Behavior Modeling for Interaction

2016 ◽  
pp. 13-45
Author(s):  
Ella Roubtsova
Keyword(s):  
Behavior Modeling

Human Behavior Modeling Method Based on the Causality Between the Situation and the Behavior

2011 ◽  
Vol 131 (3) ◽  
pp. 635-643 ◽  
Author(s):  
Kohjiro Hashimoto ◽  
Kae Doki ◽  
Shinji Doki ◽  
Shigeru Okuma ◽  
Akihiro Torii
Keyword(s):  
Human Behavior ◽  
Modeling Method ◽  
Behavior Modeling ◽  
Human Behavior Modeling

Exploring the Possibilities of Co-Simulation with CATIA V6 Dynamic Behavior Modeling

2013 ◽  
Vol 23 (3-4) ◽  
Author(s):  
Peter Smolek ◽  
Bernhard Heinzl ◽  
Horst Ecker ◽  
Felix Breitenecker
Keyword(s):  
Dynamic Behavior ◽  
Behavior Modeling

Dungeons & Replicants: Automated Game Balancing via Deep Player Behavior Modeling

2020 ◽  
Author(s):  
Johannes Pfau ◽  
Antonios Liapis ◽  
Georg Volkmar ◽  
Georgios N. Yannakakis ◽  
Rainer Malaka
Keyword(s):  
Behavior Modeling

scholarly journals Behavior Modeling for a Beacon-Based Indoor Location System

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4839
Author(s):  
Aritz Bilbao-Jayo ◽  
Aitor Almeida ◽  
Ilaria Sergi ◽  
Teodoro Montanaro ◽  
Luca Fasano ◽  
...  
Keyword(s):  
Behavior Modeling ◽  
Percentage Error ◽  
Location Prediction ◽  
Indoor Environments ◽  
Prediction System ◽  
Indoor Location ◽  
The Mean ◽  
Locating System ◽  
Monitoring Devices ◽  
Do So

In this work we performed a comparison between two different approaches to track a person in indoor environments using a locating system based on BLE technology with a smartphone and a smartwatch as monitoring devices. To do so, we provide the system architecture we designed and describe how the different elements of the proposed system interact with each other. Moreover, we have evaluated the system’s performance by computing the mean percentage error in the detection of the indoor position. Finally, we present a novel location prediction system based on neural embeddings, and a soft-attention mechanism, which is able to predict user’s next location with 67% accuracy.

Player Behavior Modeling for Enhancing Role-Playing Game Engagement

2021 ◽  
Vol 8 (2) ◽  
pp. 464-474
Author(s):  
Sha Zhao ◽  
Yizhi Xu ◽  
Zhiling Luo ◽  
Jianrong Tao ◽  
Shijian Li ◽  
...  
Keyword(s):  
Role Playing ◽  
Behavior Modeling ◽  
Role Playing Game ◽  
Game Engagement

scholarly journals Using the Landsat Burned Area Products to Derive Fire History Relevant for Fire Management and Conservation in the State of Florida, Southeastern USA

Fire ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 26
Author(s):  
Casey Teske ◽  
Melanie K. Vanderhoof ◽  
Todd J. Hawbaker ◽  
Joe Noble ◽  
John Kevin Hiers
Keyword(s):  
Prescribed Fire ◽  
Spatial Data ◽  
Fire History ◽  
Fire Management ◽  
Fire Behavior ◽  
Fire Risk ◽  
Behavior Modeling ◽  
Burned Area ◽  
Burned Areas ◽  
Burn Probability

Development of comprehensive spatially explicit fire occurrence data remains one of the most critical needs for fire managers globally, and especially for conservation across the southeastern United States. Not only are many endangered species and ecosystems in that region reliant on frequent fire, but fire risk analysis, prescribed fire planning, and fire behavior modeling are sensitive to fire history due to the long growing season and high vegetation productivity. Spatial data that map burned areas over time provide critical information for evaluating management successes. However, existing fire data have undocumented shortcomings that limit their use when detailing the effectiveness of fire management at state and regional scales. Here, we assessed information in existing fire datasets for Florida and the Landsat Burned Area products based on input from the fire management community. We considered the potential of different datasets to track the spatial extents of fires and derive fire history metrics (e.g., time since last burn, fire frequency, and seasonality). We found that burned areas generated by applying a 90% threshold to the Landsat burn probability product matched patterns recorded and observed by fire managers at three pilot areas. We then created fire history metrics for the entire state from the modified Landsat Burned Area product. Finally, to show their potential application for conservation management, we compared fire history metrics across ownerships for natural pinelands, where prescribed fire is frequently applied. Implications of this effort include increased awareness around conservation and fire management planning efforts and an extension of derivative products regionally or globally.

