OS17-1 Customization of Contents for Acquisition of Skills of FPS without Trainer

Title:	OS17-1 Customization of Contents for Acquisition of Skills of FPS without Trainer
Publication:	ICAROB2020
Volume:	25
Pages:	767-769
ISSN:	2188-7829
DOI:	10.5954/ICAROB.2020.OS17-1
Author(s):	Masao Kubo, Takeshi Ueno, Hiroshi Sato
Publication Date:	January 13, 2020
Keywords:	Reinforcement Learning, E-Learning, Game AI, Virtual Reality
Abstract:	In this paper, we conducted a study to develop self-study materials with the ability to provide more suitable materials for students' skills. A first person shooter (FPS) type simulator-based teaching material is created to acquire knowledge. Conventional games resume from a predetermined scene regardless of trainee's skill contributes to no increases in knowledge and skill. In this paper, we propose a re-spawning point suitable for each trainee by a recommendation algorithm which tries to find good game scenes by trial and error.
PDF File:	https://alife-robotics.co.jp/members2020/icarob/data/html/data/OS/OS17/OS17-1.pdf
Copyright:	© The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/