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 majo...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Beetz, Michael (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2002.
Έκδοση:1st ed. 2002.
Σειρά:Lecture Notes in Artificial Intelligence ; 2554
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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245 1 0 |a Plan-Based Control of Robotic Agents  |h [electronic resource] :  |b Improving the Capabilities of Autonomous Robots /  |c by Michael Beetz. 
250 |a 1st ed. 2002. 
264 1 |a Berlin, Heidelberg :  |b Springer Berlin Heidelberg :  |b Imprint: Springer,  |c 2002. 
300 |a XI, 194 p.  |b online resource. 
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490 1 |a Lecture Notes in Artificial Intelligence ;  |v 2554 
505 0 |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. 
520 |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. 
650 0 |a Robotics. 
650 0 |a Automation. 
650 0 |a Artificial intelligence. 
650 0 |a Computer science. 
650 0 |a Computer communication systems. 
650 0 |a Special purpose computers. 
650 0 |a Control engineering. 
650 0 |a Mechatronics. 
650 1 4 |a Robotics and Automation.  |0 http://scigraph.springernature.com/things/product-market-codes/T19020 
650 2 4 |a Artificial Intelligence.  |0 http://scigraph.springernature.com/things/product-market-codes/I21000 
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650 2 4 |a Computer Communication Networks.  |0 http://scigraph.springernature.com/things/product-market-codes/I13022 
650 2 4 |a Special Purpose and Application-Based Systems.  |0 http://scigraph.springernature.com/things/product-market-codes/I13030 
650 2 4 |a Control, Robotics, Mechatronics.  |0 http://scigraph.springernature.com/things/product-market-codes/T19000 
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