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@INPROCEEDINGS{Streck:164511,
author = {Streck, Adam and Wolbers, Thomas},
title = {{U}sing {D}iscrete {T}ime {M}arkov {C}hains for {C}ontrol
of {I}dle {C}haracter {A}nimation},
publisher = {IEEE},
reportid = {DZNE-2022-01063},
pages = {1-4},
year = {2018},
abstract = {The behavior of autonomous characters in virtual
environments is usually described via a complex
deterministic state machine or a behavior tree driven by the
current state of the system. This is very useful when a high
level of control over a character is required, but it
arguably does have a negative effect on the illusion of
realism in the decision making process of the character.
This is particularly prominent in cases where the character
only exhibits idle behavior, e.g. a student sitting in a
classroom. In this article we propose the use of discrete
time Markov chains as the model for defining realistic
non-interactive behavior and describe how to compute
decision probabilities to normalize by the length of
individual actions. Lastly, we argue that those allow for
more precise calibration and adjustment for the idle
behavior model then the models being currently employed in
practice.},
month = {Aug},
date = {2018-08-14},
organization = {IEEE Conference on Computational
Intelligence and Games (CIG),
Maastricht (Netherlands), 14 Aug 2018 -
17 Aug 2018},
cin = {AG Wolbers},
cid = {I:(DE-2719)1310002},
pnm = {344 - Clinical and Health Care Research (POF3-344)},
pid = {G:(DE-HGF)POF3-344},
typ = {PUB:(DE-HGF)8},
doi = {10.1109/CIG.2018.8490450},
url = {https://pub.dzne.de/record/164511},
}