000 04306nam a22005655i 4500
001 978-3-031-01584-7
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008 220601s2019 sz | s |||| 0|eng d
020 _a9783031015847
_9978-3-031-01584-7
024 7 _a10.1007/978-3-031-01584-7
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aHaslum, Patrik.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_984008
245 1 3 _aAn Introduction to the Planning Domain Definition Language
_h[electronic resource] /
_cby Patrik Haslum, Nir Lipovetzky, Daniele Magazzeni, Christian Muise.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aXVII, 169 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
505 0 _aPraise for An Introduction to the Planning Domain Definition Language -- Preface -- Introduction -- Discrete and Deterministic Planning -- More Expressive Classical Planning -- Numeric Planning -- Temporal Planning -- Planning with Hybrid Systems -- Conclusion -- Bibliography -- Authors' Biographies -- Index .
520 _aPlanning is the branch of Artificial Intelligence (AI) that seeks to automate reasoning about plans, most importantly the reasoning that goes into formulating a plan to achieve a given goal in a given situation. AI planning is model-based: a planning system takes as input a description (or model) of the initial situation, the actions available to change it, and the goal condition to output a plan composed of those actions that will accomplish the goal when executed from the initial situation. The Planning Domain Definition Language (PDDL) is a formal knowledge representation language designed to express planning models. Developed by the planning research community as a means of facilitating systems comparison, it has become a de-facto standard input language of many planning systems, although it is not the only modelling language for planning. Several variants of PDDL have emerged that capture planning problems of different natures and complexities, with a focus on deterministic problems. The purpose of this book is two-fold. First, we present a unified and current account of PDDL, covering the subsets of PDDL that express discrete, numeric, temporal, and hybrid planning. Second, we want to introduce readers to the art of modelling planning problems in this language, through educational examples that demonstrate how PDDL is used to model realistic planning problems. The book is intended for advanced students and researchers in AI who want to dive into the mechanics of AI planning, as well as those who want to be able to use AI planning systems without an in-depth explanation of the algorithms and implementation techniques they use.
650 0 _aArtificial intelligence.
_93407
650 0 _aMachine learning.
_91831
650 0 _aNeural networks (Computer science) .
_984010
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aMachine Learning.
_91831
650 2 4 _aMathematical Models of Cognitive Processes and Neural Networks.
_932913
700 1 _aLipovetzky, Nir.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_984013
700 1 _aMagazzeni, Daniele.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_984014
700 1 _aMuise, Christian.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_984015
710 2 _aSpringerLink (Online service)
_984019
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031000294
776 0 8 _iPrinted edition:
_z9783031004568
776 0 8 _iPrinted edition:
_z9783031027123
830 0 _aSynthesis Lectures on Artificial Intelligence and Machine Learning,
_x1939-4616
_984021
856 4 0 _uhttps://doi.org/10.1007/978-3-031-01584-7
912 _aZDB-2-SXSC
942 _cEBK
999 _c85601
_d85601