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Autonomous agents and multi-agent systems : explorations in learning, self-organization, and adaptive computation /
An autonomous agent is a computational system that acquires sensory data from its environment and decides by itself how to relate the external stimulus to its behaviours in order to attain certain goals. Responding to different stimuli received from its task environment, the agent may select and exh...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Singapore ; River Edge, N.J. :
World Scientific,
2001.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Liu, Jiming, |d 1962- |1 https://id.oclc.org/worldcat/entity/E39PBJxcVpHvGQwqkfPMYGbGHC | |
| 245 | 1 | 0 | |a Autonomous agents and multi-agent systems : |b explorations in learning, self-organization, and adaptive computation / |c Jiming Liu. |
| 260 | |a Singapore ; |a River Edge, N.J. : |b World Scientific, |c 2001. | ||
| 300 | |a 1 online resource (xx, 280 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 500 | |a "By request of the author, the royalty for this book goes to United Nations Children Fund (UNICEF) and a youth public welfare fund." | ||
| 504 | |a Includes bibliographical references (pages 257-276) and index. | ||
| 588 | 0 | |a Print version record. | |
| 520 | |a An autonomous agent is a computational system that acquires sensory data from its environment and decides by itself how to relate the external stimulus to its behaviours in order to attain certain goals. Responding to different stimuli received from its task environment, the agent may select and exhibit different behavioural patterns. The behavioural patterns may be carefully predefined or dynamically acquired by the agent based on some learning and adaptation mechanism(s). In order to achieve structural flexibility, reliability through redundancy, adaptability, and reconfigurability in real-world tasks, some researchers have started to address the issue of multiagent cooperation. Broadly speaking, the power of autonomous agents lies in their ability to deal with unpredictable, dynamically changing environments. Agent-based systems are becoming one of the most important computer technologies, holding out many promises for solving real-world problems. The aims of this book are to provide a guided tour to the pioneering work and the major technical issues in agent research, and to give an in-depth discussion on the computational mechanisms for behavioural engineering in autonomous agents. Through a systematic examination, the book attempts to provide the general design principles for building autonomous agents and the analytical tools for modelling the emerged behavioural properties of a multiagent system. | ||
| 505 | 0 | |a Ch. Introduction. 1.1. What is an agent? 1.2. Basic questions and fundamental issues. 1.3. Learning. 1.4. Neural agents. 1.5. Evolutionary agents. 1.6. Learning in cooperative agents. 1.7. Computational architectures. 1.8. Agent behavioral learning -- ch. 2. Behavioral modeling, planning, and learning. 2.1. Manipulation behaviors. 2.2. Modeling and planning manipulation behaviors. 2.3. Manipulation behavioral learning. 2.4. Summary. 2.5. Other modeling, planning, and learning methods. 2.6. Bibliographical and historical remarks -- ch. 3. Synthetic autonomy. 3.1. Synthetic autonomy based on behavioral self-organization. 3.2. Behavioral self-organization. 3.3. Summary. 3.4. Bibliographical and historical remarks -- ch. 4. Dynamics of distributed computation. 4.1. Definitions. 4.2. Overview of the approach. 4.3. Dynamics of agent-based distributed search. 4.4. Remarks. 4.5. Summary. 4.6. Bibliographical and historical remarks -- ch. 5. Self-organized autonomy in multi-agent systems. 5.1. Collective vision and motion. 5.2. Self-organized vision for image feature detection and tracking. 5.3. Self-organized motion in group robots. 5.4. Summary. 5.5. Bibliographical and historical remarks -- ch. 6. Autonomy-oriented computation. 6.1. Terminology. 6.2. The adaptive self-organizing behavior-based agents. 6.3. The general characteristics of agents. 6.4. The adaptive reproduce-and-diffuse (aR-D) algorithm. 6.5. Examples. 6.6. Computational costs. 6.7. Comparisons with conventional segmentation approaches. 6.8. Effects of behavioral characteristics on agent-based search. 6.9. Parameters affecting agent computation. 6.10. Dynamics of autonomous agents. 6.11. Balance between learning and evolution. 6.12. Summary. 6.13. Bibliographical and historical remarks -- ch. 7. Dynamics and complexity of autonomy-oriented computation. 7.1. Decentralized agent behaviors. 7.2. Goal-attainability. 7.3. Population dynamics. 7.4. Examples. 7.5. Complexity of autonomy-oriented computation. 7.6. Summary. 7.7. Bibliographical and historical remarks. | |
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
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| 650 | 0 | |a Intelligent agents (Computer software) | |
| 650 | 0 | |a Self-organizing systems. | |
| 650 | 6 | |a Agents intelligents (Logiciels) | |
| 650 | 6 | |a Systèmes auto-organisés. | |
| 650 | 7 | |a COMPUTERS |x Enterprise Applications |x Business Intelligence Tools. |2 bisacsh | |
| 650 | 7 | |a COMPUTERS |x Intelligence (AI) & Semantics. |2 bisacsh | |
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| 776 | 0 | 8 | |i Print version: |a Liu, Jiming, 1962- |t Autonomous agents and multi-agent systems. |d Singapore ; River Edge, N.J. : World Scientific, 2001 |z 9810242824 |z 9789810242824 |w (DLC) 2001275891 |w (OCoLC)48108438 |
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