Experience Management in Multi-player Games

Proc. ACM Hum.-Comput. Interact.

2022

Self Cite

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.

Section: Detecting Mental Models Of Aimentioning

confidence: 99%

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

Villareale

Harteveld

Proc. ACM Hum.-Comput. Interact.

2022

Self Cite

“…Riedl and Stern [45] presented the Automated Story Director, which can detect when the target story goal cannot be reached given the player actions, and re-plan accordingly to achieve the desired narrative effect. This is an instance of what is more generally called drama management or experience management [22,33,60,71,75]. Experience Management studies AI systems that automatically adapt interactive experiences such as computer games to serve specific users better and to fulfill specific design goals.…”

Section: Perception and Interpretationmentioning

confidence: 99%

“…As multi-player games become popular among players, further research is also needed to personalize a shared gameplay experience between multiple people. One approach is to expand the framework of experience manager to incorporate models of multiple players [75].…”

Section: Goalsmentioning

confidence: 99%

Player-Centered AI for Automatic Game Personalization: Open Problems

International Conference on the Foundations of Digital Games

Ontañón

2020

Self Cite

Computer games represent an ideal research domain for the next generation of personalized digital applications. This paper presents a player-centered framework of AI for game personalization, complementary to the commonly used system-centered approaches. Built on the Structure of Actions theory, the paper maps out the current landscape of game personalization research and identifies eight open problems that need further investigation. These problems require deep collaboration between technological advancement and player experience design. CCS CONCEPTS • Computing methodologies → Artificial intelligence; • Humancentered computing → User models.

“…Adaptive games leverage knowledge of the player to automatically adapt to better serve specific users or specific design goals [3,22,24,28,34]. These methods often rely on player modeling to detect or predict a set of characteristics of the player that can inform the AI's decisions [31].…”

Section: Player Modeling and Adaptive Gamesmentioning

confidence: 99%

Player Modeling via Multi-Armed Bandits

Gray

International Conference on the Foundations of Digital Games

Forman

et al. 2020

Self Cite

This paper focuses on building personalized player models solely from player behavior in the context of adaptive games. We present two main contributions: The first is a novel approach to player modeling based on multi-armed bandits (MABs). This approach addresses, at the same time and in a principled way, both the problem of collecting data to model the characteristics of interest for the current player and the problem of adapting the interactive experience based on this model. Second, we present an approach to evaluating and fine-tuning these algorithms prior to generating data in a user study. This is an important problem, because conducting user studies is an expensive and labor-intensive process; therefore, an ability to evaluate the algorithms beforehand can save a significant amount of resources. We evaluate our approach in the context of modeling players' social comparison orientation (SCO) and present empirical results from both simulations and real players. CCS CONCEPTS • Computing methodologies → Artificial intelligence; • Humancentered computing → User models.