000 03679nam a22005775i 4500
001 978-3-540-24623-7
003 DE-He213
005 20160624101903.0
007 cr nn 008mamaa
008 121227s2004 gw | s |||| 0|eng d
020 _a9783540246237
_9978-3-540-24623-7
024 7 _a10.1007/b95170
_2doi
050 4 _aQ334-342
050 4 _aTJ210.2-211.495
072 7 _aUYQ
_2bicssc
072 7 _aTJFM1
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aZhang, Zili.
_eauthor.
245 1 0 _aAgent-Based Hybrid Intelligent Systems
_h[electronic resource] :
_bAn Agent-Based Framework for Complex Problem Solving /
_cby Zili Zhang, Chengqi Zhang.
260 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2004.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2004.
300 _aXV, 194 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v2938
505 0 _aFundamentals 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.
520 _aSolving 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.
650 0 _aComputer science.
650 0 _aSoftware engineering.
650 0 _aDatabase management.
650 0 _aArtificial intelligence.
650 0 _aInformation systems.
650 1 4 _aComputer Science.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aSoftware Engineering.
650 2 4 _aComputation by Abstract Devices.
650 2 4 _aDatabase Management.
650 2 4 _aComputer Appl. in Administrative Data Processing.
700 1 _aZhang, Chengqi.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783540209089
786 _dSpringer
830 0 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v2938
856 4 0 _uhttp://dx.doi.org/10.1007/b95170
942 _2EBK3080
_cEBK
999 _c32374
_d32374