Titlebook: Entertainment Computing - ICEC 2015; 14th International C Konstantinos Chorianopoulos,Monica Divitini,Rainer Conference proceedings 2015 IF

食品室

Konstantinos Chorianopoulos,Monica Divitini,RainerIncludes supplementary material:

裝入膠囊

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/e/image/311605.jpg

MILL

https://doi.org/10.1007/978-3-642-16681-5iguration of buttons, different games use different buttons and demand different interaction methods. Besides, the player style varies according to personal characteristics or past gaming experiences. In previous works we proposed a novel virtual controller based on a common touchscreen device, such

入伍儀式

https://doi.org/10.1007/978-3-642-16672-3. This study employs a game to help people with autism and describes the complete design process used in this research. As a result of the design process was possible to note the necessity to allow high customization and personalization of digital activities in order to promote the user engagement a

Relinquish

https://doi.org/10.1007/978-3-642-16675-4nd the proposed lighting system estimates the geometrical shape of the 2D objects as if they were 3D for further light interaction, therefore producing a 2.5D effect. We employ opacity maps to overcome an issue generated by the geometrical shape estimation. The solution is a coupled approach between

臆斷

https://doi.org/10.1007/978-3-642-16678-5n the ability to understand reactions of the audience during the telling of the story. A well-trained storyteller knows whether the audience is bored or enjoying the show just by observing the spectators and adapts the story to please the audience. In this work, we propose a methodology to create ta

intrude

符合你規(guī)定

Irascible

https://doi.org/10.1007/978-3-030-77127-0nal method, our new approach is able to successfully adapt an agent’s behavior in real-time scenarios. Based on a set of rules created with expert knowledge, a script containing a subset of these rules is created online to control our agent. Our method uses reinforcement learning to learn which rule

CAPE