Autonomous Dynamic Reconfiguration in Multi-Agent Systems Improving the Quality and Efficiency of Collaborative Problem Solving / [electronic resource] :
edited by Markus Hannebauer.
- Berlin, Heidelberg : Springer Berlin Heidelberg, 2002.
- XXII, 290 p. online resource.
- Lecture Notes in Computer Science, 2427 0302-9743 ; .
- Lecture Notes in Computer Science, 2427 .
1.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.
High 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.
9783540458340
10.1007/3-540-45834-4 doi
Computer science.
Computer Communication Networks.
Information systems.
Artificial intelligence.
Management information systems.
Computer Science.
Artificial Intelligence (incl. Robotics).
Computer Communication Networks.
Programming Languages, Compilers, Interpreters.
Information Systems Applications (incl.Internet).
Computers and Society.
Business Information Systems.
Q334-342 TJ210.2-211.495
006.3
1.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.
High 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.
9783540458340
10.1007/3-540-45834-4 doi
Computer science.
Computer Communication Networks.
Information systems.
Artificial intelligence.
Management information systems.
Computer Science.
Artificial Intelligence (incl. Robotics).
Computer Communication Networks.
Programming Languages, Compilers, Interpreters.
Information Systems Applications (incl.Internet).
Computers and Society.
Business Information Systems.
Q334-342 TJ210.2-211.495
006.3