Automatic Strategies for Autonomous Virtual Characters

• Autorzy: Lach E.
• Języki publikacji: EN

Abstrakty

EN

Technique presented in the paper concerns automatic generation of strategies controlling virtual characters’ behaviour. Virtual people are currently widely used in many applications, especially in computer games, films and educational systems. A lot of researches focus on creating intelligent characters capable of deciding about their actions. The fully acceptable solution has not yet been found. The paper presents the problem of generating strategies by means of modified genetic programming. A new Guide-Path technique is introduced.

Słowa kluczowe

EN

genetic programming; task control; machine learning; virtual characters animation

• Wydawca: Wydawnictwo Uniwersytetu Przyrodniczo-Humanistycznego w Siedlcach
• Czasopismo: Studia Informatica : systems and information technology
• Rocznik: 2007
• Tom: Vol. 2(9)
• Strony: 57--68
• Opis fizyczny: Bibliogr. 17 poz., rys., wykr.

Twórcy

autor

Lach E.

• Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland

Bibliografia

• 1. Aaron E., Goldenstein S., Goswami A., Guibas L., Karavelas M., Metaxas. D., (2001). Scalable nonlinear dynamical systems for agent steering and crowd simulation. In: Computer and Graphics, 25(6), 983-998.
• 2. Bajurnow A., (2004). Layered learning for evolving goal scoring behavior in soccer players, Proc. of Congress on Evolutionary Computation, 1828-1835.
• 3. Blumberg B., Berlin M., Downie M., Ivanov Y., Johnson M. P., Tomlinson. B., (2002). Integrated learning for interactive synthetic characters, Conference on Computer Graphics and Interactive Techniques, 417-426.
• 4. Duthen Y., Luga H., Panatier C., Sanza C., (1999). Adaptive behavior for cooperation: a virtual reality application, Proc. of International Workshop on Robot and Human Interaction, 76-81.
• 5. Gustafson S.M., Hsu W.H., (2002). Genetic programming and multi-agent layered learning by reinforcements, Proc. of Genetic and Evolutionary Computation - GECCO, 764-771.
• 6. Jones R.M., Laird J.E., Nielsen P.E., (1998). Real-time intelligent characters for a nonvisual simulation environment. Proc. of Computer Animation Conference.
• 7. Kallmann M., Sevin de E., Thalmann D., (2001). Towards real time virtual human life simulations. In: Computer Graphics International, 31-37.
• 8. Lach E., (2005). Automatic generation of animation of virtual humans, The VII International Conference on Artificial Intelligence, 109-115.
• 9. Lach E., (2006). Wybrane techniki wspomagające animację autonomicznych postaci wirtualnych, PhD. Thesis, Gliwice, Poland, Silesian University of Technology.
• 10. Mittmann M., Remagnino P., Szarowicz A., Francik J., (2003). Automatic acquisition of actions for animated agents, Proc. of GAME-ON Conference on Simulation and AI in Computer Games.
• 11. Reynolds C.W., (1987). Flocks, herds, and schools: A distributed behavioral model. In: Computer Graphics, 25-34.
• 12. Sprinkhuizen-Kuyper I., Spronck P., Ponsen M., Postma E., (2006). Adaptive game AI with dynamic scripting. In: Machine Learning, 63(3), 217-248.
• 13. Tang W., Wan T.R., (2002). Intelligent self -learning characters for computer games, Proc. of Eurographics UK Conference, 51-58.
• 14. Thalmann D., Ulicny B., (2002). Crowd simulation for interactive virtual environments and VR training systems, Proc. of Eurographic workshop on Computer animation and simulation, 163-170.
• 15. Tu X., Terzopoulos D., (1994). Artificial fishes: Physics, locomotion, perception, behavior. Proc. of SIGGRAPH, 28, 43-50.
• 16. Whiteson S., Kohl N., Miikkulainen R., Stone P., (2005). Evolving soccer keep away players through task decomposition. In: Machine Learning, 5-30.
• 17. Koza J.R., (1992). Genetic Programming: On the Programming of Computers by Means of Natural Selection, MIT Press.