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020 _a9783540458340
_9978-3-540-45834-0
024 7 _a10.1007/3-540-45834-4
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aHannebauer, Markus.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aAutonomous Dynamic Reconfiguration in Multi-Agent Systems
_h[electronic resource] :
_bImproving the Quality and Efficiency of Collaborative Problem Solving /
_cby Markus Hannebauer.
250 _a1st ed. 2002.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2002.
300 _aXXII, 290 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 Artificial Intelligence,
_x2945-9141 ;
_v2427
505 0 _a1.Overview -- 2. Basics of Collaborative Problem Solving -- Theoretical Foundations -- 3. Distributed Constraint Problems — A Model for Collaborative Problem Solving -- 4. Autonomous Dynamic Reconfiguration — Improving Collaborative Problem Solving -- Practical Concepts -- 5. Multi-agent System Infrastructure -- 6. External Constraint Problem Solving -- 7. Composable BDI Agents -- 8. Internal Constraint Problem Solving -- 9. Controlling Agent Melting and Agent Splitting -- Assessment -- 10. Evaluation -- 11. Conclusion and Future Work -- A. Symbols and Abbreviations -- B. An XML-Encoded Request Message -- C. SICStus Prolog Code for Internal Constraint Problem Solving -- D. Initialization of the Hospital Scenario Generator.
520 _aHigh communication efforts and poor problem solving results due to restricted overview are two central issues in collaborative problem solving. This work addresses these issues by introducing the processes of agent melting and agent splitting that enable individual problem solving agents to continually and autonomously reconfigure and adapt themselves to the particular problem to be solved. The author provides a sound theoretical foundation of collaborative problem solving itself and introduces various new design concepts and techniques to improve its quality and efficiency, such as the multi-phase agreement finding protocol for external problem solving, the composable belief-desire-intention agent architecture, and the distribution-aware constraint specification architecture for internal problem solving. The practical relevance and applicability of the concepts and techniques provided are demonstrated by using medical appointment scheduling as a case study.
650 0 _aArtificial intelligence.
650 0 _aComputer networks .
650 0 _aCompilers (Computer programs).
650 0 _aApplication software.
650 0 _aComputers and civilization.
650 0 _aBusiness information services.
650 1 4 _aArtificial Intelligence.
650 2 4 _aComputer Communication Networks.
650 2 4 _aCompilers and Interpreters.
650 2 4 _aComputer and Information Systems Applications.
650 2 4 _aComputers and Society.
650 2 4 _aIT in Business.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783540443124
776 0 8 _iPrinted edition:
_z9783662197714
830 0 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v2427
856 4 0 _uhttps://doi.org/10.1007/3-540-45834-4
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
912 _aZDB-2-LNC
912 _aZDB-2-BAE
942 _cSPRINGER
999 _c187902
_d187902