Plan-Based Control of Robotic Agents

Michael Beetz

ISBN 10: 3540003355 / ISBN 13: 9783540003359

Editore: Springer Berlin Heidelberg, Springer Berlin Heidelberg Dez 2002, 2002

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This item is printed on demand - Print on Demand Titel. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 208 pp. Englisch.

Codice articolo 9783540003359

Dati bibliografici

• Titolo: Plan-Based Control of Robotic Agents
• Editore: Springer Berlin Heidelberg, Springer Berlin Heidelberg Dez 2002
• Data di pubblicazione: 2002
• Lingua: Inglese
• ISBN 10: 3540003355
• ISBN 13: 9783540003359
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 12 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 12 millimetri di profondità
• Peso: 324 grammi

Descrizione articolo

Riassunto

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.

Contenuti

Overview of the Control System.- Plan Representation for Robotic Agents.- Probabilistic Hybrid Action Models.- Learning Structured Reactive Navigation Plans.- Plan-Based Robotic Agents.- Conclusions.