Modeling Human Behavior with Cross-Disciplinary Artificial Intelligence
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Beschreibung
This book presents cutting-edge research in cross-disciplinary artificial intelligence for modeling human behavior. Real-world interactions, such as autonomous driving, speech communication, human-computer interaction, and sports behavior, require the prediction, interpretation, and control of complex interactions between agents and their environments. Although deep learning has achieved remarkable progress, it still faces two major challenges in real-world behavior modeling: ensuring interpretability and reducing the need for exhaustive data collection.
To address these challenges, this book brings together data-driven methods, mathematical models, domain knowledge, and computational approaches for understanding the environment, individuality, movement, values, and their interrelationships in human behavior. Rather than treating each application area separately, the volume highlights common principles and methodological connections across different domains.
The book consists of five parts. Part I introduces behavior signal processing from a cross-disciplinary perspective. Parts II, III, IV, and V review recent advances in vehicle signal processing, audio, speech, and language processing, human-computer interaction, and sports behavior modeling, respectively. The book will be useful for researchers, graduate students, and professionals interested in artificial intelligence, machine learning, behavior modeling, real-world data analysis, and interpretable prediction and control.
This book presents cutting-edge research in cross-disciplinary artificial intelligence for modeling human behavior. Real-world interactions, such as autonomous driving, speech communication, human-computer interaction, and sports behavior, require the prediction, interpretation, and control of complex interactions between agents and their environments. Although deep learning has achieved remarkable progress, it still faces two major challenges in real-world behavior modeling: ensuring interpretability and reducing the need for exhaustive data collection.
To address these challenges, this book brings together data-driven methods, mathematical models, domain knowledge, and computational approaches for understanding the environment, individuality, movement, values, and their interrelationships in human behavior. Rather than treating each application area separately, the volume highlights common principles and methodological connections across different domains.
The book consists of five parts. Part I introduces behavior signal processing from a cross-disciplinary perspective. Parts II, III, IV, and V review recent advances in vehicle signal processing, audio, speech, and language processing, human-computer interaction, and sports behavior modeling, respectively. The book will be useful for researchers, graduate students, and professionals interested in artificial intelligence, machine learning, behavior modeling, real-world data analysis, and interpretable prediction and control.
Offers a novel perspective by integrating AI with various fields such as driving, sports, sound, speech, and HCI Emphasizes real-world interactions and practical applications Incorporates domain knowledge into AI models
Autor*in
Kazuya Takeda
Themen in »Modeling Human Behavior with Cross-Disciplinary Artificial Intelligence«
Machine Learning Deep Learning Behavior signal processing Real-world data Prediction and control Autonomous vehicle Sound Speech Sports Animal Human Computer Interaction