Learning, Reasoning, and Planning with Neuro-Symbolic Concepts

Overview

Explore neuro-symbolic concepts as compositional abstractions for AI systems, enabling efficient learning, reasoning, and planning through the combination of symbolic programs and neural networks.

Syllabus

• Introduction to Neuro-Symbolic AI
Overview of neuro-symbolic systems
Historical context and evolution
Key motivations and benefits
• Neural Networks: Foundations
Basics of neural network architectures
Training methodologies
Limitations in reasoning and interpretability
• Symbolic AI: Basics and Techniques
Logic and knowledge representation
Search algorithms and planning
Symbol manipulation and inference
• Integration of Neural and Symbolic Approaches
Concepts of hybrid systems
Methods for combining neural and symbolic components
Case studies of existing neuro-symbolic systems
• Learning in Neuro-Symbolic Systems
Compositional learning techniques
Transfer and multitask learning in hybrid systems
Handling uncertainty and probabilistic reasoning
• Reasoning with Neuro-Symbolic Concepts
Explanation generation and interpretability
Logical reasoning with neural networks
Temporal and spatial reasoning
• Planning in Neuro-Symbolic Systems
Planning strategies and algorithms
Hierarchical models and decision-making
Real-world applications and challenges
• Applications and Case Studies
Natural language understanding and processing
Computer vision and image understanding
Robotics and autonomous systems
• Future Directions and Research Opportunities
Current trends and open problems
Ethics and societal impacts of neuro-symbolic AI
Opportunities for cross-disciplinary research
• Course Wrap-up and Project Presentations
Review of key concepts and learnings
Student project presentations
Feedback and discussion on future learning paths

Subjects

Computer Science