%0 Conference Proceedings
%T Adaptive Automated Storytelling Based on Audience Response
%+ Pontifícia Universidade Católica do Rio de Janeiro [Brasil] = Pontifical Catholic University of Rio de Janeiro [Brazil] = Université catholique pontificale de Rio de Janeiro [Brésil] (PUC-Rio)
%A Baffa, Augusto
%A Poggi, Marcus
%A Feijó, Bruno
%Z Part 1: Full papers
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 14th International Conference on Entertainment Computing (ICEC)
%C Trondheim, Norway
%Y Konstantinos Chorianopoulos
%Y Monica Divitini
%Y Jannicke Baalsrud Hauge
%Y Letizia Jaccheri
%Y Rainer Malaka
%I Springer International Publishing
%3 Entertainment Computing - ICEC 2015
%V LNCS-9353
%P 45-58
%8 2015-09-29
%D 2015
%R 10.1007/978-3-319-24589-8_4
%K Social Interaction
%K Group decision making
%K Model of Emotions
%K Automated Storytelling
%K Audience model
%K Optimization application
%Z Computer Science [cs]Conference papers
%X To tell a story, the storyteller uses all his/her skills to entertain an audience. This task not only relies on the act of telling a story, but also on 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 tailored stories to an audience based on personality traits and preferences of each individual. As an audience may be composed of individuals with similar or mixed preferences, it is necessary to consider a middle ground solution based on the individual options. In addition, individuals may have some kind of relationship with others that influence their decisions. The proposed model addresses all steps in the quest to please the audience. It infers what the preferences are, computes the scenes reward for all individuals, estimates their choices independently and in group, and allows Interactive Storytelling systems to find the story that maximizes the expected audience reward.
%G English
%Z TC 14
%2 https://inria.hal.science/hal-01758442/document
%2 https://inria.hal.science/hal-01758442/file/371182_1_En_4_Chapter.pdf
%L hal-01758442
%U https://inria.hal.science/hal-01758442
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-ICEC
%~ IFIP-TC14
%~ IFIP-LNCS-9353