Abstract
This paper addresses the issue of hybridization between reactive and cognitive approaches within a single decision-making architecture for virtual agent in an urban simulation. We use a reactive module in order to manage reactive behaviors and agent autonomy, and a cognitive module for anticipation, learning and complex behaviors management. The purpose of the cognitive module is to increase the agent’s behavior credibility. The agent’s reactive and proactive behaviors are sent to a decision module which is able to integrate, decompose, combine and select an action.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Ceranowicz, A., Torpey, M.: Adapting to urban warfare. The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology 2, 3–15 (2005)
Jepson, W., Liggett, R., Friedman, S.: An environment for real-time urban simulation. In: Proceedings of the 1995 Symposium on Interactive 3D Graphics (SI3D 1995), p. 165. ACM Press, New York (1995)
Waddell, P., Ulfarsson, G.: Introduction to urban simulation: design and development of operational models. In: Handbook in Transport, vol. 5, pp. 203–236 (2004)
Werner, T., Helbing, D.: The social Force Pedestrian Model Applied to Real Life Scenarios, pp. 17–26 (2003)
Shao, W., Terzopoulos, D.: Autonomous pedestrians. In: Proceedings of the 2005 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, SCA 2005, p. 19. ACM Press, New York (2005)
Ronald, N., Sterling, L., Kirley, M.: An agent-based approach to modelling pedestrian behaviour. International Journal of Simulation (2007)
Bloomberg, L., Dale, J.: Comparison of VISSIM and CORSIM Traffic Simulation Models on a Congested Network. Transportation Research Record Journal 98104, 52–60 (2000)
Treuille, A., Cooper, S., Popović, Z.: Continuum crowds. ACM Transactions on Graphics 25, 1160 (2006)
Maïm, J., Yersin, B., Thalmann, D.: Real-time crowds. In: SIGGRAPH Asia 2008, pp. 1–16. ACM Press, New York (2008)
Ochs, M., Sabouret, N., Corruble, V.: Simulation of the Dynamics of Nonplayer Characters’ Emotions and Social Relations in Games. IEEE Transactions on Computational Intelligence and AI in Games 1, 281–297 (2009)
Coleridge, S.: Biographia Literaria: Or, Biographical Sketches of My Literary Life and Opinions (1872)
Bates, J.: The role of emotion in believable agents. Communications of the ACM 37, 122–125 (1994)
Sengers, P.: Designing comprehensible agents. In: Proceedings of the 16th International Joint Conference on Artificial Intelligence, vol. 2, pp. 1227–1232. Morgan Kaufmann Publishers Inc., San Francisco (1999)
Russell, S.J., Norvig, P.: Artificial intelligence: a modern approach. Prentice hall (2010)
Bryson, J.: Hierarchy and sequence vs. full parallelism in action selection. In: From animals to animats (2000)
Langley, P., Laird, J.E., Rogers, S.: Cognitive architectures: Research issues and challenges. Cognitive Systems Research 10, 141–160 (2009)
Duch, W., Oentaryo, R.J., Pasquier, M.: Cognitive Architectures: Where do we go from here? In: Proceeding of the 2008 Conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference, pp. 122–136. IOS Press (2008)
Orkin, J.: Three states and a plan: the AI of FEAR. In: Game Developers Conference (2006)
Isla, D.: Handling complexity in the Halo 2 AI. In: Game Developers Conference (2005)
Meyer, J.A.: Artificial Life and the Animat Approach to Artificial Intelligence. In: Boden (ed.) Artificial Intelligence, pp. 325–354 (1996)
Guillot, A.: The animat contribution to cognitive systems research. Cognitive Systems Research 2, 157–165 (2001)
Brooks, R.A.: A Robust Layered Control System For a Mobile Robot. Massachusetts Institute of Technology, Cambridge (1985)
Maes, P.: Situated agents can have goals. Robotics and Autonomous Systems 6, 49–70 (1990)
Rosenblatt, J.: DAMN: A Distributed Architecture For Mobile Navigation - Thesis Summary. Journal of Experimental and Theoretical Artificial Intelligence, 339–360 (1995)
