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020 _a9783031391798
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024 7 _a10.1007/978-3-031-39179-8
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
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072 7 _aCOM004000
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082 0 4 _a006.3
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100 1 _aShakarian, Paulo.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aNeuro Symbolic Reasoning and Learning
_h[electronic resource] /
_cby Paulo Shakarian, Chitta Baral, Gerardo I. Simari, Bowen Xi, Lahari Pokala.
250 _a1st ed. 2023.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2023.
300 _aXII, 119 p. 18 illus., 10 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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 priorknowledge 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.
650 0 _aArtificial intelligence.
650 0 _aMachine learning.
650 1 4 _aArtificial Intelligence.
650 2 4 _aMachine Learning.
700 1 _aBaral, Chitta.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aSimari, Gerardo I.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aXi, Bowen.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aPokala, Lahari.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031391781
776 0 8 _iPrinted edition:
_z9783031391804
830 0 _aSpringerBriefs in Computer Science,
_x2191-5776
856 4 0 _uhttps://doi.org/10.1007/978-3-031-39179-8
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
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