Creating Believable and Effective AI Agents for Games and Simulations

Umarov, Iskander; Mozgovoy, Maxim

doi:10.4018/978-1-4666-6252-0.ch003

Cited by 7 publications

(1 citation statement)

References 40 publications

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“…In computer generated behavior in general, human likeness or believability has been established as one of the most important metrics to facilitate engaging game play [52,24,2,28,53,34,19]. However, these approaches have focused on producing a general closeness to human behavior so far, not explicitly on representing behavior from specific individual players within the same game session.…”

Section: Related Workmentioning

confidence: 99%

Bot or not? User Perceptions of Player Substitution with Deep Player Behavior Models

Pfau

Smeddinck

Bikas

et al. 2020

Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems

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Many online games suffer when players drop off due to lost connections or quitting prematurely, which leads to match terminations or game-play imbalances. While rule-based outcome evaluations or substitutions with bots are frequently used to mitigate such disruptions, these techniques are often perceived as unsatisfactory. Deep learning methods have successfully been used in deep player behavior modelling (DPBM) to produce non-player characters or bots which show more complex behavior patterns than those modelled using traditional AI techniques. Motivated by these findings, we present an investigation of the player-perceived awareness, believability and representativeness, when substituting disconnected players with DPBM agents in an online-multiplayer action game. Both quantitative and qualitative outcomes indicate that DPBM agents perform similarly to human players and that players were unable to detect substitutions. In contrast, players were able to detect substitution with agents driven by more traditional heuristics.

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