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AI Reasoning Methods for Robotics

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Springer Handbook of Robotics

Abstract

Artificial 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 differ substantially from that in knowledge-based pure software systems, where most of the named successes have been registered.

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. Then, some robotics-related particularities are addressed specially: issues in logic-based high-level robot control, fuzzy logics, and reasoning under time constraints. Two generic applications of reasoning are then described in some detail: action planning and learning.

General reasoning is currently not a standard feature onboard autonomous mobile robots. Beyond sketching the state of the art in robotics-related AI reasoning, this Chapter points to the involved research problems that remain to be solved towards that end.

The Chapter first reviews knowledge representation and deduction in general (Sect. 9.1), and then goes into some detail regarding reasoning issues that are considered particularly relevant for applications in robots (Sect. 9.2). Having presented reasoning methods, we then enter the field of generic reasoning applications, namely, action planning (Sect. 9.3) and machine learning (Sect. 9.4). Section 9.5 concludes.

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Abbreviations

AI:

artificial intelligence

BN:

Bayes network

DBNs:

dynamic Bayesian networks

DL:

description logics

EM:

expectation maximization

FOPL:

first-order predicate logic

HTN:

hierarchical task network

ILP:

inductive logic programming

KR:

knowledge representation

MDP:

Markovian decision process

ML:

machine learning

ML:

maximum likelihood

NASA:

National Aeronautics and Space Agency

NN:

neural networks

PI:

policy iteration

POMDP:

partially observable MDP

PRS:

procedural reasoning system

RL:

reinforcement learning

SHOP:

simple hierarchical ordered planner

SIPE:

system for interactive planning and execution monitoring

VI:

value iteration

WWW:

world wide web

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Author information

Authors and Affiliations

  1. Institut für Informatik, Universität Osnabrück, Albrechtstr. 28, 54076, Osnabrück, Germany

    Joachim Hertzberg Prof

  2. LAAS-CNRS, 7 Avenue du Colonel Roche, 31077, Toulouse, France

    Raja Chatila Prof

Authors
  1. Joachim Hertzberg Prof
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  2. Raja Chatila Prof
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Corresponding authors

Correspondence to Joachim Hertzberg Prof or Raja Chatila Prof .

Editor information

Editors and Affiliations

  1. PRISMA Lab, Dipartimento di Informatica e Sistemistica, Universitá degli Studi di Napoli Federico II, 80125, Napoli, Italy, Via Claudio 21

    Bruno Siciliano Prof.

  2. Artificial Intelligence Laboratory, Department of Computer Science, Stanford University, 94305-9010, Stanford, CA, USA

    Oussama Khatib Prof.

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© 2008 Springer-Verlag

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Hertzberg, J., Chatila, R. (2008). AI Reasoning Methods for Robotics. In: Siciliano, B., Khatib, O. (eds) Springer Handbook of Robotics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30301-5_10

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  • DOI: https://doi.org/10.1007/978-3-540-30301-5_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23957-4

  • Online ISBN: 978-3-540-30301-5

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