04760nam 22007935 450 99646605050331620200702125906.01-280-30678-597866103067873-540-24623-110.1007/b95170(CKB)1000000000016823(DE-He213)978-3-540-24623-7(SSID)ssj0000100299(PQKBManifestationID)11131566(PQKBTitleCode)TC0000100299(PQKBWorkID)10020108(PQKB)10397030(MiAaPQ)EBC3088929(PPN)155169254(EXLCZ)99100000000001682320121227d2004 u| 0engurnn|008mamaatxtrdacontentcrdamediacrrdacarrierAgent-Based Hybrid Intelligent Systems[electronic resource] An Agent-Based Framework for Complex Problem Solving /edited by Zili Zhang, Chengqi Zhang1st ed. 2004.Berlin, Heidelberg :Springer Berlin Heidelberg :Imprint: Springer,2004.1 online resource (XV, 194 p.) Lecture Notes in Artificial Intelligence ;2938Bibliographic Level Mode of Issuance: Monograph3-540-20908-5 Includes bibliographical references and index.Fundamentals of Hybrid Intelligent Systems and Agents -- 1 Introduction -- 2 Basics of Hybrid Intelligent Systems -- 3 Basics of Agents and Multi-agent Systems -- Methodology and Framework -- 4 Agent-Oriented Methodologies -- 5 Agent-Based Framework for Hybrid Intelligent Systems -- 6 Matchmaking in Middle Agents -- Application Systems -- 7 Agent-Based Hybrid Intelligent System for Financial Investment Planning -- 8 Agent-Based Hybrid Intelligent System for Data Mining -- Concluding Remarks -- 9 The Less the More -- Appendix: Sample Source Codes of the Agent-Based Financial Planning System -- References.Solving complex problems in real-world contexts, such as financial investment planning or mining large data collections, involves many different sub-tasks, each of which requires different techniques. To deal with such problems, a great diversity of intelligent techniques are available, including traditional techniques like expert systems approaches and soft computing techniques like fuzzy logic, neural networks, or genetic algorithms. These techniques are complementary approaches to intelligent information processing rather than competing ones, and thus better results in problem solving are achieved when these techniques are combined in hybrid intelligent systems. Multi-Agent Systems are ideally suited to model the manifold interactions among the many different components of hybrid intelligent systems. This book introduces agent-based hybrid intelligent systems and presents a framework and methodology allowing for the development of such systems for real-world applications. The authors focus on applications in financial investment planning and data mining.Lecture Notes in Artificial Intelligence ;2938Artificial intelligenceSoftware engineeringComputersDatabase managementApplication softwareArtificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Software Engineeringhttps://scigraph.springernature.com/ontologies/product-market-codes/I14029Computation by Abstract Deviceshttps://scigraph.springernature.com/ontologies/product-market-codes/I16013Database Managementhttps://scigraph.springernature.com/ontologies/product-market-codes/I18024Computer Appl. in Administrative Data Processinghttps://scigraph.springernature.com/ontologies/product-market-codes/I2301XArtificial intelligence.Software engineering.Computers.Database management.Application software.Artificial Intelligence.Software Engineering.Computation by Abstract Devices.Database Management.Computer Appl. in Administrative Data Processing.006.3/3Zhang ZiliPh. D.1239223Zhang Ziliedthttp://id.loc.gov/vocabulary/relators/edtZhang Chengqiedthttp://id.loc.gov/vocabulary/relators/edtMiAaPQMiAaPQMiAaPQBOOK996466050503316Agent-Based Hybrid Intelligent Systems2875483UNISA