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dc.contributor.editorSands, Timothy
dc.date.accessioned2021-04-20T16:16:45Z
dc.date.available2021-04-20T16:16:45Z
dc.date.issued2020
dc.identifierONIX_20210420_9781789841121_2880
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/67520
dc.description.abstractKirchhoff’s laws give a mathematical description of electromechanics. Similarly, translational motion mechanics obey Newton’s laws, while rotational motion mechanics comply with Euler’s moment equations, a set of three nonlinear, coupled differential equations. Nonlinearities complicate the mathematical treatment of the seemingly simple action of rotating, and these complications lead to a robust lineage of research culminating here with a text on the ability to make rigid bodies in rotation become self-aware, and even learn. This book is meant for basic scientifically inclined readers commencing with a first chapter on the basics of stochastic artificial intelligence to bridge readers to very advanced topics of deterministic artificial intelligence, espoused in the book with applications to both electromechanics (e.g. the forced van der Pol equation) and also motion mechanics (i.e. Euler’s moment equations). The reader will learn how to bestow self-awareness and express optimal learning methods for the self-aware object (e.g. robot) that require no tuning and no interaction with humans for autonomous operation. The topics learned from reading this text will prepare students and faculty to investigate interesting problems of mechanics. It is the fondest hope of the editor and authors that readers enjoy the book.
dc.languageEnglish
dc.subject.classificationbic Book Industry Communication::U Computing & information technology::UY Computer science::UYQ Artificial intelligenceen_US
dc.subject.otherArtificial intelligence
dc.titleDeterministic Artificial Intelligence
dc.typebook
oapen.identifier.doi10.5772/intechopen.81309
oapen.relation.isPublishedBy78a36484-2c0c-47cb-ad67-2b9f5cd4a8f6
oapen.relation.isbn9781789841121
oapen.relation.isbn9781789841114
oapen.relation.isbn9781838807283
oapen.imprintIntechOpen
oapen.pages178


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