Overview
Explore Goal-Based Agents through theory and hands-on Python implementation, learning how these intelligent systems act based on goals rather than just conditions, with practical examples and code demonstrations.
Syllabus
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- Introduction to Goal-Based Agents
-- Definition of Goal-Based Agents
-- Comparison with Other Agent Types: Reflex, Utility, and Learning Agents
-- Overview of Course Structure and Objectives
- Theoretical Foundations
-- Understanding Goals and Constraints
-- Planning and Decision-Making Processes
-- Optimization Techniques for Goal Achievement
- Logical Representation and Search
-- Knowledge Representation for Goal-Based Agents
-- Search Algorithms: Depth-First, Breadth-First, A*, and Heuristic Approaches
-- Practical Example: Implementing Search in Python
- Architecture of Goal-Based Agents
-- Components of a Goal-Based Agent
-- Goal Formulation and Prioritization
-- Agent Environment Interaction
- Practical Implementation in Python
-- Introduction to Python Libraries for AI (e.g., NumPy, Matplotlib)
-- Setting up the Development Environment
-- Coding a Simple Goal-Based Agent: Step-by-Step
- Advanced Planning Techniques
-- Planning Under Uncertainty
-- Probabilistic Models and Decision Trees
-- Integration with Machine Learning Models
- Real-World Applications
-- Robotics and Autonomous Navigation
-- Smart Assistants and Task Scheduling
-- Case Studies and Industry Examples
- Hands-On Project: Building a Goal-Based System
-- Project Overview and Requirements
-- Designing the System Architecture
-- Implementing and Testing the System
- Conclusion and Future Directions
-- Trends in Goal-Based Agent Research
-- Future Challenges and Opportunities
-- Recap of Key Learnings
- Additional Resources and References
-- Suggested Readings
-- Online Tutorials and Communities
-- Final Q&A and Course Feedback
Taught by
Tags
On-Demand
