Creating Affective Autonomous Characters Using Planning in Partially Observable Stochastic Domains

The ability to reason about and respond to their own emotional states can enhance the believability of Non-Player Characters (NPCs). In this paper, we use a Partially Observable Markov Decision Process (POMDP)-based framework to model emotion over time. A two-level appraisal model, involving quick a...

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Bibliographic Details
Published in:IEEE transactions on computational intelligence and AI in games. Vol. 9; no. 1; pp. 42 - 62
Main Authors: Huang, Xiangyang, Zhang, Shudong, Shang, Yuanyuan, Zhang, Weigong, Liu, Jie
Format: Journal Article
Language:English
Published: IEEE 01.03.2017
Subjects:
Affective computing
Appraisal
Computational modeling
emotion dynamics
Games
Markov processes
Planning
POMDP
primary and secondary emotions
probabilistic computation tree logic (PCTL)
Psychology
Uncertainty
ISSN:1943-068X, 1943-0698
Online Access:Get full text
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Summary:The ability to reason about and respond to their own emotional states can enhance the believability of Non-Player Characters (NPCs). In this paper, we use a Partially Observable Markov Decision Process (POMDP)-based framework to model emotion over time. A two-level appraisal model, involving quick and reactive vs. slow and deliberate appraisals, is proposed for the creation of affective autonomous characters based on POMDPs, wherein the probability of goal satisfaction is used in an appraisal and reappraisal process for emotion generation. We not only extend Probabilistic Computation Tree Logic (PCTL) for reasoning about the properties of emotional states based on POMDPs but also illustrate how four reactive (primary) emotions and nine deliberate (secondary) emotions can be derived by combining PCTL with the belief-desire theory of emotion. The results of an empirical study suggest that the proposed model can be used to create characters that appear to be more believable and more intelligent.
ISSN:1943-068X
1943-0698
DOI:10.1109/TCIAIG.2015.2494599