Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/80763
Title: Trace it like you believe it : time-continuous believability prediction
Authors: Pacheco, Cristiana
Melhart, David
Liapis, Antonios
Yannakakis, Georgios N.
Perez-Liebana, Diego
Keywords: Games -- Design
Truthfulness and falsehood
Video games -- Design
Consumers' preferences
Issue Date: 2021
Publisher: IEEE
Citation: Pacheco, C., Melhart, D., Liapis, A., Yannakakis, G. N., & Perez-Liebana, D. (2021). Trace it like you believe it : time-continuous believability prediction. Trace it like you believe it : time-continuous believability prediction
Abstract: Assessing the believability of agents, characters and simulated actors is a core challenge for human computer interaction. While numerous approaches are suggested in the literature, they are all limited to discrete and low-granularity representations of believable behavior. In this paper we view believability, for the first time, as a time-continuous phenomenon and we explore the suitability of two different affect annotation schemes for its assessment. In particular, we study the degree to which we can predict character believability in a continuous fashion through a two-player game study. The game features various opponent behaviors that are assessed for their believability by 89 participants that played the game and then annotated their recorded playthrough. Random forest models are then trained to predict believability based on ad-hoc designed in-game features. Results suggest that a discrete annotation method leads to a more robust assessment of the ground truth and subsequently better modelling performance. Our best models are able to predict a change in perceived believability with a 72.5% accuracy on average (up to 90% in the best cases) in a time-continuous manner.
Description: This research is supported by the IEEE CIS Graduate Student Research Grants and the EP/L015846/1 for the Centre for Doctoral Training in Intelligent Games and Game Intelligence(IGGI) from the UK Engineering and Physical Sciences Research Council (EPSRC).
URI: https://www.um.edu.mt/library/oar/handle/123456789/80763
Appears in Collections:Scholarly Works - InsDG

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