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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 |
Tabla de Contenidos:
- 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.