Comparing Learning Techniques for Hidden Markov Models of Human Supervisory Control Behavior

Boussemart, Yves; Fargeas, Jonathan Las; Cummings, Mary L.; Roy, Nicholas

doi:10.2514/6.2009-1842

Cited by 7 publications

(5 citation statements)

References 12 publications

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“…HMM is one of the most popular probabilistic models that have been applied intensively to investigate hidden behavioral patterns in various fields (e.g., Jeong et al 2008, Boussemart et al 2009, Carola, Mirabeau, and Gross 2011, Tang et al 2016. To analyze VPAL behaviors, we created a mapping of the HMM parameters to the actual game scenario, where the hidden states (S t ) represent the player's time-dependent strategy of their behaviors and the observed output (O t ) represent abstractions of the observed low-level actions executed by the player (see Table 1).…”

Section: Hmm Modelmentioning

confidence: 99%

“…The algorithm iteratively adjusts the parameters of the model to maximize the likelihood that the sequence of observed data was generated by the HMM. Due to absence of having a priori of what the expected hidden states should be, we used the EM algorithm in an unsupervised mode, which utilizes Bayesian inference to automatically infer the optimal parameters of the model (Boussemart et al 2009). The algorithm uses a gradient search in the parameter space to optimize the likelihood.…”

Section: The Algorithm and Modeling Processmentioning

confidence: 99%

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Modeling Individual Differences in Game Behavior Using HMM

Bunian

Canossa

Colvin

et al. 2021

AIIDE

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Player modeling is an important concept that has gained much attention in game research due to its utility in developing adaptive techniques to target better designs for engagement and retention. Previous work has explored modeling individual differences using machine learning algorithms performed on aggregated game actions. However, players’ individual differences may be better manifested through sequential patterns of the in-game player’s actions. While few works have explored sequential analysis of player data, none have explored the use of Hidden Markov Models (HMM) to model individual differences, which is the topic of this paper. In particular, we developed a modeling approach using data collected from players playing a Role-Playing Game (RPG). Our proposed approach is two fold: 1. We present a Hidden Markov Model (HMM) of player in-game behaviors to model individual differences, and 2. using the output of the HMM, we generate behavioral features used to classify real world players’ characteristics, including game expertise and the big five personality traits. Our results show predictive power for some of personality traits, such as game expertise and conscientiousness, but the most influential factor was game expertise.

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Section: Hmm Modelmentioning

confidence: 99%

Section: The Algorithm and Modeling Processmentioning

confidence: 99%

Modeling Individual Differences in Game Behavior Using HMM

Bunian

Canossa

Colvin

et al. 2021

AIIDE

View full text Add to dashboard Cite

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“…The HMM formalism is widely used in machine learning, especially in speech recognition [27] and development of human operator behavior models in driving [28]. HMMs using an unsupervised approach to model training have been shown to provide more accurate operator behavior models over supervised learning approaches [29], [30]. Because an HMM can present higher level operator behavioral states using hidden system states based on lower level operator interactions with a supervisory control system like a UAV ground control station, the HMM was selected as the modeling framework for this effort.…”

Section: Markov Modeling Approachesmentioning

confidence: 99%

Operator Strategy Model Development in UAV Hacking Detection

Zhu

Cummings

Elfar

et al. 2019

IEEE Trans. Human-Mach. Syst.

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“…Understanding how navigators behave when they are involved in a ship collision is crucial for preventing collision accidents caused by human error, which remains a major cause of shipping incidents [1][2][3]. In relation to this, behavior models of navigators who operate in complex, high-risk domains are of great value because of high losses due to navigator failure [4,5]. To ensure the safety of maritime transportation, scientific measures must be taken to respond to and prevent various types of maritime accidents, instead of having ambiguous anticipations of possible human errors [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Development of Navigator Behavior Models for the Evaluation of Collision Avoidance Behavior in the Collision-Prone Navigation Environment

2019

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In order to prevent ship collisions, it is important to understand the behavior of navigators that leads to these collisions. The main cause of marine accidents in the Republic of Korea is attributed to navigator error, particularly in collisions. Hence, reducing navigator error is a key issue that needs to be addressed to prevent accidents. However, the lack of objective measure to quantify navigator error remains a challenge. The purpose of this study is to develop an objective identification of a navigator’s behavior in a collision encountering situation. Two behavior models for the success and failure of collision avoidance are developed by collecting participants’ actions, using a ship maneuvering simulator within a given scenario. These maneuvering behavior models are validated in terms of their discrimination powers. The results show that maneuvering behavior is clearly identified in the data processing and model development phases. The proposed behavior models are expected to provide a better understanding of how navigators behave to help reduce collision accidents.

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Comparing Learning Techniques for Hidden Markov Models of Human Supervisory Control Behavior

Cited by 7 publications

References 12 publications

Modeling Individual Differences in Game Behavior Using HMM

Modeling Individual Differences in Game Behavior Using HMM

Operator Strategy Model Development in UAV Hacking Detection

Development of Navigator Behavior Models for the Evaluation of Collision Avoidance Behavior in the Collision-Prone Navigation Environment

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