1st Edition
Behavior Trees in Robotics and AI An Introduction
1. What are Behavior Trees? 2. How Behavior Trees Generalize and Relate to Earlier Ideas 3. Design principles 4. Extensions of Behavior Trees 5. Analysis of Efficiency, Safety, and Robustness 6. Formal Analysis of How Behavior Trees Generalize Earlier Ideas 7. Behavior Trees and Automated Planning 8. Behavior Trees and Machine Learning 9. Stochastic Behavior Trees 10. Concluding Remarks
Biography
Michele Colledanchise is currently a postdoctoral researcher in the iCub Facility at the Italian Institute of Technology, Genoa, Italy. He received his Ph.D. degree in computer science from the Royal Institute of Technology (KTH), Stockholm, Sweden, in 2017. In the spring of 2016, he visited the Control and Dynamical Systems, Californa Institute of Technology (Caltech), Pasadena, CA. His research interests include control systems, system architectures, and automated planning, with a strong focus on robotic applications.
Petter Ögren was born in Stockholm, Sweden, in 1974. He received theM.S. degree in engineering physics and the Ph.D. degree in applied mathematics from the Royal Institute of Technology (KTH), Stockholm, Sweden, in 1998 and 2003, respectively. In the fall of 2001, he visited the Mechanical Engineering Department, Princeton University, Princeton, NJ. From 2003 to 2012 he worked as a senior scientist and deputy research director in Autonomous Systems at the Swedish Defence Research Agency (FOI). He is currently an Associate Professor at the Robotics, Perception and Learning lab (RPL) at KTH. His research interests include robot control architectures and multi-agent coordination.
