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Introduction to Programming with Fortran: with coverage of Fortran 90, 95, 2003 and 77
Introduction to Programming with Fortran - with coverage of Fortran 90, 95, 2003 and 77
MATLAB - Modelling, Programming and Simulations
-MATLAB: Modelling, Programming and Simulations- ed. by Emilson Pereira Leite (Repost)
Introduction to Programming with Fortran: with coverage of Fortran 90, 95, 2003 and 77 [Repost]
An Introduction to Programming and Numerical Methods in MATLAB [Repost]
"MATLAB: Modelling, Programming and Simulations" ed. by Emilson Pereira Leite (Repost)
Programming Social Applications: Building Viral Experiences with OpenSocial, OAuth, OpenID, and Distributed Web... (repost)
MATLAB Programming for Engineers (2nd edition) [Repost]
Sriranga Veeraraghavan, "Sams Teach Yourself Shell Programming in 24 Hours" (Repost)
Programming Computer Vision with Python - Tools and algorithms for analyzing images
Matlab: A Practical Introduction to Programming and Problem Solving (2nd edition) [Repost]
ECOOP 2011 - Object-Oriented Programming
Mobile JavaScript Application Development - Bringing Web Programming to Mobile Devices [Paperback]
Concurrent Programming on Windows (repost)
Expert WSS 3.0 and MOSS 2007 Programming (repost)
Embedded Software Design and Programming of Multiprocessor System-on-Chip (repost)
OpenCV 2 Computer Vision Application Programming Cookbook
Programming Computer Vision with Python: Tools and algorithms for analyzing images
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Programming Plan-Based Control of Robotic Agents: Improving the Capabilities of Autonomous Robots
Posted on 2010-04-15
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More Michael Beetz "Plan-Based Control of Robotic Agents: Improving the Capabilities of Autonomous Robots " Robotic agents, such as autonomous office couriers or robot tourguides, must be both reliable and efficient. Thus, they have to flexibly interleave their tasks, exploit opportunities, quickly plan their course of action, and, if necessary, revise their intended activities. This book makes three major contributions to improving the capabilities of robotic agents: - first, a plan representation method is introduced which allows for specifying flexible and reliable behavior - second, probabilistic hybrid action models are presented as a realistic causal model for predicting the behavior generated by modern concurrent percept-driven robot plans - third, the system XFRMLEARN capable of learning structured symbolic navigation plans is described in detail.
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