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Desirable Behaviors for Companion Bots in First-Person Shooters

机译:第一人称射击者的伴侣班次的可取行为

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First-Person Shooter games are a popular genre that often includes a team deathmatch mode of play: teams of agents score by killing members of the other team. When played without other humans, this mode features both opponent bots and companion bots. This paper uses a human subject study with 30 participants to analyze player preference for cooperative teammates vs. skilled, but less cooperative, teammates in the game Unreal Tournament 2004. Specifically, participants play with both a skilled bot based on neuroevolution and a less skilled bot hand-coded to be more cooperative. Survey results indicate that users perceived significant differences between the bots in several categories, e.g. following behavior and skill at scoring, but did not have a significant preference for one bot over another. However, participants did significantly prefer whichever bot they personally felt was more "helpful" and also preferred whichever bot they happened to see more of. These data indicate how user perception can strongly depend on coincidental interactions, such as being seen more. They also identify some qualities that humans desire in teammates, and indicate that simply scoring more will not necessarily result in a higher user preference.
机译:第一人称射击游戏是一种流行的流行,通常包括团队死亡竞赛模式:代理团队通过杀死另一个团队的成员。在没有其他人的情况下播放时,这种模式具有对手机器人和伴侣机器人。本文采用30名参与者的人类学科研究,分析了合作队友与熟练,但少合作,队友在比赛中的游戏不真实的2004年。具体而言,参与者与基于神经系统和熟练的机器人的熟练BOT一起玩手工编码更合作。调查结果表明,用户在几个类别中感知机器人之间的显着差异,例如,在得分的行为和技能之后,但对另一个机器人没有显着偏好。然而,与会者明确偏好他们亲自感受到的机器人更为“乐于助人”,而且还优先于他们碰巧的机器人看到更多。这些数据表明用户感知如何能够强烈地依赖于巧合的相互作用,例如被看到的更多。他们还确定了人类在队友中渴望的一些品质,并表明简单得分更多不会导致更高的用户偏好。

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