For a domestic personal robot, personalized services are as important as predesigned tasks, because the robot needs to adjust the home state based on the operator's habits. An operator's habits are composed of...For a domestic personal robot, personalized services are as important as predesigned tasks, because the robot needs to adjust the home state based on the operator's habits. An operator's habits are composed of cues, behaviors, and rewards. This article introduces behavioral footprints to describe the operator's behaviors in a house, and applies the inverse reinforcement learning technique to extract the operator's habits, represented by a reward function. We implemented the proposed approach with a mobile robot on indoor temperature adjustment, and compared this approach with a baseline method that recorded all the cues and behaviors of the operator. The result shows that the proposed approach allows the robot to reveal the operator's habits accurately and adjust the environment state accordingly.展开更多
Potential behavior prediction involves understanding the latent human behavior of specific groups,and can assist organizations in making strategic decisions.Progress in information technology has made it possible to a...Potential behavior prediction involves understanding the latent human behavior of specific groups,and can assist organizations in making strategic decisions.Progress in information technology has made it possible to acquire more and more data about human behavior.In this paper,we examine behavior data obtained in realworld scenarios as an information network composed of two types of objects(humans and actions)associated with various attributes and three types of relationships(human-human,human-action,and action-action),which we call the heterogeneous behavior network(HBN).To exploit the abundance and heterogeneity of the HBN,we propose a novel network embedding method,human-action-attribute-aware heterogeneous network embedding(a4 HNE),which jointly considers structural proximity,attribute resemblance,and heterogeneity fusion.Experiments on two real-world datasets show that this approach outperforms other similar methods on various heterogeneous information network mining tasks for potential behavior prediction.展开更多
There is a long and rich tradition in the social sciences of using models of collective behavior in animals as jump- ing-off points for the study of human behavior, including collective human behavior. Here, we come a...There is a long and rich tradition in the social sciences of using models of collective behavior in animals as jump- ing-off points for the study of human behavior, including collective human behavior. Here, we come at the problem in a slightly different fashion. We ask whether models of collective human behavior have anything to offer those who study animal behavior. Our brief example of tipping points, a model first developed in the physical sciences and later used in the social sciences, suggests that the analysis of human collective behavior does indeed have considerable to offer展开更多
基金supported in part by Hong Kong RGC GRC (CUHK14205914 and CUHK415512)
文摘For a domestic personal robot, personalized services are as important as predesigned tasks, because the robot needs to adjust the home state based on the operator's habits. An operator's habits are composed of cues, behaviors, and rewards. This article introduces behavioral footprints to describe the operator's behaviors in a house, and applies the inverse reinforcement learning technique to extract the operator's habits, represented by a reward function. We implemented the proposed approach with a mobile robot on indoor temperature adjustment, and compared this approach with a baseline method that recorded all the cues and behaviors of the operator. The result shows that the proposed approach allows the robot to reveal the operator's habits accurately and adjust the environment state accordingly.
基金Project supported by the National Natural Science Foundation of China(Nos.U1509206,61625107,and U1611461)the Key Program of Zhejiang Province,China(No.2015C01027).
文摘Potential behavior prediction involves understanding the latent human behavior of specific groups,and can assist organizations in making strategic decisions.Progress in information technology has made it possible to acquire more and more data about human behavior.In this paper,we examine behavior data obtained in realworld scenarios as an information network composed of two types of objects(humans and actions)associated with various attributes and three types of relationships(human-human,human-action,and action-action),which we call the heterogeneous behavior network(HBN).To exploit the abundance and heterogeneity of the HBN,we propose a novel network embedding method,human-action-attribute-aware heterogeneous network embedding(a4 HNE),which jointly considers structural proximity,attribute resemblance,and heterogeneity fusion.Experiments on two real-world datasets show that this approach outperforms other similar methods on various heterogeneous information network mining tasks for potential behavior prediction.
文摘There is a long and rich tradition in the social sciences of using models of collective behavior in animals as jump- ing-off points for the study of human behavior, including collective human behavior. Here, we come at the problem in a slightly different fashion. We ask whether models of collective human behavior have anything to offer those who study animal behavior. Our brief example of tipping points, a model first developed in the physical sciences and later used in the social sciences, suggests that the analysis of human collective behavior does indeed have considerable to offer
