Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers最新文献
Untimely interruptions from our mobile devices may have a significant impact on our work performance, stress and well-being, and in critical situations, such as when driving, can even have fatal consequences. State of the art approaches to inferring interruptiblity of mobile users harness an array of sensors available on our devices. Yet, the energy consumption of these sensors clashes with the need to preserve the most precious of the device's resources - its battery charge. In this work we revisit the sensor-based approach to interruptiblity inference and examine the trade-off between a sensor's energy use and its contribution to interruptiblity modelling. Our findings, based on a two week long field study with 14 users demonstrate that turning on additional sensors indeed improves interruptiblity inference, but at a cost of increased energy consumption. We then propose an interruptiblity management systems that uses the classifier confidence as a knob allowing fine-grain tuning along the trade-off front, thus enabling user- and application- specific energy-optimal interruptiblity management.
{"title":"Trading energy for accuracy in mobile interruptiblity inference","authors":"Aleksandar Cuculoski, V. Pejović","doi":"10.1145/3410530.3414429","DOIUrl":"https://doi.org/10.1145/3410530.3414429","url":null,"abstract":"Untimely interruptions from our mobile devices may have a significant impact on our work performance, stress and well-being, and in critical situations, such as when driving, can even have fatal consequences. State of the art approaches to inferring interruptiblity of mobile users harness an array of sensors available on our devices. Yet, the energy consumption of these sensors clashes with the need to preserve the most precious of the device's resources - its battery charge. In this work we revisit the sensor-based approach to interruptiblity inference and examine the trade-off between a sensor's energy use and its contribution to interruptiblity modelling. Our findings, based on a two week long field study with 14 users demonstrate that turning on additional sensors indeed improves interruptiblity inference, but at a cost of increased energy consumption. We then propose an interruptiblity management systems that uses the classifier confidence as a knob allowing fine-grain tuning along the trade-off front, thus enabling user- and application- specific energy-optimal interruptiblity management.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"131 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83741613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Recalling medical instructions provided during a doctor's visit can be difficult due to access barriers, primarily for older adults who visit doctors multiple times per year and rely on their memory to act on doctor's recommendations. There are several interventions that aid patients in recalling information after doctors' visits; however, some have been proven ineffective, and those that are effective can present additional challenges for older adults. In this paper, we explore the challenges that older adults with chronic illnesses face when collecting and recalling medical instructions from multiple doctors' visits and discuss implications for AI-assisted tools to enable older adults better access medical instructions. We interviewed 12 older adults to understand their strategies for gathering and recalling information, the challenges they face, and their opinions about automatic transcription of their conversations with doctors to help them recall information after a visit. We found that participants face accessibility challenges such as hearing information and recalling medical instructions that require additional time or follow-up with the doctor. Therefore, patients saw potential value for a tool that automatically transcribes and helps with recall of medical instructions, but desired additional features to summarize, categorize, and highlight critical information from the conversations with their doctors.
{"title":"Understanding barriers to medical instruction access for older adults: implications for AI-assisted tools","authors":"Pegah Karimi, Aqueasha Martin-Hammond","doi":"10.1145/3410530.3414412","DOIUrl":"https://doi.org/10.1145/3410530.3414412","url":null,"abstract":"Recalling medical instructions provided during a doctor's visit can be difficult due to access barriers, primarily for older adults who visit doctors multiple times per year and rely on their memory to act on doctor's recommendations. There are several interventions that aid patients in recalling information after doctors' visits; however, some have been proven ineffective, and those that are effective can present additional challenges for older adults. In this paper, we explore the challenges that older adults with chronic illnesses face when collecting and recalling medical instructions from multiple doctors' visits and discuss implications for AI-assisted tools to enable older adults better access medical instructions. We interviewed 12 older adults to understand their strategies for gathering and recalling information, the challenges they face, and their opinions about automatic transcription of their conversations with doctors to help them recall information after a visit. We found that participants face accessibility challenges such as hearing information and recalling medical instructions that require additional time or follow-up with the doctor. Therefore, patients saw potential value for a tool that automatically transcribes and helps with recall of medical instructions, but desired additional features to summarize, categorize, and highlight critical information from the conversations with their doctors.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79393653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mobile phones have become a new means of accessing and executing crowdsourcing tasks in a variety of situations. Yet, while it is commonly assumed that people are likely to perform these tasks during activity breakpoints, it remains unclear whether different types of such breakpoints affect the likelihood that crowdsourcing tasks will be performed. To explore this question, we classified breakpoints into five types, according to phone users' preceding, current, and upcoming activities, and conducted a six-week experience sampling method study of 30 users' breakpoint-type-specific crowdsourcing-task performance behavior. We found that these participants tended to engage in crowdsourcing tasks when they were at breakpoints between two different activities, rather than within an activity, and also when breakpoints were long. Additionally, the higher the complexity of their previous activity, the lower the crowdsourcing-task execution rate. However, high complexity of the post-crowdsourcing task activity had no obvious impact on execution rate.
