This paper presents a design of a non-player character (AI) for promoting
balancedness in use of body segments when engaging in full-body motion gaming.
In our experiment, we settle a battle between the proposed AI and a player by
using FightingICE, a fighting game platform for AI development. A middleware
called UKI is used to allow the player to control the game by using body motion
instead of the keyboard and mouse. During gameplay, the proposed AI analyze
health states of the player; it determines its next action by predicting how
each candidate action, recommended by a Monte-Carlo tree search algorithm, will
induce the player to move, and how the player's health tends to be affected.
Our result demonstrates successful improvement in balancedness in use of body
segments on 4 out of 5 subjects.

本文提出了一种非玩家角色（AI）设计，用于促进全身运动游戏中身体各部分平衡性的使用。在使用 FightingICE 平台进行 AI 开发的战斗中，通过使用名为 UKI 的中间件，允许玩家使用身体动作而非键盘和鼠标来控制游戏。在游戏过程中，通过预测每个候选行动如何引起玩家移动以及玩家的生命状态如何受到影响，建议的 Monte-Carlo 树搜索算法确定下一步行动。结果表明，在 4 名受试者中成功提高了身体各段的平衡性。

运动适应性游戏 AI 促进健康

Adaptive Motion Gaming AI for Health Promotion

This paper introduces a principled approach for the design of a scalable
general reinforcement learning agent. Our approach is based on a direct
approximation of AIXI, a Bayesian optimality notion for general reinforcement
learning agents. Previously, it has been unclear whether the theory of AIXI
could motivate the design of practical algorithms. We answer this hitherto open
question in the affirmative, by providing the first computationally feasible
approximation to the AIXI agent. To develop our approximation, we introduce a
new Monte-Carlo Tree Search algorithm along with an agent-specific extension to
the Context Tree Weighting algorithm. Empirically, we present a set of
encouraging results on a variety of stochastic and partially observable
domains. We conclude by proposing a number of directions for future research.

本文介绍了一种基于贝叶斯优化概念的通用强化学习智能体的可扩展设计方法，提出了一种可行的 AIXI 智能体近似算法，并在随机和部分可观测领域上展示了一系列鼓舞人心的结果，最后提出了未来研究的方向。