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007			cr nn 008mamaa
008			220601s2019 sz | s |||| 0|eng d
020			_a9783031015847
024	7		_a10.1007/978-3-031-01584-7 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ335 _b2019 EB
100	1		_aHaslum, Patrik _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686636
245	1	3	_aAn Introduction to the Planning Domain Definition Language _cby Patrik Haslum, Nir Lipovetzky, Daniele Magazzeni, Christian Muise.
250			_a1st edition 2019
264		1	_aCham _bSpringer International Publishing _c2019
300			_a1 recurso en línea (XVII, 169 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_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.
988			_aSynthesis Collection of Technology_2019
650		7	_2embne _aInteligencia artificial _9413115
650		7	_2embne _9165250 _aComplejidad computacional
700	1		_aLipovetzky, Nir _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686637
700	1		_aMagazzeni, Daniele _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686638
700	1		_aMuise, Christian _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
776	0	8	_iPrinted edition: _z9783031000294
776	0	8	_iPrinted edition: _z9783031004568
776	0	8	_iPrinted edition: _z9783031027123
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01584-7 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _esc _zSI