UID:
almahu_9948621548802882
Format:
XI, 194 p.
,
online resource.
Edition:
1st ed. 2002.
ISBN:
9783540363811
Series Statement:
Lecture Notes in Artificial Intelligence ; 2554
Content:
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.
Note:
Overview of the Control System -- Plan Representation for Robotic Agents -- Probabilistic Hybrid Action Models -- Learning Structured Reactive Navigation Plans -- Plan-Based Robotic Agents -- Conclusions.
In:
Springer Nature eBook
Additional Edition:
Printed edition: ISBN 9783540003359
Additional Edition:
Printed edition: ISBN 9783662195024
Language:
English
DOI:
10.1007/3-540-36381-5
URL:
https://doi.org/10.1007/3-540-36381-5
