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1. |
Record Nr. |
UNINA9910785235403321 |
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Autore |
Turner Chris |
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Titolo |
Tort law / Chris Turner |
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Pubbl/distr/stampa |
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London, : Hodder Education, 2010 |
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ISBN |
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1-282-91449-9 |
9781444110913 |
1444110918 |
9786612914492 |
1-4441-2789-6 |
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Edizione |
[4th ed.] |
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Descrizione fisica |
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1 online resource (136 p.) |
[vii], 131 p. ; ; 20 cm |
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Collana |
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Disciplina |
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Soggetti |
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Torts - Great Britain |
Law - Great Britain |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Includes index. |
Previous edition: 2008. |
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Nota di contenuto |
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Front Cover; Key Facts: Tort; Copyright Page; Contents; Preface; Chapter 1. The nature of tortious liability; Chapter 2. Negligence; Chapter 3. Occupier's liability; Chapte 4. Nuisance; Chapter 5. Strict liability; Chapter 6. Trepass to land; Chapter 7. Torts concerning goods; Chapter 8. Trespass to the person; Chapter 9. Torts affecting reputation; Chapter 10. Employment-related torts; Chapter 11. Remedies and limitation periods; Index |
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Sommario/riassunto |
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Key Facts is the essential revision series for anyone studying law, including LLB, ILEX and post-graduate conversion courses.The Key Facts series provides the simplest and most effective way for you to absorb and retain the essential facts needed to pass your exams effortlessly.Key features include:* Diagrams at the start of chapters to summarise the key points* Structured heading levels to allow for clear recall of the main facts* Charts and tables to break down more complex informationNew to these editions is an improved text design making the books ea |
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2. |
Record Nr. |
UNISA996550558403316 |
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Autore |
Shakarian Paulo |
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Titolo |
Neuro symbolic reasoning and learning / / Paulo Shakarian, Chitta Baral, Gerardo I. Simari, Bowen Xi, Lahari Pokala |
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Pubbl/distr/stampa |
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Cham : , : Springer, , [2023] |
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©2023 |
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ISBN |
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Descrizione fisica |
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1 online resource (xii, 119 pages) : illustrations |
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Collana |
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SpringerBriefs in computer science, , 2191-5776 |
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Disciplina |
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Soggetti |
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Artificial intelligence |
Machine learning |
Logic, Symbolic and mathematical |
Artificial Intelligence |
Machine Learning |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Chapter 1. New Ideas in Neuro Symbolic Reasoning and Learning -- Chapter 2. Brief Introduction to Propositional Logic and Predicate Calculus -- Chapter 3. Fuzzy and Annotated Logic for Neuro Symbolic Artificial Intelligence -- Chapter 4. LTN: Logic Tensor Networks -- Chapter 5. Neuro Symbolic Reasoning with Ontological Networks -- Chapter 6. LNN: Logical Neural Networks -- Chapter 7. NeurASP -- Chapter 8. Neuro Symbolic Learning with Differentiable Inductive Logic Programming -- Chapter 9. Understanding SATNet: Constraint Learning and Symbol Grounding -- Chapter 10. Neuro Symbolic AI for Sequential Decision Making -- Chapter 11. Neuro Symbolic Applications. |
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Sommario/riassunto |
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This 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 |
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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. |
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