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- W2502577057 abstract "Controlling Graphical avatars intelligently in real-time applications such as 3D computer simulating environment has become important as the storage and computational power of computers has increased. Such avatars are usually controlled by Finite State Machines (FSM), in which each individual state represents the status of the avatars. The FSMs are usually manually designed, and the number of states and transitions are therefore limited. A more complex approach is needed for the avatar’s actions, which are automatically generated to adapt to different situation. The levels of the missions and algorithms for the control are the essential elements to achieve the requirements, respectively. Reinforcement Learning can be used to control the avatar intelligently in the 3D environment. When simulating the interactions between avatars and changeable environments, the problem becomes more difficult than working in a certain unchanged situation. Specific Framework and methods should be created for controlling the behaviors of avatars, such as using hierarchical structure to describe these actions. The approach has many problems to solve such as where the levels of the missions will be defined and how the learning algorithm will be used to control the avatars, et cetera. In this chapter, these problems will be discussed." @default.
- W2502577057 created "2016-08-23" @default.
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- W2502577057 date "2012-01-01" @default.
- W2502577057 modified "2023-09-22" @default.
- W2502577057 title "Computer-Controlled Graphical Avatars and Reinforcement Learning" @default.
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- W2502577057 doi "https://doi.org/10.4018/978-1-4666-1806-0.ch018" @default.
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