“让我们节约资源!”:用于多人环境意识游戏的动态协作AI

P. Sequeira, Francisco S. Melo, Ana Paiva
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引用次数: 6

摘要

在本文中,我们提出了一个协作人工智能(AI)模块,用于回合制,多人,环境意识游戏。这款游戏是EnerCities严肃游戏的一个版本,在欧盟项目的背景下进行了修改,以支持机器人导师以社交和教学方式与两名人类玩家互动的连续游戏。为此,我们创建了一个AI模块,能够通知机器人导师的游戏玩法和教学决策。具体来说,该模块包括一个行动计划器,能够与游戏模拟器一起,根据玩家偏好和当前游戏值执行前瞻性规划。这样的预测值也可以作为一个警报系统,告知其他玩家当前行为的临近后果,并在游戏中提供可选择的、可持续的行动方案。该模块还集成了一个社交组件,可以持续模拟每个玩家的游戏偏好,并自动调整导师的策略,以遵循团队的“行动趋势”。因此,提议的AI模块用于告知游戏状态的重要方面以及人类玩家的行动。在本文中,我们概述了这种协作版本游戏的属性和复杂性,并详细介绍了AI模块及其组件。我们还报告了在几个实验研究中使用所提出的模块来控制机器人导师的行为的成功,包括与玩协作能源城市的儿童的互动。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
“Let's save resources!”: A dynamic, collaborative AI for a multiplayer environmental awareness game
In this paper we present a collaborative artificial intelligence (AI) module for a turn-based, multiplayer, environmental awareness game. The game is a version of the EnerCities serious game, modified in the context of a European-Union project to support sequential plays of an emphatic robotic tutor interacting with two human players in a social and pedagogical manner. For that purpose, we created an AI module capable of informing the game-playing and pedagogical decision-making of the robotic tutor. Specifically, the module includes an action planner capable of, together with a game simulator, perform forward-planning according to player preferences and current game values. Such predicted values are also used as an alert system to inform the other players of near consequences of current behaviors and advise alternative, sustainable courses of action in the game. The module also incorporates a social component that continuously models the game preferences of each player and automatically adjusts the tutor's strategy so to follow the group's “action tendency”. The proposed AI module is therefore used to inform about important aspects of the game state and also the human players actions. In this paper we overview the properties and complexity of this collaborative version of the game and detail the AI module and its components. We also report on the successes of using the proposed module for controlling the behavior of a robotic tutor in several experimental studies, including the interaction with children playing collaborative EnerCities.
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