04611nam 22007335 450 991072893950332120230530164122.09783319655963(electronic bk.)978331965595610.1007/978-3-319-65596-3(MiAaPQ)EBC30555907(Au-PeEL)EBL30555907(OCoLC)1381095126(DE-He213)978-3-319-65596-3(BIP)060217650(PPN)270616411(CKB)26806715400041(EXLCZ)992680671540004120230530d2023 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierAlgorithmic Intelligence Towards an Algorithmic Foundation for Artificial Intelligence /by Stefan Edelkamp1st ed. 2023.Cham :Springer International Publishing :Imprint: Springer,2023.1 online resource (482 pages)Print version: Edelkamp, Stefan Algorithmic Intelligence Cham : Springer International Publishing AG,c2023 9783319655956 Preface -- 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.In 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.Artificial intelligenceData miningControl engineeringRoboticsAutomationBusiness information servicesBusiness logisticsArtificial IntelligenceData Mining and Knowledge DiscoveryControl, Robotics, AutomationIT in BusinessLogisticsScienceArtificial intelligence.Data mining.Control engineering.Robotics.Automation.Business information services.Business logistics.Artificial Intelligence.Data Mining and Knowledge Discovery.Control, Robotics, Automation.IT in Business.Logistics.006.3Edelkamp Stefan853901MiAaPQMiAaPQMiAaPQ9910728939503321Algorithmic Intelligence3389825UNINA