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.