LNAI 2801 Developmental Neural Networks for Agents 1st Edition by Andy Balaam ISBN 9783540200574 354020057X

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Product details:
ISBN 10: 354020057X
ISBN 13: 9783540200574
Author: Andy Balaam

A system for generating neural networks to control simulated agents is described. The networks develop during the lifetime of the agents in a process guided by the genotype and affected by the agent’s experience. Evolution was used to generate effective controllers of this kind for orientation and discrimination tasks as introduced by Beer. This scheme allows these behaviours to be generated quickly and effectively and may offer insights into the effects of developmental processes on cognition. For example, development may allow environmental regularities to be recognised without genetic prespecification. Possible future research into the abilities of these controllers to adapt to radical changes and to undertake widely varying tasks with a single genotype is described.

LNAI 2801 Developmental Neural Networks for Agents 1st Edition Table of contents:

Chapter 1: Introduction to Neural Networks

• What Are Neural Networks?
• History and Evolution of Neural Networks
• Basic Concepts and Terminology
• Types of Neural Networks
• Relevance of Neural Networks in Autonomous Systems

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Chapter 2: Developmental Systems and Developmental Robotics

• Understanding Development in Biological Systems
• Developmental Robotics: A Framework for Artificial Agents
• The Role of Development in Autonomous Agents
• Evolutionary and Developmental Approaches to Robotics

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Chapter 3: Developmental Neural Networks (DNNs)

• Overview of Developmental Neural Networks
• How DNNs Differ from Traditional Neural Networks
• Key Concepts in Developmental Neural Networks
• The Role of DNNs in Shaping Agent Behavior
• Advantages of Using DNNs for Autonomous Learning

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Chapter 4: Architecture of Developmental Neural Networks

• Core Components of DNNs
• The Architecture of Developmental Neural Networks
• How Neural Networks Develop and Adapt Over Time
• Self-Organization and Learning in Developmental Systems
• Modularity and Hierarchical Structures in DNNs

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Chapter 5: Learning and Adaptation in Developmental Neural Networks

• Developmental Learning vs. Traditional Learning
• Mechanisms of Learning and Memory in DNNs
• Emergent Behavior and Adaptation in Dynamic Environments
• Incremental Learning and the Importance of Experience
• The Role of Exploration in Learning Processes

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Chapter 6: Developmental Algorithms for Autonomous Agents

• Algorithms for Developing Neural Networks in Autonomous Systems
• Evolutionary Techniques for Neural Network Development
• Exploration of Evolutionary Developmental Algorithms (EDA)
• Case Studies of Developmental Algorithms in Action
• Implementing Developmental Learning in Agent Systems

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Chapter 7: Implementing DNNs in Agents

• How to Implement Developmental Neural Networks in Autonomous Agents
• Tools and Frameworks for Building DNNs
• Sensorimotor Integration and Feedback Loops
• Practical Considerations for Building DNN-based Agents
• Example Projects Using Developmental Neural Networks in Robotics

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Chapter 8: Case Studies and Applications of DNNs in Autonomous Agents

• Applications in Developmental Robotics
• Real-world Examples of DNNs in Simulated Agents
• DNNs for Self-Organizing Systems and Multi-Agent Environments
• Development of Behavior and Skills Over Time
• Application of DNNs in Artificial Life, Robotics, and AI

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Chapter 9: Challenges in Developmental Neural Networks

• Scalability and Complexity in DNNs
• The Challenge of Learning from Sparse or Noisy Data
• Robustness and Stability in Developmental Systems
• Addressing Ethical and Practical Concerns in Autonomous Systems
• Open Problems and Future Challenges in DNNs

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Chapter 10: Future Directions in Developmental Neural Networks

• Emerging Trends and Research in Developmental Neural Networks
• The Role of DNNs in Artificial General Intelligence (AGI)
• Interdisciplinary Approaches to Development in AI
• Future Applications in Healthcare, Robotics, and Autonomous Vehicles
• Toward a Unified Theory of Development and Learning in AI

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Conclusion

• Summary of Key Findings
• The Impact of Developmental Neural Networks on Autonomous Systems
• Final Thoughts on the Future of Developmental Learning in AI

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