{"title":"Effects of activity breakpoints on mobile crowdsourcing task performance","authors":"Chia-En Chiang, Yung-Ju Chang, Felicia Feng","doi":"10.1145/3410530.3414409","DOIUrl":"https://doi.org/10.1145/3410530.3414409","url":null,"abstract":"Mobile phones have become a new means of accessing and executing crowdsourcing tasks in a variety of situations. Yet, while it is commonly assumed that people are likely to perform these tasks during activity breakpoints, it remains unclear whether different types of such breakpoints affect the likelihood that crowdsourcing tasks will be performed. To explore this question, we classified breakpoints into five types, according to phone users' preceding, current, and upcoming activities, and conducted a six-week experience sampling method study of 30 users' breakpoint-type-specific crowdsourcing-task performance behavior. We found that these participants tended to engage in crowdsourcing tasks when they were at breakpoints between two different activities, rather than within an activity, and also when breakpoints were long. Additionally, the higher the complexity of their previous activity, the lower the crowdsourcing-task execution rate. However, high complexity of the post-crowdsourcing task activity had no obvious impact on execution rate.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79664304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ge Ma, Weixi Gu, Qiyang Huang, Guowei Zhu, Kan Lv, Yujia Li
The concept of "Industrial Internet" was first proposed by General Electric in 2012. It aims to promote the intellectualization of the whole service system. However, with the development of the Industrial Internet, some criminals launch attacks on industrial control terminals (such as computers and mobile devices), causing the failure of industrial control terminals or wrong instructions, which resulting in factory losses. Therefore, there is an immediate need to extract valuable information from mobile network streaming, accurately detect abnormal behaviors and timely raise the alarm. In this paper, we propose a method of anomaly detection for mobile devices in Industrial Internet based on knowledge graph and demonstrate the results by using visualization technology. First, we use the optimized data mining algorithm based on frequent item sets to analyse the data, so that our method can accurately detect different kinds of concurrent attacks. Second, this method is able to locate the IP addresses of the attacker and the victim accurately. Third, we design an anomaly alarm module, which can visualize the results in multiple dimensions and assist security administrators to understand complex network situation in real time and take corresponding measures according to the network anomaly.
{"title":"Anomaly detection for mobile devices in industrial internet","authors":"Ge Ma, Weixi Gu, Qiyang Huang, Guowei Zhu, Kan Lv, Yujia Li","doi":"10.1145/3410530.3414422","DOIUrl":"https://doi.org/10.1145/3410530.3414422","url":null,"abstract":"The concept of \"Industrial Internet\" was first proposed by General Electric in 2012. It aims to promote the intellectualization of the whole service system. However, with the development of the Industrial Internet, some criminals launch attacks on industrial control terminals (such as computers and mobile devices), causing the failure of industrial control terminals or wrong instructions, which resulting in factory losses. Therefore, there is an immediate need to extract valuable information from mobile network streaming, accurately detect abnormal behaviors and timely raise the alarm. In this paper, we propose a method of anomaly detection for mobile devices in Industrial Internet based on knowledge graph and demonstrate the results by using visualization technology. First, we use the optimized data mining algorithm based on frequent item sets to analyse the data, so that our method can accurately detect different kinds of concurrent attacks. Second, this method is able to locate the IP addresses of the attacker and the victim accurately. Third, we design an anomaly alarm module, which can visualize the results in multiple dimensions and assist security administrators to understand complex network situation in real time and take corresponding measures according to the network anomaly.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76168535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We present an extensible sensor research platform suitable for motion- and sound-based activity and context recognition in wearable and ubiquitous computing applications. The 30x30mm platform is extensible through plug-in boards, which makes it well suited to explore novel sensor technologies. Its firmware can acquire 9-axis inertial measurement unit (IMU) data and device orientation in quaternions at up to 565Hz, sound at 16KHz and external analog inputs, without any programming, allowing for use by non-experts. The data of distinct modalities can be acquired in isolation or simultaneously for multimodal sensing, and can be streamed over Bluetooth or stored locally. The platform has a real-time clock, which enables the acquisition of the data from multiple nodes with a ±10ppm frequency tolerance, without requiring inter-node connectivity. This is useful to collect data from multiple people. Acquiring multimodal data, the measured power consumption is 222mW when streaming and 67mW when logging to an SD card. With a 165mAh battery, this leads to 2h15mn and 9h of operation, respectively, with a weight of 10.8g (6.75g without battery).
