Coupled Behavior Representation, Modeling, Analysis, and Reasoning

Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:
Journal Article
Citation:
IEEE Intelligent Systems, 2014, 29 (4), pp. 66 - 69
Issue Date:
2014
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Behavior refers to the action, reaction, or property of an entity, human or otherwise, to situations or stimuli in its environment.1 The in-depth analysis of behavior has been increasingly recognized as a crucial means for understanding and disclosing interior driving forces and intrinsic cause-effects on business and social applications, including Web community analysis, counter-terrorism, fraud detection, and customer relationship management. With the deepening and widening of social/business intelligences and their networking, the concept of behavior is in great demand to be consolidated and formalized to deeply scrutinize the native behavior intention, lifecycle, and impact on complex problems and business issues. Although there’s an emerging focus on deep behavior studies, such as social network analysis,2 periodic behavior analysis3 and behavior informatics approach,1 previous research work has mainly focused on individual behaviors without considering the interactions of them. However, with increasing network and community-based events as well as their applications, such as group-based crime and social network interactions, coupling relationships between behaviors contribute to the intrinsic causes and impacts of eventual business and social problems. In the real-world applications, group behavior interactions (that is, coupled behaviors) are widely seen in natural, social, and artificial behavior-related problems. Complex behavior and social applications often exhibit strong explicit or implicit coupling relationships both between their entities and properties. Moreover, it’s also quite difficult to model, analyze, and check behaviors coupled with one another due to the complexity from data, domain, context, and impact perspectives. Due to the emerging popularity and importance of coupled behaviors, the representation, modeling, analysis, mining and learning, and determination of coupled behaviors are becoming increasingly essential yet challenging in ubiquitous behavioral applications and problem-solving techniques. They inevitably and undoubtedly constitute new computing opportunities and technological innovations, and thus we refer to them as coupled behavior informatics, which is an important branch of behavior computing and analytics.4 Coupled behavior informatics consists of methodologies, techniques, and practical tools for exploring human, organizational, artificial/virtual, qualitative, and quantitative behaviors, their interactions and relationships, the formation and decomposition of behavior-oriented groups, and collective intelligence. Here, we present the limitations of current research, and explore the needs, opportunities, challenges, prospects, and trends of coupled behavior informatics in terms of coupled behavior representation and modeling as well as analysis and reasoning
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