Learning Path:

1. Search under Adversarial Circumstances:

   • Resources:
     • Game Theory - MIT OpenCourseWare
     • [AI: A Modern Approach by Stuart Russell and Peter Norvig (Chapter 5-6)]
   • Notes: Explain adversarial search, examples, and applications.

2. Optimal Decision in Game:

   • Resources:
     • Game Theory and AI - Medium
   • Notes: Define optimal decision, minimax algorithm, and applications.

3. Minimax Algorithm and Alpha-beta Pruning:

   • Resources:
     • Minimax and Alpha-beta Pruning - GeeksforGeeks
   • Notes: Explain algorithms, examples, and practical implementation.

4. Games with an Element of Chance:

   • Resources:
     • Probabilistic Game Theory - Medium
   • Notes: Define stochastic games, examples, and real-time applications.

5. Imperfect Real Time Decision and Partially Observable Games:

   • Resources:
     • AI for Games - Stanford University
   • Notes: Explain imperfect real-time decision-making and partially observable games.

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Link to original note: AI-Learning ResourcesAI-Learning ResourcesTo help you gain a deep understanding of Artificial Intelligence (AI), we'll create a structured learning path based on the provided syllabus. The plan includes a detailed breakdown of each unit, along with recommendations for resources such as research papers, blogs, articles, videos, and other multimedia content. Additionally, we'll suggest methods for annotating and making notes to optimize your learning experience. Unit I - Introduction to AI * Topics1 * Learning Path1 * Multimedia Content