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008			220601s2021 sz | s |||| 0|eng d
020			_a9783031015892
024	7		_a10.1007/978-3-031-01589-2 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA76.5915 _b2021 EB
100	1		_aMirsky, Reuth _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686641
245	1	0	_aIntroduction to Symbolic Plan and Goal Recognition _cby Reuth Mirsky, Sarah Keren, Christopher Geib
250			_a1st edition 2021
264		1	_aCham _bSpringer International Publishing _c2021
300			_a1 recurso en línea (XX, 100 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		_aPreface -- Acknowledgments -- Introduction -- Defining a Recognition Problem -- Implicit vs. Explicit Representation of Knowledge -- Improving a Recognizer -- Future Directions -- Bibliography -- Authors' Biographies.
520			_aPlan recognition, activity recognition, and goal recognition all involve making inferences about other actors based on observations of their interactions with the environment and other agents. This synergistic area of research combines, unites, and makes use of techniques and research from a wide range of areas including user modeling, machine vision, automated planning, intelligent user interfaces, human-computer interaction, autonomous and multi-agent systems, natural language understanding, and machine learning. It plays a crucial role in a wide variety of applications including assistive technology, software assistants, computer and network security, human-robot collaboration, natural language processing, video games, and many more. This wide range of applications and disciplines has produced a wealth of ideas, models, tools, and results in the recognition literature. However, it has also contributed to fragmentation in the field, with researchers publishing relevant results in a wide spectrum of journals and conferences. This book seeks to address this fragmentation by providing a high-level introduction and historical overview of the plan and goal recognition literature. It provides a description of the core elements that comprise these recognition problems and practical advice for modeling them. In particular, we define and distinguish the different recognition tasks. We formalize the major approaches to modeling these problems using a single motivating example. Finally, we describe a number of state-of-the-art systems and their extensions, future challenges, and some potential applications.
988			_aSynthesis Collection of Technology_2021
650		7	_2embne _9166090 _aAprendizaje automático
650		7	_2embne _aInteligencia artificial _9413115
650		7	_2embne _9155848 _aInteracción hombre-ordenador
700	1		_aKeren, Sarah _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686642
700	1		_aGeib, Christopher _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686643
776	0	8	_iPrinted edition: _z9783031000348
776	0	8	_iPrinted edition: _z9783031004612
776	0	8	_iPrinted edition: _z9783031027178
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01589-2 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2023 _dz _esc _zSI