{"title":"ARM cortex M4-based extensible multimodal wearable platform for sensor research and context sensing from motion & sound","authors":"D. Roggen","doi":"10.1145/3410530.3414368","DOIUrl":"https://doi.org/10.1145/3410530.3414368","url":null,"abstract":"We present an extensible sensor research platform suitable for motion- and sound-based activity and context recognition in wearable and ubiquitous computing applications. The 30x30mm platform is extensible through plug-in boards, which makes it well suited to explore novel sensor technologies. Its firmware can acquire 9-axis inertial measurement unit (IMU) data and device orientation in quaternions at up to 565Hz, sound at 16KHz and external analog inputs, without any programming, allowing for use by non-experts. The data of distinct modalities can be acquired in isolation or simultaneously for multimodal sensing, and can be streamed over Bluetooth or stored locally. The platform has a real-time clock, which enables the acquisition of the data from multiple nodes with a ±10ppm frequency tolerance, without requiring inter-node connectivity. This is useful to collect data from multiple people. Acquiring multimodal data, the measured power consumption is 222mW when streaming and 67mW when logging to an SD card. With a 165mAh battery, this leads to 2h15mn and 9h of operation, respectively, with a weight of 10.8g (6.75g without battery).","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"12 10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88016045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shibo Zhang, Qiuyang Xu, Sougata Sen, N. Alshurafa
Smartphones, with their ubiquity and plethora of embedded sensors enable on-the-go measurement. Here, we describe one novel measurement potential, weight measurement, by turning an everyday smartphone into a weighing scale. We describe VibroScale, our vibration-based approach to measuring the weight of objects that are small in size. Being able to objectively measure the weight of objects in free-living settings, without the burden of carrying a scale, has several possible uses, particularly in weighing small food items. We designed a smartphone app and regression algorithm, which we termed VibroScale, that estimates the relative induced intensity of an object placed on the smartphone. We tested our proposed method using more than 50 fruits and other everyday objects of different sizes and weights. Our smartphone-based method can measure the weight of fruit without relying on an actual scale. Overall, we observed that VibroScale can measure one type of object with a mean absolute error of 12.4 grams and a mean absolute percentage error of 7.7%. We believe that in future this approach can be generalized to estimate calories and measure the weight of various types of objects.
{"title":"VibroScale","authors":"Shibo Zhang, Qiuyang Xu, Sougata Sen, N. Alshurafa","doi":"10.1145/3410530.3414397","DOIUrl":"https://doi.org/10.1145/3410530.3414397","url":null,"abstract":"Smartphones, with their ubiquity and plethora of embedded sensors enable on-the-go measurement. Here, we describe one novel measurement potential, weight measurement, by turning an everyday smartphone into a weighing scale. We describe VibroScale, our vibration-based approach to measuring the weight of objects that are small in size. Being able to objectively measure the weight of objects in free-living settings, without the burden of carrying a scale, has several possible uses, particularly in weighing small food items. We designed a smartphone app and regression algorithm, which we termed VibroScale, that estimates the relative induced intensity of an object placed on the smartphone. We tested our proposed method using more than 50 fruits and other everyday objects of different sizes and weights. Our smartphone-based method can measure the weight of fruit without relying on an actual scale. Overall, we observed that VibroScale can measure one type of object with a mean absolute error of 12.4 grams and a mean absolute percentage error of 7.7%. We believe that in future this approach can be generalized to estimate calories and measure the weight of various types of objects.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"241 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84142931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Individuals with mobility impairments often discuss the challenges associated with donning and doffing shirts (i.e. putting them on and taking them off). Limited previous work has tackled this issue, but the comfort and aesthetic integrity of the shirt is often forgotten. In this paper, we co-designed an adaptive shirt with individuals with mobility impairments and personal support workers. With the insights from these discussions, we developed an augmented top that transforms wide sizes (for the easy donning and doffing) into their preferred fit. The study resulted in the design of SMAller Aid, which uses Shape Memory Alloy (SMA) springs to retract to a smaller size. The shirt adapts to their needs while retaining its aesthetic integrity to empower them with independence and no required assistance.
