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181201 ||| eng |
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|a 9783540363811
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|a Beetz, Michael
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|a Plan-Based Control of Robotic Agents
|h Elektronische Ressource
|b Improving the Capabilities of Autonomous Robots
|c by Michael Beetz
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| 250 |
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|a 1st ed. 2002
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| 260 |
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|a Berlin, Heidelberg
|b Springer Berlin Heidelberg
|c 2002, 2002
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| 300 |
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|a XI, 194 p
|b online resource
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|a Overview of the Control System -- Plan Representation for Robotic Agents -- Probabilistic Hybrid Action Models -- Learning Structured Reactive Navigation Plans -- Plan-Based Robotic Agents -- Conclusions
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| 653 |
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|a Computer Communication Networks
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| 653 |
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|a Computer science
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| 653 |
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|a Control, Robotics, Automation
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| 653 |
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|a Computer Science
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Computer networks
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| 653 |
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|a Control engineering
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Robotics
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| 653 |
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|a Special Purpose and Application-Based Systems
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| 653 |
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|a Automation
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| 653 |
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|a Computers, Special purpose
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|a eng
|2 ISO 639-2
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| 989 |
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|b SBA
|a Springer Book Archives -2004
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|a Lecture Notes in Artificial Intelligence
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|a 10.1007/3-540-36381-5
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| 856 |
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|u https://doi.org/10.1007/3-540-36381-5?nosfx=y
|x Verlag
|3 Volltext
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|a 629.8
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| 520 |
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|a 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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