Rosenblatt, J.K., Payton, D.W.: A fine-grained alternative to the subsumption architecture for mobile robot control. In: International Joint Conference on Neural Networks, vol. 2, pp. 317–323. IEEE (1989)
Tyrrell, T.: The use of hierarchies for action selection. Adaptive Behavior 1, 387 (1993)
Laird, J.E., Newell, A., Rosenbloom, P.S.: SOAR: an architecture for general intelligence. Artif. Intell. 33, 1–64 (1987)
Laird, J.E.: Extending the Soar Cognitive Architecture. In: Proceeding of the 2008 Conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference, pp. 224–235. IOS Press, Amsterdam (2008)
Anderson, J.R., Bothell, D., Byrne, M.D., Douglass, S., Lebiere, C., Qin, Y.: An integrated theory of the mind. Psychological Review 111, 1036–1060 (2004)
Rao, A.S., Georgeff, M.P.: BDI Agents: From Theory to Practice. In: Proceedings of the First International Conference on Multi-Agent Systems (ICMAS 1995), pp. 312–319 (1995)
Georgeff, M., Pell, B., Pollack, M., Tambe, M., Wooldridge, M.: The belief-desire-intention model of agency. Intelligent Agents V: Agents Theories, Architectures, and Languages, 1–10 (1999)
Müller, J.P., Pischel, M.: The Agent Architecture InteRRaP: Concept and Application (1993)
Langley, P., Choi, D.: A unified cognitive architecture for physical agents. In: Proceedings of the 21st National Conference on Artificial Intelligence, vol. 2, pp. 1469–1474. AAAI Press (2006)
Newell, A., Simon, H.A., rand corp Santa Monica Calif.: GPS, a program that simulates human thought. Defense Technical Information Center (1961)
Schmidt, B.: Human factors in complex systems : The modelling of human behaviour. In: Simulation in Wider Europe, 19th European Conferance on Modelling and Simulation, pp. 5–14 (2005)
Robert, G., Guillot, A.: MHiCS, a modular and hierarchical classifier systems architecture for bots. In: 4th International Conference on Intelligent Games and Simulation (GAME-ON 2003), pp. 140–144 (2003)
de Sevin, E.: An action selection architecture for autonomous virtual humans in persistent worlds (2006)
Dujardin, T., Routier, J.-C.: Behavior Design of Game Character’s Agent. In: 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, pp. 423–430. IEEE (2010)
Robert, R.: Anticipatory Systems (1985)
Meyer, C., Ganascia, J.G., Zucker, J.D.: Learning strategies in games by anticipation. In: International Joint Conference on Artificial Intelligence, pp. 698–707. Lawrence Erlbaum Associates Ltd. (1997)
Butz, M., Sigaud, O., Gérard, P.: Anticipatory Behavior in Adaptive Learning Systems. Springer, Heidelberg (2003)
Burke, R.: Great expectations: Prediction in entertainment applications. Life-Like Characters Tools, Affective Functions, and Applications (2004)
Erol, K., Hendler, J., Nau, D.S.: UMCP: A sound and complete procedure for hierarchical task-network planning. In: Proc. 2nd Intl. Conf. on AI Planning Systems, pp. 249–254 (1994)
Navarro, L., Flacher, F., Corruble, V.: Dynamic level of detail for large scale agent-based urban simulations. In: The 10th International Conference on Autonomous Agents and Multiagent Systems, vol. 2, pp. 701–708. International Foundation for Autonomous Agents and Multiagent Systems (2011)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Reynaud, Q., de Sevin, E., Donnart, JY., Corruble, V. (2013). A Cognitive Module in a Decision-Making Architecture for Agents in Urban Simulations. In: Dignum, F., Brom, C., Hindriks, K., Beer, M., Richards, D. (eds) Cognitive Agents for Virtual Environments. CAVE 2012. Lecture Notes in Computer Science(), vol 7764. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36444-0_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-36444-0_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36443-3
Online ISBN: 978-3-642-36444-0
eBook Packages: Computer ScienceComputer Science (R0)