000 | 03679nam a22005775i 4500 | ||
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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 |
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024 | 7 |
_a10.1007/b95170 _2doi |
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050 | 4 | _aQ334-342 | |
050 | 4 | _aTJ210.2-211.495 | |
072 | 7 |
_aUYQ _2bicssc |
|
072 | 7 |
_aTJFM1 _2bicssc |
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072 | 7 |
_aCOM004000 _2bisacsh |
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082 | 0 | 4 |
_a006.3 _223 |
100 | 1 |
_aZhang, Zili. _eauthor. |
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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. |
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300 |
_aXV, 194 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aLecture Notes in Computer Science, _x0302-9743 ; _v2938 |
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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. |
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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 |
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856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/b95170 |
942 |
_2EBK3080 _cEBK |
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999 |
_c32374 _d32374 |