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Simulating socially intelligent agents in semantic virtual environments

Published online by Cambridge University Press:  01 December 2008

Francisco Grimaldo ,
Miguel Lozano ,
Fernando Barber  and
Guillermo Vigueras
Francisco Grimaldo
Affiliation:
Computer Science Department, University of Valencia, Dr. Moliner 50, 46100 Burjassot, Valencia, Spain; e-mail: [email protected], [email protected], [email protected], [email protected]
Miguel Lozano
Affiliation:
Computer Science Department, University of Valencia, Dr. Moliner 50, 46100 Burjassot, Valencia, Spain; e-mail: [email protected], [email protected], [email protected], [email protected]
Fernando Barber
Affiliation:
Computer Science Department, University of Valencia, Dr. Moliner 50, 46100 Burjassot, Valencia, Spain; e-mail: [email protected], [email protected], [email protected], [email protected]
Guillermo Vigueras
Affiliation:
Computer Science Department, University of Valencia, Dr. Moliner 50, 46100 Burjassot, Valencia, Spain; e-mail: [email protected], [email protected], [email protected], [email protected]
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Abstract

The simulation of synthetic humans inhabiting virtual environments is a current research topic with a great number of behavioral problems to be tackled. Semantical virtual environments (SVEs) have recently been proposed not only to ease world modeling but also to enhance the agent–object and agent–agent interaction. Thus, we propose the use of ontologies to define the world’s knowledge base and to introduce semantic levels of detail that help the sensorization of complex scenes—containing lots of interactive objects. The object taxonomy also helps to create general and reusable operativity for autonomous characters—for example, liquids can be poured from containers such as bottles. On the other hand, we use the ontology to define social relations among agents within an artificial society. These relations must be taken into account in order to display socially acceptable decisions. Therefore, we have implemented a market-based social model that reaches coordination and sociability by means of task exchanges. This paper presents a multi-agent framework oriented to simulate socially intelligent characters in SVEs. The framework has been successfully tested in three-dimensional (3D) dynamic scenarios while simulating a virtual university bar, where groups of waiters and customers interact with both the objects in the scene and the other virtual agents, finally displaying complex social behaviors.

Type
Article
Information
The Knowledge Engineering Review , Volume 23 , Issue 4 , December 2008 , pp. 369 - 388
Copyright
Copyright © Cambridge University Press2008

