Symbolic AI Algorithms Repository

Summary:

I'm excited to explore Symbolic AI and its different algorithms and logic reasoning. To document my learning journey and for future reference, I have created this repository. As I progress my learning journey, I will write about various AI algorithms and concepts.

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Contents and Prerequisites:

• The first part of this repository will focus on search algorithms. I will continue to add more content as I learn and explore new topics in AI.

  • BFS/ DFS/ IDS
  • UCS/ Gready/ A-star
  • Min-max/ Alpha-beta prunning
  • ReadME: Z_Algo_Details.md

• The second part is about constraint satisfaction problems.

  • Constraint graph
  • Variable selection heuristic
  • Value ordering heuristic
  • Arc consistency
  • ReadME: Z_Mapping_Problem.md

• The third part is about planning, covering things such as:

  • Linear/non-linear/Serial/Parallel plan
  • Different planning algorithm
  • STRIPS, PDDL, ADL

• If you're interested in logical reasoning or KRR, check out:

  • KRR
  • Logic

• The renowned book, Artificial Intelligence: A Modern Approach (3rd Edition), has been uploaded to the folder. Even if you're a fan of ML, you can still have some fun exploring the book to learn more about traditional AI.

• If you're interested in exploring more usage of search algorithms, I invite you to take a look at my Algorithm repository. Additionally, you can further practice and test your skills on platforms like leetcode.

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Specifications:

For search algorithms, I will not use a tree, but instead, I will use mazes, graphs, and grids created by dictionaries or nested dictionaries or my own data structure called grid. When there's a distance or cost associated with moving between places, it will be considered.

Extra:

I will provide detailed docstrings for each algorithm. Last update date: 26/06/2023