Oui, Chef!!: Supervised Learning for Novel Gameplay with Believable AI

Cimolino, Gabriele; Lee, Sam; Petraroia, Quentin; Graham, T. C. Nicholas

doi:10.1145/3341215.3358247

Cited by 6 publications

(1 citation statement)

References 5 publications

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“…With the recent boom in Artificial Intelligence (AI) applications, game designers have been increasingly exploring a variety of AI approaches in computer games [72,79], from procedural content generation (PCG) [10,56,63] to intelligent non-player characters [24,72]. As a result, designers are producing novel gameplay experiences by foregrounding these complex systems in the user interface (UI) and enabling players to directly interact with the AI as part of the core gameplay experience [8,21,29,54,64,79]. With this development, players are now becoming aware of and playfully interacting with a growing number of these complex systems, and in turn, constructing mental models that may be attention to for the development of their mental model, either attending to gameplay observations (i.e., bottom-up focus) or utilizing prior knowledge or experiences (i.e., top-down focus) to develop their mental model.…”

Section: Introductionmentioning

confidence: 99%

"I Want To See How Smart This AI Really Is": Player Mental Model Development of an Adversarial AI Player

Villareale

Harteveld

Zhu

2022

Proc. ACM Hum.-Comput. Interact.

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Understanding players' mental models are crucial for game designers who wish to successfully integrate player-AI interactions into their game. However, game designers face the difficult challenge of anticipating how players model these AI agents during gameplay and how they may change their mental models with experience. In this work, we conduct a qualitative study to examine how a pair of players develop mental models of an adversarial AI player during gameplay in the multiplayer drawing game iNNk. We conducted ten gameplay sessions in which two players (n = 20, 10 pairs) worked together to defeat an AI player. As a result of our analysis, we uncovered two dominant dimensions that describe players' mental model development (i.e., focus and style). The first dimension describes the focus of development which refers to what players pay attention to for the development of their mental model (i.e., top-down vs. bottom-up focus). The second dimension describes the differences in the style of development, which refers to how players integrate new information into their mental model (i.e., systematic vs. reactive style). In our preliminary framework, we further note how players process a change when a discrepancy occurs, which we observed occur through comparisons (i.e., compare to other systems, compare to gameplay, compare to self). We offer these results as a preliminary framework for player mental model development to help game designers anticipate how different players may model adversarial AI players during gameplay.

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