Spotting the Difference: Identifying Player Opponent Preferences in FPS Games

Conroy, David

doi:10.1007/978-3-642-33542-6_10

Cited by 2 publications

(1 citation statement)

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“…Conroy et al [23] carried out a study to analyse human players responses to computer-controlled opponents in FPS games. The study examined how well the players can distinguish between other humans and NPCs while also seeking to identify some of the characteristics that lead to an opponent being labelled as artificially controlled.…”

Section: Related Researchmentioning

confidence: 99%

Adaptive Shooting for Bots in First Person Shooter Games Using Reinforcement Learning

Glavin

Madden

2015

IEEE Trans. Comput. Intell. AI Games

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In current state-of-the-art commercial first person shooter games, computer controlled bots, also known as nonplayer characters, can often be easily distinguishable from those controlled by humans. Tell-tale signs such as failed navigation, "sixth sense" knowledge of human players' whereabouts and deterministic, scripted behaviours are some of the causes of this. We propose, however, that one of the biggest indicators of non-humanlike behaviour in these games can be found in the weapon shooting capability of the bot. Consistently perfect accuracy and "locking on" to opponents in their visual field from any distance are indicative capabilities of bots that are not found in human players. Traditionally, the bot is handicapped in some way with either a timed reaction delay or a random perturbation to its aim, which doesn't adapt or improve its technique over time. We hypothesize that enabling the bot to learn the skill of shooting through trial and error, in the same way a human player learns, will lead to greater variation in game-play and produce less predictable non-player characters. This paper describes a reinforcement learning shooting mechanism for adapting shooting over time based on a dynamic reward signal from the amount of damage caused to opponents.

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Section: Related Researchmentioning

confidence: 99%