{"title":"SMAller aid: exploring shape-changing assistive wearables for people with mobility impairment","authors":"Amanda McLeod, Sara Nabil, L. Jones, A. Girouard","doi":"10.1145/3410530.3414418","DOIUrl":"https://doi.org/10.1145/3410530.3414418","url":null,"abstract":"Individuals with mobility impairments often discuss the challenges associated with donning and doffing shirts (i.e. putting them on and taking them off). Limited previous work has tackled this issue, but the comfort and aesthetic integrity of the shirt is often forgotten. In this paper, we co-designed an adaptive shirt with individuals with mobility impairments and personal support workers. With the insights from these discussions, we developed an augmented top that transforms wide sizes (for the easy donning and doffing) into their preferred fit. The study resulted in the design of SMAller Aid, which uses Shape Memory Alloy (SMA) springs to retract to a smaller size. The shirt adapts to their needs while retaining its aesthetic integrity to empower them with independence and no required assistance.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84163458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Min-Wei Hung, Tina Chien-Wen Yuan, Yi-Chao Chen, Nanyi Bi, Wan-Chen Lee, Ming-Chyi Huang, Chuang-Wen You
Technology abuse refers to the excessive use of personal technology devices, which can have a negative impact on adolescent patients' lifestyles and might lead to negative physical and mental health outcomes. This study conducted a needs assessment study to gain guidelines for the development of assistive systems to help adolescents deal with technology abuse issues. Our results identify current difficulties to depict screen use on multiple devices for the recording of device usage data as well as behavioral data related to lifestyles (e.g., sleep conditions). We also proposed a preliminary design of technology solutions to make the information sharing among patients and parents possible for constructive communication between them and provide treatment teams with the data necessary for diagnosis and the formulation of treatment plans.
{"title":"Leveraging family force to assist adolescent patients in the treatment of technology abuse","authors":"Min-Wei Hung, Tina Chien-Wen Yuan, Yi-Chao Chen, Nanyi Bi, Wan-Chen Lee, Ming-Chyi Huang, Chuang-Wen You","doi":"10.1145/3410530.3414391","DOIUrl":"https://doi.org/10.1145/3410530.3414391","url":null,"abstract":"Technology abuse refers to the excessive use of personal technology devices, which can have a negative impact on adolescent patients' lifestyles and might lead to negative physical and mental health outcomes. This study conducted a needs assessment study to gain guidelines for the development of assistive systems to help adolescents deal with technology abuse issues. Our results identify current difficulties to depict screen use on multiple devices for the recording of device usage data as well as behavioral data related to lifestyles (e.g., sleep conditions). We also proposed a preliminary design of technology solutions to make the information sharing among patients and parents possible for constructive communication between them and provide treatment teams with the data necessary for diagnosis and the formulation of treatment plans.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84257613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper targets the SHL recognition challenge, which focuses on the location-independent and user-independent activity recognition using smartphone sensors. To address this long-range temporal problem with periodic nature, we propose a new approach (team IndRNN), an Independently Recurrent Neural Network (IndRNN) based long-term temporal activity recognition with spatial and frequency domain features. The data is first segmented into one second sliding windows, then temporal and frequency domain features are extracted as short-term temporal features. A deep IndRNN model is used to predict the unknown test dataset location. Under the predicted location, a deep IndRNN model is further used to classify the 8 activities with best performed features. Finally, transfer learning and model fusion are used to improve the result under the user-independence case. The proposed method achieves 86.94% accuracy on the validation set at the predicted location.
