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008			230913s2023 sz | o |||| 0|eng d
020			_a9783031391798
024	7		_a10.1007/978-3-031-39179-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2023 EB
100	1		_aShakarian, Paulo _eautor _4http://id.loc.gov/vocabulary/relators/aut _9678575
245	0	0	_aNeuro Symbolic Reasoning and Learning _cby Paulo Shakarian, Chitta Baral, Gerardo I Simari, Bowen Xi, Lahari Pokala
250			_a1st ed 2023
264		1	_aCham _bSpringer Nature Switzerland _c2023
300			_a1 recurso en línea
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aSpringerBriefs in Computer Science _x2191-5776
505	0		_aChapter1 New Ideas in Neuro Symbolic Reasoning and Learning -- Chapter2 Brief Introduction to Propositional Logic and Predicate Calculus -- Chapter3 Fuzzy and Annotated Logic for Neuro Symbolic Artificial Intelligence -- Chapter4 LTN: Logic Tensor Networks -- Chapter5 Neuro Symbolic Reasoning with Ontological Networks -- Chapter6 LNN: Logical Neural Networks -- Chapter7 NeurASP -- Chapter8 Neuro Symbolic Learning with Differentiable Inductive Logic Programming -- Chapter9 Understanding SATNet: Constraint Learning and Symbol Grounding -- Chapter10 Neuro Symbolic AI for Sequential Decision Making -- Chapter11 Neuro Symbolic Applications.
520			_aThis book provides a broad overview of the key results and frameworks for various NSAI tasks as well as discussing important application areas. This book also covers neuro symbolic reasoning frameworks such as LNN, LTN, and NeurASP and learning frameworks. This would include differential inductive logic programming, constraint learning and deep symbolic policy learning. Additionally, application areas such a visual question answering and natural language processing are discussed as well as topics such as verification of neural networks and symbol grounding. Detailed algorithmic descriptions, example logic programs, and an online supplement that includes instructional videos and slides provide thorough but concise coverage of this important area of AI. Neuro symbolic artificial intelligence (NSAI) encompasses the combination of deep neural networks with symbolic logic for reasoning and learning tasks. NSAI frameworks are now capable of embedding prior knowledge in deep learning architectures, guiding the learning process with logical constraints, providing symbolic explainability, and using gradient-based approaches to learn logical statements. Several approaches are seeing usage in various application areas. This book is designed for researchers and advanced-level students trying to understand the current landscape of NSAI research as well as those looking to apply NSAI research in areas such as natural language processing and visual question answering. Practitioners who specialize in employing machine learning and AI systems for operational use will find this book useful as well.
988			_aSpringer_Computer_2023
650		7	_2embne _9166090 _aAprendizaje automático
700	1		_9689663 _aBaral, Chitta _eautor
700	1		_9689664 _aSimari, Gerardo I. _eautor
700	1		_9689665 _aXi, Bowen _eautor
700	1		_9689666 _aPokala, Lahari _eautor
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-39179-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2024 _dz _eb _zSI