Learning and Coordination: Enhancing Agent Performance through Distributed Decision Making
Intelligent systems of the natural kind are adaptive and robust: they learn over time and degrade gracefully under stress. If artificial systems are to display a similar level of sophistication, an organizing framework and operating principles are required to manage the resulting complexity of desig...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Dordrecht
Springer Netherlands
1994
|
| Schriftenreihe: | Microprocessor-Based and Intelligent Systems Engineering
13 |
| Schlagworte: | |
| Online-Zugang: | BTU01 Volltext |
| Zusammenfassung: | Intelligent systems of the natural kind are adaptive and robust: they learn over time and degrade gracefully under stress. If artificial systems are to display a similar level of sophistication, an organizing framework and operating principles are required to manage the resulting complexity of design and behavior. This book presents a general framework for adaptive systems. The utility of the comprehensive framework is demonstrated by tailoring it to particular models of computational learning, ranging from neural networks to declarative logic. The key to robustness lies in distributed decision making. An exemplar of this strategy is the neural network in both its biological and synthetic forms. In a neural network, the knowledge is encoded in the collection of cells and their linkages, rather than in any single component. Distributed decision making is even more apparent in the case of independent agents. For a population of autonomous agents, their proper coordination may well be more instrumental for attaining their objectives than are their individual capabilities. This book probes the problems and opportunities arising from autonomous agents acting individually and collectively. Following the general framework for learning systems and its application to neural networks, the coordination of independent agents through game theory is explored. Finally, the utility of game theory for artificial agents is revealed through a case study in robotic coordination. Given the universality of the subjects -- learning behavior and coordinative strategies in uncertain environments -- this book will be of interest to students and researchers in various disciplines, ranging from all areas of engineering to the computing disciplines; from the life sciences to the physical sciences; and from the management arts to social studies |
| Beschreibung: | 1 Online-Ressource (XII, 188 p) |
| ISBN: | 9789401110167 |
| DOI: | 10.1007/978-94-011-1016-7 |
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Datensatz im Suchindex
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| author | Kim, Steven H. |
| author_facet | Kim, Steven H. |
| author_role | aut |
| author_sort | Kim, Steven H. |
| author_variant | s h k sh shk |
| building | Verbundindex |
| bvnumber | BV045186110 |
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| dewey-ones | 006 - Special computer methods |
| dewey-raw | 006.3 |
| dewey-search | 006.3 |
| dewey-sort | 16.3 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| doi_str_mv | 10.1007/978-94-011-1016-7 |
| format | Electronic eBook |
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| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:10:56Z |
| institution | BVB |
| isbn | 9789401110167 |
| language | English |
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| publishDate | 1994 |
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| series | Microprocessor-Based and Intelligent Systems Engineering |
| series2 | Microprocessor-Based and Intelligent Systems Engineering |
| spelling | Kim, Steven H. Verfasser aut Learning and Coordination Enhancing Agent Performance through Distributed Decision Making by Steven H. Kim Dordrecht Springer Netherlands 1994 1 Online-Ressource (XII, 188 p) txt rdacontent c rdamedia cr rdacarrier Microprocessor-Based and Intelligent Systems Engineering 13 Intelligent systems of the natural kind are adaptive and robust: they learn over time and degrade gracefully under stress. If artificial systems are to display a similar level of sophistication, an organizing framework and operating principles are required to manage the resulting complexity of design and behavior. This book presents a general framework for adaptive systems. The utility of the comprehensive framework is demonstrated by tailoring it to particular models of computational learning, ranging from neural networks to declarative logic. The key to robustness lies in distributed decision making. An exemplar of this strategy is the neural network in both its biological and synthetic forms. In a neural network, the knowledge is encoded in the collection of cells and their linkages, rather than in any single component. Distributed decision making is even more apparent in the case of independent agents. For a population of autonomous agents, their proper coordination may well be more instrumental for attaining their objectives than are their individual capabilities. This book probes the problems and opportunities arising from autonomous agents acting individually and collectively. Following the general framework for learning systems and its application to neural networks, the coordination of independent agents through game theory is explored. Finally, the utility of game theory for artificial agents is revealed through a case study in robotic coordination. Given the universality of the subjects -- learning behavior and coordinative strategies in uncertain environments -- this book will be of interest to students and researchers in various disciplines, ranging from all areas of engineering to the computing disciplines; from the life sciences to the physical sciences; and from the management arts to social studies Computer Science Artificial Intelligence (incl. Robotics) Manufacturing, Machines, Tools Mechanical Engineering Operations Management Computer science Production management Artificial intelligence Mechanical engineering Manufacturing industries Machines Tools Neuronales Netz (DE-588)4226127-2 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 s Neuronales Netz (DE-588)4226127-2 s 1\p DE-604 Erscheint auch als Druck-Ausgabe 9789401044424 Microprocessor-Based and Intelligent Systems Engineering 13 (DE-604)BV046828176 13 https://doi.org/10.1007/978-94-011-1016-7 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Kim, Steven H. Learning and Coordination Enhancing Agent Performance through Distributed Decision Making Microprocessor-Based and Intelligent Systems Engineering Computer Science Artificial Intelligence (incl. Robotics) Manufacturing, Machines, Tools Mechanical Engineering Operations Management Computer science Production management Artificial intelligence Mechanical engineering Manufacturing industries Machines Tools Neuronales Netz (DE-588)4226127-2 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| subject_GND | (DE-588)4226127-2 (DE-588)4193754-5 |
| title | Learning and Coordination Enhancing Agent Performance through Distributed Decision Making |
| title_auth | Learning and Coordination Enhancing Agent Performance through Distributed Decision Making |
| title_exact_search | Learning and Coordination Enhancing Agent Performance through Distributed Decision Making |
| title_full | Learning and Coordination Enhancing Agent Performance through Distributed Decision Making by Steven H. Kim |
| title_fullStr | Learning and Coordination Enhancing Agent Performance through Distributed Decision Making by Steven H. Kim |
| title_full_unstemmed | Learning and Coordination Enhancing Agent Performance through Distributed Decision Making by Steven H. Kim |
| title_short | Learning and Coordination |
| title_sort | learning and coordination enhancing agent performance through distributed decision making |
| title_sub | Enhancing Agent Performance through Distributed Decision Making |
| topic | Computer Science Artificial Intelligence (incl. Robotics) Manufacturing, Machines, Tools Mechanical Engineering Operations Management Computer science Production management Artificial intelligence Mechanical engineering Manufacturing industries Machines Tools Neuronales Netz (DE-588)4226127-2 gnd Maschinelles Lernen (DE-588)4193754-5 gnd |
| topic_facet | Computer Science Artificial Intelligence (incl. Robotics) Manufacturing, Machines, Tools Mechanical Engineering Operations Management Computer science Production management Artificial intelligence Mechanical engineering Manufacturing industries Machines Tools Neuronales Netz Maschinelles Lernen |
| url | https://doi.org/10.1007/978-94-011-1016-7 |
| volume_link | (DE-604)BV046828176 |
| work_keys_str_mv | AT kimstevenh learningandcoordinationenhancingagentperformancethroughdistributeddecisionmaking |