Bot or not? user perceptions of player substitution with deep player behavior models

Johannes Pfau, Jan David Smeddinck, Ioannis Bikas, Rainer Malaka

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract


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 agent substitutes perform similarly to human players and that players were unable to detect substitutions. Notably, players were in fact able to detect substitution with agents driven by more traditional heuristics.
Original languageEnglish
Title of host publicationProceedings of the 2020 CHI conference on human factors in computing systems
PublisherAssociation for Computing Machinery
Pages1-10
Number of pages10
DOIs
Publication statusPublished - 2020
Externally publishedYes

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