feat: updated roadmap

README.md

@@ -4,29 +4,29 @@
 # Roadmap
 ### Improve Evaluation Function
 Enhance the evaluation function to consider more factors:
-- King safety
-- Pawn structure (isolated, doubled, passed pawns)
-- Piece mobility
-- Control of key squares (e.g., center control)
-- Rook on open files
-- Bishop pair advantage
+- [ ] King safety
+- [ ] Pawn structure (isolated, doubled, passed pawns)
+- [ ] Piece mobility
+- [ ] Control of key squares (e.g., center control)
+- [ ] Rook on open files
+- [ ] Bishop pair advantage
 
 ### Enhance Move Ordering
 Implement more advanced move ordering techniques:
-- History heuristic: Track the success of moves in the search tree and prioritize moves that have historically caused beta cutoffs.
-- Killer move heuristic: Track moves that cause beta cutoffs at the same depth and prioritize them.
-- Principal variation search: Use the best move from the previous iteration as the first move in the current iteration.
+- [ ] History heuristic: Track the success of moves in the search tree and prioritize moves that have historically caused beta cutoffs.
+- [X] Killer move heuristic: Track moves that cause beta cutoffs at the same depth and prioritize them.
+- [X] Principal variation search: Use the best move from the previous iteration as the first move in the current iteration.
 
 ### Improve Quiescence Search
 Extend the quiescence search to include more types of moves, such as:
-- Checks
-- Threats (moves that create immediate threats)
+- [X] Checks
+- [ ] Threats (moves that create immediate threats)
 
 ### Optimize Transposition Table
-Use Zobrist hashing to efficiently hash board positions and implement a more sophisticated replacement scheme (e.g., always replace, depth-based replacement).
+Use Zobrist hashing to efficiently hash board positions and implement a more sophisticated replacement scheme (e.g., always replace, depth- [ based replacement).
 
 ### Implement Advanced Search Techniques
-- **Iterative Deepening**: Perform a series of depth-limited searches, increasing the depth with each iteration. This helps with move ordering and time management.
-- **Null Move Pruning**: Assume the opponent will pass their turn and see if the current player can still achieve a good position. If so, prune the branch.
-- **Late Move Reductions**: Reduce the search depth for moves that are considered less likely to be good (e.g., moves that are not captures or checks).
-- **Aspiration Windows**: Use a narrow window around the expected score to speed up alpha-beta search. If the score falls outside the window, re-search with a wider window.
+- [X] **Iterative Deepening**: Perform a series of depth- [ limited searches, increasing the depth with each iteration. This helps with move ordering and time management.
+- [ ] **Null Move Pruning**: Assume the opponent will pass their turn and see if the current player can still achieve a good position. If so, prune the branch.
+- [ ] **Late Move Reductions**: Reduce the search depth for moves that are considered less likely to be good (e.g., moves that are not captures or checks).
+- [ ] **Aspiration Windows**: Use a narrow window around the expected score to speed up alpha- [ beta search. If the score falls outside the window, re- [ search with a wider window.