Automated Playtesting with a Cognitive Model of Sensorimotor Coordination

Lee, Injung; Kim, Hyunchul; Lee, Byungjoo

doi:10.1145/3474085.3475429

Cited by 11 publications

(3 citation statements)

References 41 publications

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“…The result on MiniDungeons 2 game shows how different personas brings different behavior in response to game contents which can be seen as different play-styles. Lee et al [34] investigate a data-driven cognitive model of human performance in moving-target acquisition to estimate the game difficulty for different players with different skill level. There is limited research on the emotion prediction and its usage for automation of PX evaluation.…”

Section: Related Workmentioning

confidence: 99%

Model-based Player Experience Testing with Emotion Pattern Verification

Ansari

Prasetya

Prandi

et al. 2023

Lecture Notes in Computer Science

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Player eXperience (PX) testing has attracted attention in the game industry as video games become more complex and widespread. Understanding players’ desires and their experience are key elements to guarantee the success of a game in the highly competitive market. Although a number of techniques have been introduced to measure the emotional aspect of the experience, automated testing of player experience still needs to be explored. This paper presents a framework for automated player experience testing by formulating emotion patterns’ requirements and utilizing a computational model of players’ emotions developed based on a psychological theory of emotions along with a model-based testing approach for test suite generation. We evaluate the strength of our framework by performing mutation test. The paper also evaluates the performance of a search-based generated test suite and LTL model checking-based test suite in revealing various variations of temporal and spatial emotion patterns. Results show the contribution of both algorithms in generating complementary test cases for revealing various emotions in different locations of a game level.

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Section: Related Workmentioning

confidence: 99%

Model-based Player Experience Testing with Emotion Pattern Verification

Ansari

Prasetya

Prandi

et al. 2023

Lecture Notes in Computer Science

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“…The parameters 𝑎 and 𝑏 must be identified for each user and type of movement (e.g., interaction technique) separately. Recently, more advanced models have been developed to predict, e.g., the failure rate and button press timing in moving-target acquisition tasks [36,37].…”

Section: Forward Models Of Interaction Movementsmentioning

confidence: 99%

Simulating Interaction Movements via Model Predictive Control

Klar¹,

Fischer²,

Fleig³

et al. 2022

Preprint

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We present a method to simulate movement in interaction with computers, using Model Predictive Control (MPC). The method starts from understanding interaction from an Optimal Feedback Control (OFC) perspective. We assume that users aim to minimize an internalized cost function, subject to the constraints imposed by the human body and the interactive system. In contrast to previous linear approaches used in HCI, MPC can compute optimal controls for nonlinear systems. This allows us to use state-of-the-art biomechanical models and handle nonlinearities that occur in almost any interactive system. Instead of torque actuation, our model employs second-order muscles acting directly at the joints. We compare three different cost functions and evaluate the simulated trajectories against user movements in a Fitts' Law type pointing study with four different interaction techniques. Our results show that the combination of distance, control, and joint acceleration cost matches individual users' movements best, and predicts movements with an accuracy that is within the between-user variance. To aid HCI researchers and designers, we introduce CFAT, a novel method to identify maximum voluntary torques in joint-actuated models based on experimental data, and give practical advice on how to simulate human movement for different users, interaction techniques, and tasks.CCS Concepts: • Human-centered computing → HCI theory, concepts and models.

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“…Selecting a moving target within a particular time window is a task that arises in some situations, e.g., smartphone games. For this task, researchers have studied error rates and models of timing performance [25,26] as well as automated game playtesting [27]. Controlling a timed activation in these cases (e.g., via a button or screen press) might be interpreted as an activation of a single switch.…”

Section: Selecting a Moving Target Within A Time Windowmentioning

confidence: 99%