scholarly journals Influence of Occupant Behavior for Building Energy Conservation: A Systematic Review Study of Diverse Modeling and Simulation Approach

Buildings ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 41
Author(s):  
Mohammad Nyme Uddin ◽  
Hsi-Hsien Wei ◽  
Hung Lin Chi ◽  
Meng Ni
Keyword(s):  
Systematic Review ◽  
Energy Conservation ◽  
Building Energy ◽  
Outdoor Environment ◽  
Behavior Modeling ◽  
Physical Factors ◽  
Occupant Behavior ◽  
Modeling Approaches ◽  
Building Energy Conservation ◽  
Review Study

Energy consumption in buildings depends on several physical factors, including its physical characteristics, various building services systems/appliances used, and the outdoor environment. However, the occupants’ behavior that determines and regulates the building energy conservation also plays a critical role in the buildings’ energy performance. Compared to physical factors, there are relatively fewer studies on occupants’ behavior. This paper reports a systematic review analysis on occupant behavior and different modeling approaches using the Scopus and Science Direct databases. The comprehensive review study focuses on the current understanding of occupant behavior, existing behavior modeling approaches and their limitations, and key influential parameters on building energy conservation. Finally, the study identifies six significant research gaps for future development: occupant-centered space layout deployment; occupant behavior must be understood in the context of developing or low-income economies; there are higher numbers of quantitative occupant behavior studies than qualitative; the extensive use of survey or secondary data and the lack of real data used in model validation; behavior studies are required for diverse categories building; building information modeling (BIM) integration with existing occupant behavior modeling/simulation. These checklists of the gaps are beneficial for researchers to accomplish the future research in the built environment.

Leveraging Collaborative-Filtering for Personalized Behavior Modeling

2021 ◽  
Vol 5 (1) ◽  
pp. 1-27
Author(s):  
Xuhai Xu ◽  
Prerna Chikersal ◽  
Janine M. Dutcher ◽  
Yasaman S. Sefidgar ◽  
Woosuk Seo ◽  
...  
Keyword(s):  
College Students ◽  
Collaborative Filtering ◽  
Human Behavior ◽  
Work Performance ◽  
Performance Metrics ◽  
Learning Algorithm ◽  
Model Performance ◽  
Majority Voting ◽  
Behavior Modeling ◽  
Depression Detection

The prevalence of mobile phones and wearable devices enables the passive capturing and modeling of human behavior at an unprecedented resolution and scale. Past research has demonstrated the capability of mobile sensing to model aspects of physical health, mental health, education, and work performance, etc. However, most of the algorithms and models proposed in previous work follow a one-size-fits-all (i.e., population modeling) approach that looks for common behaviors amongst all users, disregarding the fact that individuals can behave very differently, resulting in reduced model performance. Further, black-box models are often used that do not allow for interpretability and human behavior understanding. We present a new method to address the problems of personalized behavior classification and interpretability, and apply it to depression detection among college students. Inspired by the idea of collaborative-filtering, our method is a type of memory-based learning algorithm. It leverages the relevance of mobile-sensed behavior features among individuals to calculate personalized relevance weights, which are used to impute missing data and select features according to a specific modeling goal (e.g., whether the student has depressive symptoms) in different time epochs, i.e., times of the day and days of the week. It then compiles features from epochs using majority voting to obtain the final prediction. We apply our algorithm on a depression detection dataset collected from first-year college students with low data-missing rates and show that our method outperforms the state-of-the-art machine learning model by 5.1% in accuracy and 5.5% in F1 score. We further verify the pipeline-level generalizability of our approach by achieving similar results on a second dataset, with an average improvement of 3.4% across performance metrics. Beyond achieving better classification performance, our novel approach is further able to generate personalized interpretations of the models for each individual. These interpretations are supported by existing depression-related literature and can potentially inspire automated and personalized depression intervention design in the future.

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