Implement Tree of Thoughts (ToT) reasoning:
ToT Framework: Instead of linear reasoning (CoT), explore multiple reasoning paths as a tree.
Components:
generate_thoughts(state, k): Generate k candidate next stepsevaluate_state(state): Score state (0-1)search(initial, goal_test, strategy): Find solution path- bfs: Breadth-first exploration
- dfs: Depth-first
- beam: Keep only top-k branches at each level
prune_low_value_branches(threshold): Remove poor branchesbacktrack_best_path(): Extract solution
Node Structure:
{'state': ..., 'parent': ..., 'children': [...], 'value': ..., 'depth': ...}
Search: Explore tree, evaluating states, pruning low-value branches.
Examples
Example 1:
Input:
tot = TreeOfThoughts(branching_factor=2, max_depth=3); thoughts = tot.generate_thoughts('start', 2); len(thoughts)Output:
2Explanation: Generated 2 thoughts as requested
Starter Code
class TreeOfThoughts:
"""
Tree of Thoughts reasoning for complex problem solving.
"""
def __init__(self, branching_factor=3, max_depth=5):
self.branching_factor = branching_factor
self.max_depth = max_depth
self.tree = {}
def generate_thoughts(self, state, k):
"""Generate k possible next thoughts from state"""
# Your implementation here
pass
def evaluate_state(self, state):
"""
Evaluate state value (0-1).
Higher = more promising for reaching goal.
"""
# Your implementation here
pass
def search(self, initial_state, goal_test, strategy='bfs'):
"""
Search for solution using ToT.
Strategies: 'bfs', 'dfs', 'beam'
Returns path of thoughts to solution.
"""
# Your implementation here
pass
def prune_low_value_branches(self, threshold=0.3):
"""Remove branches with value below threshold"""
# Your implementation here
pass
def backtrack_best_path(self):
"""Find best path from root to leaf"""
# Your implementation here
passPython3
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