03246nam 22005655 450 991033763770332120200703061129.03-030-10546-610.1007/978-3-030-10546-4(CKB)4100000007522458(MiAaPQ)EBC5639444(DE-He213)978-3-030-10546-4(PPN)233800468(EXLCZ)99410000000752245820190117d2019 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierDeep Reinforcement Learning for Wireless Networks /by F. Richard Yu, Ying He1st ed. 2019.Cham :Springer International Publishing :Imprint: Springer,2019.1 online resource (78 pages)SpringerBriefs in Electrical and Computer Engineering,2191-81123-030-10545-8 This Springerbrief presents a deep reinforcement learning approach to wireless systems to improve system performance. Particularly, deep reinforcement learning approach is used in cache-enabled opportunistic interference alignment wireless networks and mobile social networks. Simulation results with different network parameters are presented to show the effectiveness of the proposed scheme. There is a phenomenal burst of research activities in artificial intelligence, deep reinforcement learning and wireless systems. Deep reinforcement learning has been successfully used to solve many practical problems. For example, Google DeepMind adopts this method on several artificial intelligent projects with big data (e.g., AlphaGo), and gets quite good results.. Graduate students in electrical and computer engineering, as well as computer science will find this brief useful as a study guide. Researchers, engineers, computer scientists, programmers, and policy makers will also find this brief to be a useful tool. .SpringerBriefs in Electrical and Computer Engineering,2191-8112Wireless communication systemsMobile communication systemsArtificial intelligenceElectrical engineeringWireless and Mobile Communicationhttps://scigraph.springernature.com/ontologies/product-market-codes/T24100Artificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Communications Engineering, Networkshttps://scigraph.springernature.com/ontologies/product-market-codes/T24035Wireless communication systems.Mobile communication systems.Artificial intelligence.Electrical engineering.Wireless and Mobile Communication.Artificial Intelligence.Communications Engineering, Networks.006.31006.31Yu F. Richardauthttp://id.loc.gov/vocabulary/relators/aut864694He Yingauthttp://id.loc.gov/vocabulary/relators/autBOOK9910337637703321Deep Reinforcement Learning for Wireless Networks1930060UNINA03540nam 22006732 450 991095606590332120151005020621.01-107-21746-61-283-37848-51-139-18903-497866133784841-139-18775-91-139-19034-21-139-18312-51-139-18544-60-511-97453-1(CKB)2550000000061433(EBL)807277(OCoLC)782876977(SSID)ssj0000570948(PQKBManifestationID)11353989(PQKBTitleCode)TC0000570948(PQKBWorkID)10611271(PQKB)11363016(UkCbUP)CR9780511974533(MiAaPQ)EBC807277(Au-PeEL)EBL807277(CaPaEBR)ebr10520695(CaONFJC)MIL337848(PPN)261363700(EXLCZ)99255000000006143320101011d2011|||| uy| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierLogical dynamics of information and interaction /Johan van Benthem1st ed.Cambridge :Cambridge University Press,2011.1 online resource (xi, 373 pages) digital, PDF file(s)Title from publisher's bibliographic system (viewed on 05 Oct 2015).1-107-41717-1 0-521-76579-X Includes bibliographical references and index.Preface; 1. Logical dynamics, agency, and intelligent interaction; 2. Epistemic logic and semantic information; 3. Dynamic logic of public observation; 4. Multi-agent dynamic-epistemic logic; 5. Dynamics of inference and awareness; 6. Questions and issue management; 7. Soft information, correction, and belief change; 8. An encounter with probability; 9. Preference statics and dynamics; 10. Decisions, actions, and games; 11. Processes over time; 12. Epistemic group structure and collective agency; 13. Logical dynamics in philosophy; 14. Computation as conversation; 15. Rational dynamics in game theory; 16. Meeting cognitive realities; 17. Conclusion; Bibliography.This book develops a view of logic as a theory of information-driven agency and intelligent interaction between many agents - with conversation, argumentation and games as guiding examples. It provides one uniform account of dynamic logics for acts of inference, observation, questions and communication, that can handle both update of knowledge and revision of beliefs. It then extends the dynamic style of analysis to include changing preferences and goals, temporal processes, group action and strategic interaction in games. Throughout, the book develops a mathematical theory unifying all these systems, and positioning them at the interface of logic, philosophy, computer science and game theory. A series of further chapters explores repercussions of the 'dynamic stance' for these areas, as well as cognitive science.Logical Dynamics of Information & InteractionLogic, Symbolic and mathematicalLogic, Symbolic and mathematical.511.3MAT018000bisacshBenthem Johan van1949-51846UkCbUPUkCbUPBOOK9910956065903321Logical dynamics of information and interaction4426459UNINA