LEADER 04297nam 22006375 450 001 9910744508303321 005 20251009082300.0 010 $z9783031391798$b(print) 010 $z3031391799$b(print) 010 $a9783031391798 010 $a3031391799 024 7 $a10.1007/978-3-031-39179-8 035 $a(MiAaPQ)EBC30742315 035 $a(Au-PeEL)EBL30742315 035 $a(OCoLC)1397575687 035 $a(DE-He213)978-3-031-39179-8 035 $a(PPN)272736333 035 $a(CKB)28222999100041 035 $a(EXLCZ)9928222999100041 100 $a20230913d2023 u| 0 101 0 $aeng 135 $aur|n#|||a|||a 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNeuro Symbolic Reasoning and Learning /$fby Paulo Shakarian, Chitta Baral, Gerardo I. Simari, Bowen Xi, Lahari Pokala 205 $a1st ed. 2023. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2023. 215 $a1 online resource (xii, 119 pages) $cillustrations 225 1 $aSpringerBriefs in Computer Science,$x2191-5776 311 08$aPrint version: Shakarian, Paulo Neuro Symbolic Reasoning and Learning Cham : Springer,c2023 9783031391781 320 $aIncludes bibliographical references. 327 $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. 330 $aThis book provides a broad overview of the key results and frameworks for various NSAI tasks as well as discussing important application areas. This book also covers neuro symbolic reasoning frameworks such as LNN, LTN, and NeurASP and learning frameworks. This would include differential inductive logic programming, constraint learning and deep symbolic policy learning. Additionally, application areas such a visual question answering and natural language processing are discussed as well as topics such as verification of neural networks and symbol grounding. Detailed algorithmic descriptions, example logic programs, and an online supplement that includes instructional videos and slides provide thorough but concise coverage of this important area of AI. Neuro symbolic artificial intelligence (NSAI) encompasses the combination of deep neural networks with symbolic logic for reasoning and learning tasks. NSAI frameworks are now capable of embedding priorknowledge in deep learning architectures, guiding the learning process with logical constraints, providing symbolic explainability, and using gradient-based approaches to learn logical statements. Several approaches are seeing usage in various application areas. This book is designed for researchers and advanced-level students trying to understand the current landscape of NSAI research as well as those looking to apply NSAI research in areas such as natural language processing and visual question answering. Practitioners who specialize in employing machine learning and AI systems for operational use will find this book useful as well. 410 0$aSpringerBriefs in Computer Science,$x2191-5776 606 $aArtificial intelligence 606 $aMachine learning 606 $aArtificial Intelligence 606 $aMachine Learning 615 0$aArtificial intelligence. 615 0$aMachine learning. 615 14$aArtificial Intelligence. 615 24$aMachine Learning. 676 $a006.31 700 $aShakarian$b Paulo$0791399 702 $aBaral$b Chitta 702 $aSimari$b Gerardo I. 702 $aXi$b Bowen 702 $aPokala$b Lahari 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910744508303321 996 $aNeuro symbolic reasoning and learning$93659084 997 $aUNINA