E., & Theodoropoulos, G. (2010) A continuous matching algorithm for interest management in distributed virtual environments. 1–10. doi:10.1109/PADS.2010.5471665.
Mavs website. (2013). MAVS Lab. Retrieved June, 2013, from http://mavs.utdallas.edu/.
Metal gear solid. (2010). Konami Corporation. Retrieved October, 2010, from http://www.konami.com/.
Mili, R. Z., & Steiner, R. (2008). Modeling agent-environment interactions in adaptive mas. In D. Weyns, S. A. Brueckner, Y. Demazeau, (Eds.), Proceedings of engineering environments mediated multiagent systems (EEMAS’07), European conference on complex systems (2007). Also in Lecture Notes in AI 5049. (pp. 135–147). Verlag: Springer.
Mili, R.Z., Oladimeji, E., & Steiner, R. (2006). Architecture of the DIVAs simulation system. In Proceedings of agent-directed simulation symposium ADS06. Huntsville, AL.
Mili, R. Z., Steiner, R., & Oladimeji, E. (2006). DIVAs: Illustrating an abstract architecture for agent-environment simulation systems. Multiagent and Grid Systems, Special Issue on Agent-oriented Software Development Methodologies, 2(4), 505–525.
Möller, T., Haines, E., & Hoffman, N. (2008). Real-time rendering. Natick, MA: AK Peters Limited.
Munz, M., Ma andhlisch, M., & Dietmayer, K. (2010). Generic centralized multi sensor data fusion based on probabilistic sensor and environment models for driver assistance systems. Intelligent Transportation Systems Magazine, IEEE, 2(1), 6–17. doi:10.1109/MITS.2010.937293.
Pelechano, N., Allbeck, J., & Badler, N. (2007). Controlling individual agents in high-density crowd simulation. 2007 ACM SIGGRAPH/Eurographics symposium on computer animation (pp. 99–108). San Diego, CA.
Russell, S., & Norvig, P. (1995). Artificial intelligence: A modern approach. Upper Saddle River, NJ: Prentice Hall.
Rymill, S. J., & Dodgson, N. A. (2005). Psychologically-based vision and attention for the simulation of human behaviour. In Proceedings of computer graphics and interactive techniques (pp. 229–236). Dunedin.
Sharma, S. (2009). Simulation and modeling of group behavior during emergency evacuation. In Proceedings of the IEEE symposium on intelligent agents (pp. 122–127). Nashville, TN.
Sharma, S., Singh, H., & Prakash, A. (2008). Multi-agent modeling and simulation of human behavior in aircraft evacuations. IEEE Transactions on Intelligent Transportation Systems, 44(4), 1477–1488.
Shendarkar, A., Vasudevan, K., Lee, S., & Son, Y. J. (2006). Crowd simulation for emergency response using bdi agent based on virtual reality. In Proceedings of the winter simulation conference (pp. 545–553). Monterey, CA.
Shi, J., Ren, A., & Chen, C. (2009). Agent-based evacuation model of large public buildings under fire conditions. Automation in Construction, 18(3), 338–347.
Shirley, P., & Marschner, S. (2009). Fundamentals of computer graphics. Natick, MA: AK Peters Limited.
Splinter cell. (2010). Ubisoft. Retrieved October, 2010, from http://www.splintercell.com/.
Steel, T. (2010). A self-organizing environment for distributed multiagent-based simulation. Ph.D. thesis, University of Texas at Dallas.
Steel, T., Kuiper, D., & Wenkstern, R. (2010). Context-aware virtual agents in open environments. In Sixth international conference on autonomic and autonomous systems (ICAS), 2010 (pp. 90–96). IEEE.
Steel, T., Kuiper, D., & Wenkstern, R. (2010). Virtual agent perception in multi-agent based simulation systems. In Proceedings of IEEE/WIC/ACM international conference on intelligent agent technology (IAT-10) (pp. 453–456). Toronto: ACM.
Uno, K., & Kashiyama, K. (2008). Development of simulation system for the disaster evacuation based on multi-agent model using gis. Tsinghua Science and Technology, 13(1), 348–353.
Was, J. (2005). Cellular automata model of pedestrian dynamics for normal and evacuation conditions. In Proceedings of intelligent systems design and applications (ISDA05) (pp. 151–159). Wroclaw.
World of warcraft. (2012). Blizzard Entertainment Inc. Retrieved October, 2012, from http://www.worldofwarcraft.com.
Xiaofeng, M., Chaozhong, W., & Xinping, Y. (2008). A multi-agent model for evacuation system under large-scale events. In Proceedings of international symposium on computational intelligence and design (ISCID08) (pp. 557–560). Wuhan.
Acknowledgments
The DIVAs project is supported by Rockwell-Collins under Grant number 5-25143. We would like to acknowledge the contribution of the MAVs lab members for the development of the DIVAs framework. In particular we acknowledge the assistance of Travis Steel and Frederico Araujo in the implementation of the vision algorithms within DIVAs 3.0.
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Kuiper, D.M., Wenkstern, R.Z. Agent vision in multi-agent based simulation systems. Auton Agent Multi-Agent Syst 29, 161–191 (2015). https://doi.org/10.1007/s10458-014-9250-8
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DOI: https://doi.org/10.1007/s10458-014-9250-8