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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