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AI Reasoning Methods for Robotics

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Springer Handbook of Robotics

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Abstract

Artificial intelligence (GlossaryTerm

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 differ substantially from that in knowledge-based pure software 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 first 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.

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Abbreviations

2-D:

two-dimensional

AAAI:

Association for the Advancement of Artificial Intelligence

AI:

artificial intelligence

BN:

Bayesian network

CDC:

cardinal direction calculus

CSP:

constraint satisfaction problem

DBN:

dynamic Bayesian network

DC:

disconnected

DL:

description logic

DPLL:

Davis–Putnam algorithm

ECAI:

European Conference on Artificial Intelligence

EC:

externally connected

FF:

fast forward

FOPL:

first-order predicate logic

HTN:

hierarchical task network

IA:

interval algebra

ICAPS:

International Conference on Automated Planning and Scheduling

IJCAI:

International Joint Conference on Artificial Intelligence

IPC:

international AI planning competition

KR:

knowledge representation

LTL:

linear temporal logic

MDP:

Markov decision process

NTPP:

nontangential proper part

OUR-K:

ontology based unified robot knowledge

OWL:

web ontology language

PA:

point algebra

PI:

policy iteration

POMDP:

partially observable Markov decision process

PO:

partially overlapping

POP:

partial-order planning

PRM:

probabilistic roadmap

RA:

rectangle algebra

RCC:

region connection calculus

SAT:

International Conference on Theory and Applications of Satisfiability Testing

SMT:

satisfiabiliy modulo theory

STP:

simple temporal problem

TAL:

temporal action logic

TCSP:

temporal constraint satisfaction problem

TL:

temporal logic

TPP:

tangential proper part

VI:

value iteration

W3C:

WWW consortium

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Author information

Authors and Affiliations

  1. Institute for Artificial Intelligence, University Bremen, Am Fallturm 1, 28359, Bremen, Germany

    Michael Beetz

  2. Institute of Intelligent Systems and Robotics, University Pierre et Marie Curie, 4 Place Jussieu, 75005, Paris, France

    Raja Chatila

  3. Institute for Computer Science, Osnabrück University, Albrechtstrasse 28, 54076, Osnabrück, Germany

    Joachim Hertzberg

  4. School of Science and Technology, University of Örebro, Fakultetsgatan 1, 70182, Örebro, Sweden

    Federico Pecora

Authors
  1. Michael Beetz
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  2. Raja Chatila
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  3. Joachim Hertzberg
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  4. Federico Pecora
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Correspondence to Michael Beetz .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Naples Federico II, Via Claudio 21, 80125, Naples, Italy

    Bruno Siciliano

  2. Department of Computer Sciences, Artificial Intelligence Laboratory, Stanford University, 450 Serra Mall, CA 94305, Stanford, USA

    Oussama Khatib

Video-References

Video-References

:

SHAKEY: Experimentation in robot learning and planning (1969) available from http://handbookofrobotics.org/view-chapter/14/videodetails/704

:

From knowledge grounding to dialogue processing available from http://handbookofrobotics.org/view-chapter/14/videodetails/705

:

RoboEarth final demonstrator available from http://handbookofrobotics.org/view-chapter/14/videodetails/706

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Beetz, M., Chatila, R., Hertzberg, J., Pecora, F. (2016). AI Reasoning Methods for Robotics. In: Siciliano, B., Khatib, O. (eds) Springer Handbook of Robotics. Springer Handbooks. Springer, Cham. https://doi.org/10.1007/978-3-319-32552-1_14

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