Human Behavior Modeling: The Necessity of Narrative

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

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.131.635 ◽

2011 ◽

Vol 131 (3) ◽

pp. 635-643 ◽

Cited By ~ 6

Author(s):

Kohjiro Hashimoto ◽

Kae Doki ◽

Shinji Doki ◽

Shigeru Okuma ◽

Akihiro Torii

Keyword(s):

Human Behavior ◽

Modeling Method ◽

Behavior Modeling ◽

Human Behavior Modeling

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Exploiting Social Networks for Large-Scale Human Behavior Modeling

IEEE Pervasive Computing ◽

10.1109/mprv.2011.70 ◽

2011 ◽

Vol 10 (4) ◽

pp. 45-53 ◽

Cited By ~ 29

Author(s):

Nicholas D. Lane ◽

Ye Xu ◽

Hong Lu ◽

Andrew T. Campbell ◽

Tanzeem Choudhury ◽

...

Keyword(s):

Social Networks ◽

Human Behavior ◽

Large Scale ◽

Behavior Modeling ◽

Human Behavior Modeling

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Integrating Human Behavior Modeling and Data Mining Techniques to Predict Human Errors in Numerical Typing

IEEE Transactions on Human-Machine Systems ◽

10.1109/thms.2014.2357178 ◽

2015 ◽

Vol 45 (1) ◽

pp. 39-50 ◽

Cited By ~ 14

Author(s):

Cheng-Jhe Lin ◽

Changxu Wu ◽

Wanpracha A. Chaovalitwongse

Keyword(s):

Data Mining ◽

Human Behavior ◽

Behavior Modeling ◽

Human Errors ◽

Data Mining Techniques ◽

Human Behavior Modeling

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A Unified Model for Human Behavior Modeling Using a Hierarchy with a Variable Number of States

2014 22nd International Conference on Pattern Recognition ◽

10.1109/icpr.2014.653 ◽

2014 ◽

Cited By ~ 4

Author(s):

Hande Alemdar ◽

T.L.M van Kasteren ◽

M.E. Niessen ◽

A. Merentitis ◽

Cem Ersoy

Keyword(s):

Human Behavior ◽

Variable Number ◽

Unified Model ◽

Behavior Modeling ◽

Human Behavior Modeling

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Reports of the AAAI 2009 Spring Symposia

AI Magazine ◽

10.1609/aimag.v30i3.2253 ◽

2009 ◽

Vol 30 (3) ◽

pp. 89

Author(s):

Jie Bao ◽

Uldis Bojars ◽

Ranzeem Choudhury ◽

Li Ding ◽

Mark Greaves ◽

...

Keyword(s):

Human Behavior ◽

Predictive Analytics ◽

Temporal Reasoning ◽

Behavior Modeling ◽

Event Processing ◽

Human Behavior Modeling ◽

Reasoning Systems ◽

Learning By Reading ◽

Human Teachers ◽

Social Semantic Web

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, was pleased to present the 2009 Spring Symposium Series, held Monday through Wednesday, March 23–25, 2009 at Stanford University. The titles of the nine symposia were Agents that Learn from Human Teachers, Benchmarking of Qualitative Spatial and Temporal Reasoning Systems, Experimental Design for Real-World Systems, Human Behavior Modeling, Intelligent Event Processing, Intelligent Narrative Technologies II, Learning by Reading and Learning to Read, Social Semantic Web: Where Web 2.0 Meets Web 3.0, and Technosocial Predictive Analytics. The goal of the Agents that Learn from Human Teachers was to investigate how we can enable software and robotics agents to learn from real-time interaction with an everyday human partner. The aim of the Benchmarking of Qualitative Spatial and Temporal Reasoning Systems symposium was to initiate the development of a problem repository in the field of qualitative spatial and temporal reasoning and identify a graded set of challenges for future midterm and long-term research. The Experimental Design symposium discussed the challenges of evaluating AI systems. The Human Behavior Modeling symposium explored reasoning methods for understanding various aspects of human behavior, especially in the context of designing intelligent systems that interact with humans. The Intelligent Event Processing symposium discussed the need for more AI-based approaches in event processing and defined a kind of research agenda for the field, coined as intelligent complex event processing (iCEP). The Intelligent Narrative Technologies II AAAI symposium discussed innovations, progress, and novel techniques in the research domain. The Learning by Reading and Learning to Read symposium explored two aspects of making natural language texts semantically accessible to, and processable by, machines. The Social Semantic Web symposium focused on the real-world grand challenges in this area. Finally, the Technosocial Predictive Analytics symposium explored new methods for anticipatory analytical thinking that provide decision advantage through the integration of human and physical models.

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