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References

Badawi, M. & Donikian, S. 2004. Autonomous agents interacting with their virtual environment through synoptic objects. In Proceedings of the 17th Conference on Computer Animation and Social Agents (CASA2004).Google Scholar
Badler, N., Bindiganavale, R., Bourne, J., Palmer, M., Shi, J. & Schuler, W. 2000. A Parametrized Action Representation for Virtual Human Agents. MIT Press, 256–284.Google Scholar
Badler, N., Philips, C. & Webber, B. 1993. Simulating Humans: Computer Graphics, Animation and Control. Oxford University Press.CrossRefGoogle Scholar
Bickmore, T. & Cassell, J. 2001. Relational agents: a model and implementation of building user trust. In Proceedings of the Conference on Human Factors in Computing Systems (CHI’2001). ACM.CrossRefGoogle Scholar
Bordini, R. H., da Rocha, A. C., Hübner, J. F., Moreira, A. F., Okuyama, F. Y. & Vieira, R. 2005. MAS-SOC: a social simulation platform based on agent-oriented programming. Journal of Artificial Societies and Social Simulation 8(3), 133–160.Google Scholar
Bordini, R. H. & Hübner, J. F. 2007. Jason, available at http://jason.sourceforge.net/.Google Scholar
Chang, P. H.-M., Chien, Y.-H., Kao, E. C.-C. & Soo, V.-W. 2005. A knowledge-based scenario framework to support intelligent planning characters. In Proceedings of the 5th International Workshop of Intelligent Virtual Agents (IVA05), Lecture Notes in Artificial Intelligence 3661, 134–145. Springer Verlag.Google Scholar
Charles, F., Lozano, M., Mead, S., Bisquerra, A. & Cavazza, M. 2003. Planning formalisms and authoring in interactive storytelling. In First International Conference on Technologies for Interactive Digital Storytelling and Entertainment, Fraunhofer IRB Verlag, 216–225. Springer Verlag.Google Scholar
Ciger, J. 2005. Collaboration with Agents in VR Environments. PhD thesis, École Polytechnique Fédérale de Lausanne.Google Scholar
Conte, R. 1999. Artificial social intelligence: a necessity for agent systems’ developments. The Knowledge Engineering Review 14, 109–118.CrossRefGoogle Scholar
Costa de Paiva, D., Vieira, R. & Musse, S. R. 2005. Ontology-based crowd simulation for normal life situations. In Proceedings of the Computer Graphics. IEEE.Google Scholar
Decker, K. S. & Lesser, V. R. 1997. Designing a family of coordination algorithms. Readings in Agents.Google Scholar
Doyle, P. 2002. Believability through context using “knowledge in the world” to create intelligent characters. In First International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS02). Academic Press, 342–349.Google Scholar
Farenc, N., Boulic, R. & Thalmann, D. 1999. An informed environment dedicated to the simulation of virtual humans in urban context. In Computer Graphics Forum (Eurographics ’99), 18(3), Brunet, P. & Scopigno R. (eds.). The Eurographics Association and Blackwell Publishers, 309–318.Google Scholar
Giampapa, J. A. & Sycara, K. 2002. Team-oriented Agent Coordination in the RETSINA Multi-agent System. Technical report cmu-ri-tr-02-34, Robotics Institute, Carnegie Mellon University.Google Scholar
Gil, Y. 2005. Description logics and planning. AI Magazine 26(2), 73–84.Google Scholar
Grimaldo, F., Barber, F. & Lozano, M. 2006. An ontology-based approach for IVE+VA. In Proceedings of the IVEVA’06: Intelligent Virtual Environments and Virtual Agents.Google Scholar
Grimaldo, F., Lozano, M., Barber, F. & Orduña, J. 2005. Integrating social skills in task-oriented 3D IVA. In Proceedings of the IVA’05: International Conference on Intelligent Virtual Agents, Lecture Notes in Artificial Intelligence. Springer Verlag.Google Scholar
Gutierrez, M. 2006. Semantic Virtual Environments. PhD thesis.Google Scholar
Harris, J. & Young, M. 2005. Proactive mediation in plan-based narrative environments, IVA’05.CrossRefGoogle Scholar
Hogg, L. M. & Jennings, N. 2001. Socially intelligent reasoning for autonomous agents. IEEE Transactions on Systems Man and Cybernetics 31(5), 381–393.CrossRefGoogle Scholar
Iglesias, A. & Luengo, F. 2004. Intelligent agents in virtual worlds. Third International Conference on CyberWorlds IEEE Computer Society, 62–69.Google Scholar
Kallmann, M. & Thalmann, D. 1999. Direct 3D interaction with smart objects. In VRST ’99: Proceedings of the ACM Symposium on Virtual Reality Software and Technology. Academic Press, 124–130.Google Scholar
