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Lookup NU author(s): Dr Jan SmeddinckORCiD
This is the authors' accepted manuscript of a conference proceedings (inc. abstract) that has been published in its final definitive form by Association for Computing Machinery, 2020.
For re-use rights please refer to the publisher's terms and conditions.
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.
Author(s): Pfau J, Smeddinck JD, Bikas I, Malaka R
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: CHI Conference on Human Factors in Computing Systems (CHI '20)
Year of Conference: 2020
Online publication date: 25/04/2020
Acceptance date: 20/12/2019
Date deposited: 29/04/2020
Publisher: Association for Computing Machinery
Library holdings: Search Newcastle University Library for this item
Series Title: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems