Default stopping conditions

Research Analysis Bugs Solved Bug: The tot_dfs method did not have the pruning_threshold parameter implemented.

Root Problem: The original implementation of the tot_dfs method did not include dynamic pruning, which could lead to unnecessary exploration of low-quality states.

Fix: Implemented dynamic pruning in the tot_dfs method by adding a pruning_threshold parameter. If the state evaluation value is below the pruning_threshold, the state is pruned, and its children are not explored.

Optimizations and Improvements Optimization: Introduced a confidence threshold for the evaluation values.

Benefit: By setting a confidence threshold, the algorithm can stop and return the solution when a generated thought has an evaluation value above this threshold. This ensures that the algorithm stops when it has reached a high-quality solution, improving reliability.

Optimization: Set a maximum number of iterations for the algorithm.

Benefit: Limiting the number of iterations prevents the algorithm from running indefinitely if a satisfactory solution is not found. This improves the algorithm's efficiency and ensures that it returns the best solution found so far within a reasonable time frame.

Optimization: Implemented a convergence check to monitor the change in evaluation values between consecutive iterations.

Benefit: By monitoring the change in evaluation values, the algorithm can stop and return the solution when the change is below a certain threshold for a specified number of consecutive iterations. This indicates that the algorithm has likely reached a local optimum and further exploration may not yield significant improvements. This optimization increases the algorithm's efficiency.

Optimization: Implemented parallel processing for generating thoughts and evaluating states.

Benefit: Parallel processing speeds up the execution of the algorithm by utilizing multiple CPU cores to generate thoughts and evaluate states concurrently. This reduces the overall computation time and improves the algorithm's efficiency.

Overall Benefits The bug fixes and optimizations implemented in the Tree of Thoughts algorithm increase its reliability, efficiency, and speed. By introducing stopping conditions such as confidence threshold, max iterations, and convergence check, the algorithm can now stop when it has reached a satisfactory solution or when further exploration is unlikely to yield significant improvements. Additionally, the implementation of dynamic pruning and parallel processing reduces the search space and computation time, resulting in a faster and more efficient algorithm. These improvements ultimately benefit the user by providing more accurate and timely solutions to their problems.