Compendium of Neurosymbolic Artificial Intelligence
Saved in:
| Title: | Compendium of Neurosymbolic Artificial Intelligence |
|---|---|
| Description: | If only it were possible to develop automated and trainable neural systems that could justify their behavior in a way that could be interpreted by humans like a symbolic system. The field of Neurosymbolic AI aims to combine two disparate approaches to AI; symbolic reasoning and neural or connectionist approaches such as Deep Learning. The quest to unite these two types of AI has led to the development of many innovative techniques which extend the boundaries of both disciplines. This book, Compendium of Neurosymbolic Artificial Intelligence, presents 30 invited papers which explore various approaches to defining and developing a successful system to combine these two methods. Each strategy has clear advantages and disadvantages, with the aim of most being to find some useful middle ground between the rigid transparency of symbolic systems and the more flexible yet highly opaque neural applications. The papers are organized by theme, with the first four being overviews or surveys of the field. These are followed by papers covering neurosymbolic reasoning; neurosymbolic architectures; various aspects of Deep Learning; and finally two chapters on natural language processing. All papers were reviewed internally before publication. The book is intended to follow and extend the work of the previous book, Neuro-symbolic artificial intelligence: The state of the art (IOS Press; 2021) which laid out the breadth of the field at that time. Neurosymbolic AI is a young field which is still being actively defined and explored, and this book will be of interest to those working in AI research and development. |
| Authors: | Md Kamruzzaman Sarker, Aaron Eberhart |
| Resource Type: | eBook. |
| Subjects: | Neural networks (Computer science), Artificial intelligence |
| Categories: | COMPUTERS / Artificial Intelligence / General |
| Database: | eBook Collection (EBSCOhost) |
| FullText | Links: – Type: ebook-pdf Text: Availability: 0 |
|---|---|
| Header | DbId: nlebk DbLabel: eBook Collection (EBSCOhost) An: 3679988 RelevancyScore: 1116 AccessLevel: 6 PubType: eBook PubTypeId: ebook PreciseRelevancyScore: 1116.28857421875 |
| IllustrationInfo | |
| ImageInfo | – Size: thumb Target: https://rps2images.ebscohost.com/rpsweb/othumb?id=NL$3679988$PDF&s=r – Size: medium Target: https://rps2images.ebscohost.com/rpsweb/othumb?id=NL$3679988$PDF&s=d |
| Items | – Name: Title Label: Title Group: Ti Data: Compendium of Neurosymbolic Artificial Intelligence – Name: Abstract Label: Description Group: Ab Data: If only it were possible to develop automated and trainable neural systems that could justify their behavior in a way that could be interpreted by humans like a symbolic system. The field of Neurosymbolic AI aims to combine two disparate approaches to AI; symbolic reasoning and neural or connectionist approaches such as Deep Learning. The quest to unite these two types of AI has led to the development of many innovative techniques which extend the boundaries of both disciplines. This book, Compendium of Neurosymbolic Artificial Intelligence, presents 30 invited papers which explore various approaches to defining and developing a successful system to combine these two methods. Each strategy has clear advantages and disadvantages, with the aim of most being to find some useful middle ground between the rigid transparency of symbolic systems and the more flexible yet highly opaque neural applications. The papers are organized by theme, with the first four being overviews or surveys of the field. These are followed by papers covering neurosymbolic reasoning; neurosymbolic architectures; various aspects of Deep Learning; and finally two chapters on natural language processing. All papers were reviewed internally before publication. The book is intended to follow and extend the work of the previous book, Neuro-symbolic artificial intelligence: The state of the art (IOS Press; 2021) which laid out the breadth of the field at that time. Neurosymbolic AI is a young field which is still being actively defined and explored, and this book will be of interest to those working in AI research and development. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Md+Kamruzzaman+Sarker%22">Md Kamruzzaman Sarker</searchLink><br /><searchLink fieldCode="AR" term="%22Aaron+Eberhart%22">Aaron Eberhart</searchLink> – Name: TypePub Label: Resource Type Group: TypPub Data: eBook. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Neural+networks+%28Computer+science%29%22">Neural networks (Computer science)</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink> – Name: SubjectBISAC Label: Categories Group: Su Data: <searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Artificial+Intelligence+%2F+General%22">COMPUTERS / Artificial Intelligence / General</searchLink> |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=3679988 |
| RecordInfo | BibRecord: BibEntity: Classifications: – Code: 006.3 Scheme: ddc Type: prePub Languages: – Code: eng Text: English Subjects: – SubjectFull: Neural networks (Computer science) Type: general – SubjectFull: Artificial intelligence Type: general Titles: – TitleFull: Compendium of Neurosymbolic Artificial Intelligence Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Md Kamruzzaman Sarker – PersonEntity: Name: NameFull: Aaron Eberhart – PersonEntity: Name: NameFull: Md Kamruzzaman Sarker – PersonEntity: Name: NameFull: Aaron Eberhart IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2023 – D: 03 M: 10 Type: profile Y: 2023 Identifiers: – Type: isbn-print Value: 9781643684062 – Type: isbn-electronic Value: 9781643684079 Numbering: – Type: volume Value: 00369 Titles: – TitleFull: Compendium of Neurosymbolic Artificial Intelligence Type: main |
| ResultId | 1 |
