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http://dx.doi.org/10.9717/kmms.2022.25.2.432

Game AI Agents using Deliberative Behavior Tree based on Utility Theory  

Kwon, Minji (Game Animation Engineering Major, Dong-eui University)
Seo, Jinsek (Game Engineering Major, Dong-eui University)
Publication Information
Journal of Korea Multimedia Society / v.25, no.2, 2022 , pp. 432-439 More about this Journal
Abstract
This paper introduces deliberative behavior tree using utility theory. The proposed approach combine the strengths of behavior trees and utility theory to implement complex behavior of AI agents in an easier and more concise way. To achieve this goal, we devised and implemented three types of additional behavior tree nodes, which evaluate utility values of its own node or its subtree while traversing and selecting its child nodes based on the evaluated values. In order to validate our approach, we implemented a sample scenario using conventional behavior tree and our proposed deliberative tree respectively. And then we compared and analyzed the simulation results.
Keywords
Behavior Tree; Utility System; Artificial Intelligence;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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