Kao, E. C.-C., Chang, P. H-M., Chien, Y.-H. & Soo, V.-W. 2005. Using ontology to establish social context and support social reasoning. In Proceedings of the IVA’05: International Conference on Intelligent Virtual Agents.CrossRefGoogle Scholar
Kim, I.-C. 2003. KGBot: A BDI agent deploying within a complex 3D virtual environment. In Intelligent Virtual Agents, Lecture Notes in Computer Science, Rist, D. B. T., Aylett, R. & Rickel J. (eds.). Springer Verlag, 192–196.CrossRefGoogle Scholar
Levison, L. 1996. Connecting Planning and Acting via Object-specific Reasoning. PhD thesis, University of Pennsylvania.Google Scholar
Lozano, M., Grimaldo, F. & Barber, F. 2004. Integrating minimin-HSP agents in a dynamic simulation framework. In Third Helenic Conference on AI, SETN 2004, Lecture Notes in Computer Science 3025, 535–544. Springer Verlag.Google Scholar
Luck, M. & Aylett, R. 2000. Applying artificial intelligence to virtual reality: intelligent virtual environments. Applied Artificial Intelligence 14(1), 3–32.CrossRefGoogle Scholar
Meneguzzi, F., Zorzo, A. & Da Costa Móra, M. 2004. Propositional planning in BDI agents. In SAC’04: Proceedings of the 2004 ACM Symposium on Applied Computing. ACM Press, 58–63.Google Scholar
Molyneux, P. 2001. Black&white, Postmortem: Lionhead studio.Google Scholar
Otto, K. A. 2005. Towards semantic virtual environments. Workshop Towards Semantic Virtual Environments (SVE’05).CrossRefGoogle Scholar
Pellens, B., Bille, W., De Troyer, O. & Kleinermann, F. 2005. VR-wise: a conceptual modelling approach for virtual environments. Methods and Tools for Virtual Reality (MeTo-VR 2005) Workshop.Google Scholar
Piaget, J. 1995. Sociological Studies. Routledge.Google Scholar
Prada, R. & Paiva, A. 2005. Believable groups of synthetic characters. In AAMAS ’05: Proceedings of the 4th International Joint Conference on Autonomous Agents and Multiagent Systems. ACM Press, 37–43.Google Scholar
Rao, A. S. 1996. AgentSpeak(L): BDI agents speak out in a logical computable language. In Proceedings of the MAAMAW’96, No. 1038, Lecture Notes in Artificial Intelligence, 42–55. Springer Verlag.CrossRefGoogle Scholar
Rao, A. S. & Georgeff, M. P. 1991. Modeling rational agents within a BDI-architecture. In Proceedings of the 2nd International Conference on Principles of Knowledge Representation and Reasoning (KR’91). Morgan Kaufmann Publishers Inc., 473–484.Google Scholar
Raupp, S. & Thalmann, D. 2001. Hierarchical model for real time simulation of virtual human crowds. IEEE Transactions on Visualization and Computer Graphics 7(2), 152–164.Google Scholar
Ribeiro, M., da Rocha, A. C. & Bordini, R. H. 2003. A system of exchange values to support social interactions in artificial societies. In AAMAS’03: Autonomous Agents and Multi-agent Systems. ACM Press.Google Scholar
Schmitt, M. & Rist, T. 2003. Avatar arena: virtual group-dynamics in multicharacter negotiation scenarios. In Proceedings of the IVA’03: International Conference on Intelligent Virtual Agents, Lecture Notes in Artificial Intelligence. Springer Verlag.CrossRefGoogle Scholar
Soto, M. & Allongue, S. 2002. Modeling methods for reusable and interoperable virtual entities in multimedia virtual worlds. Multimedia Tools and Applications 16, 161–177.CrossRefGoogle Scholar
Stanford Medical Informatics 2006. Protege, available at http://protege.stanford.edu/.Google Scholar
Thalmann, D. & Monzani, J. 2002. Behavioural animation of virtual humans: what kind of law and rules? In Proceedings of Computer Animation. IEEE Computer Society Press, 154–163.Google Scholar
Thomas, G. & Donikian, S. 2000. Virtual humans animation in informed urban environments. Computer Animation.Google Scholar
Tomlison, B. & Blumberg, B. 2002. Social synthetic characters. In Proceedings of the Computer Graphics 26.Google Scholar
Tu, X. & Terzopoulos, D. 1994. Artificial fishes: physics, locomotion, perception, behavior, SIGGRAPH. In Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 1994. ACM, 43–50.Google Scholar
Viroli, M., Ricci, A. & Omicini, A. 2006. Operating instructions for intelligent agent coordination. The Knowledge Engineering Review 21, 49–69.CrossRefGoogle Scholar
Vosinakis, S. & Panayotopoulos, T. 2003. A task definition language for virtual agents. Journal of WSCG 11, 512–519.Google Scholar
W3C 2004. OWL Web ontology language guide, available at http://www.w3.org/TR/owl-guide/.Google Scholar