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001 978-3-319-65596-3
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008 230530s2023 sz | s |||| 0|eng d
020 _a9783319655963
_9978-3-319-65596-3
024 7 _a10.1007/978-3-319-65596-3
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aEdelkamp, Stefan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aAlgorithmic Intelligence
_h[electronic resource] :
_bTowards an Algorithmic Foundation for Artificial Intelligence /
_cby Stefan Edelkamp.
250 _a1st ed. 2023.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2023.
300 _aXXV, 467 p. 173 illus., 90 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aPreface -- Towards a Characterization -- Part I, Basics -- 1. Programming Primer -- 2. Shortest Paths -- 3. Sorting -- 4. Deep Learning -- 5. Monte-Carlo Search -- Part II, Big Data -- 6. Graph data -- 7. Multimedia Data -- 8. Network Data -- 9. Image Data -- 10. Navigation Data -- Part III, Research Areas -- 11. Machine Learning -- 12. Problem Solving -- 13. Card Game Playing -- 14. Action Planning -- 15. General Game Playing -- 16. Multiagent Systems -- 17. Recommendation and Configuration Part IV, Applications -- 18. Adversarial Planning -- 19. Model Checking -- 20. Computational Biology -- 21. Logistics -- 22. Additive Manufacturing -- 23. Robot Motion Planning -- 24. Industrial Production -- 25. Further Application Areas. - Index and References.
520 _aIn this book the author argues that the basis of what we consider computer intelligence has algorithmic roots, and he presents this with a holistic view, showing examples and explaining approaches that encompass theoretical computer science and machine learning via engineered algorithmic solutions. Part I of the book introduces the basics. The author starts with a hands-on programming primer for solving combinatorial problems, with an emphasis on recursive solutions. The other chapters in the first part of the book explain shortest paths, sorting, deep learning, and Monte Carlo search. A key function of computational tools is processing Big Data efficiently, and the chapters in Part II of the book examine traditional graph problems such as finding cliques, colorings, independent sets, vertex covers, and hitting sets, and the subsequent chapters cover multimedia, network, image, and navigation data. The highly topical research areas detailed in Part III are machine learning, problem solving, action planning, general game playing, multiagent systems, and recommendation and configuration. Finally, in Part IV the author uses application areas such as model checking, computational biology, logistics, additive manufacturing, robot motion planning, and industrial production to explain how the techniques described may be exploited in modern settings. The book is supported with a comprehensive index and references, and it will be of value to researchers, practitioners, and students in the areas of artificial intelligence and computational intelligence.
650 0 _aArtificial intelligence.
650 0 _aData mining.
650 0 _aControl engineering.
650 0 _aRobotics.
650 0 _aAutomation.
650 0 _aBusiness information services.
650 0 _aBusiness logistics.
650 1 4 _aArtificial Intelligence.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aControl, Robotics, Automation.
650 2 4 _aIT in Business.
650 2 4 _aLogistics.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319655956
776 0 8 _iPrinted edition:
_z9783319655970
856 4 0 _uhttps://doi.org/10.1007/978-3-319-65596-3
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c178721
_d178721