{"title":"IndRNN based long-term temporal recognition in the spatial and frequency domain","authors":"Beidi Zhao, Shuai Li, Yanbo Gao","doi":"10.1145/3410530.3414355","DOIUrl":"https://doi.org/10.1145/3410530.3414355","url":null,"abstract":"This paper targets the SHL recognition challenge, which focuses on the location-independent and user-independent activity recognition using smartphone sensors. To address this long-range temporal problem with periodic nature, we propose a new approach (team IndRNN), an Independently Recurrent Neural Network (IndRNN) based long-term temporal activity recognition with spatial and frequency domain features. The data is first segmented into one second sliding windows, then temporal and frequency domain features are extracted as short-term temporal features. A deep IndRNN model is used to predict the unknown test dataset location. Under the predicted location, a deep IndRNN model is further used to classify the 8 activities with best performed features. Finally, transfer learning and model fusion are used to improve the result under the user-independence case. The proposed method achieves 86.94% accuracy on the validation set at the predicted location.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90589962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Multi-agent systems have attracted much attention in the recent years due to their capabilities to handle complex and computation-heavy tasks and compatibility with incentive schemes. Considering the difficulty of creating an actual prototype and environment for evaluation, a simulation platform is a cheap and efficient way in analyzing and testing, prior to real environmental implementations. Existing simulators for multi-agent systems are inadequate to analyze the effects of different customized incentive schemes on agents' behavior patterns due to two reasons: 1) They lack the functionality to support various types of complex incentives, e.g., mixture of monetary incentives and non-monetary incentives, which influences agents' behaviors explicitly and implicitly; 2) They are not able to emulate heterogeneous agents' realtime behaviors that are influenced by complex incentives and deviate from their original behavior patterns shown in historical traces. In this paper, we focus on mobile agents that can move in a patio-temporal space, and we present a physical knowledge aided multi-agent simulation platform considering the influence of both direct and indirect incentives unified through a general utility-driven agent reaction function. The behaviors of agents are then emulated in three behavioral models: myopic, semi-myopic, and farsighted, by varying the assumption of agents in maximizing their utilities and integrating the physical knowledge and historical mobility patterns. We finally examine the effectiveness of the platform in incentivizing vehicle agents to optimize the final distribution of the agents through a ride-sharing vehicle experimental scenario. The emulated agents' behaviors can also be collected into data traces for analyzing other patterns of the agents.
{"title":"A generative simulation platform for multi-agent systems with incentives","authors":"Zhengwei Wu, Xiaoxi Zhang, Susu Xu, Xinlei Chen, Pei Zhang, H. Noh, Carlee Joe-Wong","doi":"10.1145/3410530.3414590","DOIUrl":"https://doi.org/10.1145/3410530.3414590","url":null,"abstract":"Multi-agent systems have attracted much attention in the recent years due to their capabilities to handle complex and computation-heavy tasks and compatibility with incentive schemes. Considering the difficulty of creating an actual prototype and environment for evaluation, a simulation platform is a cheap and efficient way in analyzing and testing, prior to real environmental implementations. Existing simulators for multi-agent systems are inadequate to analyze the effects of different customized incentive schemes on agents' behavior patterns due to two reasons: 1) They lack the functionality to support various types of complex incentives, e.g., mixture of monetary incentives and non-monetary incentives, which influences agents' behaviors explicitly and implicitly; 2) They are not able to emulate heterogeneous agents' realtime behaviors that are influenced by complex incentives and deviate from their original behavior patterns shown in historical traces. In this paper, we focus on mobile agents that can move in a patio-temporal space, and we present a physical knowledge aided multi-agent simulation platform considering the influence of both direct and indirect incentives unified through a general utility-driven agent reaction function. The behaviors of agents are then emulated in three behavioral models: myopic, semi-myopic, and farsighted, by varying the assumption of agents in maximizing their utilities and integrating the physical knowledge and historical mobility patterns. We finally examine the effectiveness of the platform in incentivizing vehicle agents to optimize the final distribution of the agents through a ride-sharing vehicle experimental scenario. The emulated agents' behaviors can also be collected into data traces for analyzing other patterns of the agents.","PeriodicalId":7183,"journal":{"name":"Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers","volume":"78 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89633034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Adjunct Proceedings of the 2020 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2020 ACM International Symposium on Wearable Computers
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