AI Reasoning Methods for Robotics

Michael Beetz, Raja Chatila, Joachim Hertzberg, Federico Pecora

In: Bruno Siciliano, Oussama Khatib (editor). Springer Handbook of Robotics. Chapter 14 Pages 329-356 ISBN 978-3-319-32550-7 Springer Berlin Heidelberg 2016.


Arti!cial intelligence (AI) reasoning technology involving, e.g., inference, planning, and learning, has a track record with a healthy number of successful applications. So can it be used as a toolbox of methods for autonomous mobile robots? Not necessarily, as reasoning on a mobile robot about its dynamic, partially known environment may dier substantially from that in knowledge-based pure so#ware systems, where most of the named successes have been registered. Moreover, recent knowledge about the robot’s environment cannot be given a priori, but needs to be updated from sensor data, involving challenging problems of symbol grounding and knowledge base change. This chapter sketches the main robotics-relevant topics of symbol-based AI reasoning. Basic methods of knowledge representation and inference are described in general, covering both logic- and probability-based approaches. The chapter first gives a motivation by example, to what extent symbolic reasoning has the potential of helping robots perform in the !rst place. Then (Sect. 14.2), we sketch the landscape of representation languages available for the endeavor. After that (Sect. 14.3), we present approaches and results for several types of practical, robotics-related reasoning tasks, with an emphasis on temporal and spatial reasoning. Plan-based robot control is described in some more detail in Sect. 14.4. Section 14.5 concludes.


Weitere Links

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz