Generate an advanced algorithm in python that envelops this idea:

```


### Algorithm Overview

**Smart UnlockedGent** combines elements of **heuristic-based searching** (like A* or Dijkstra's) with **backtracking intelligence**. The algorithm uses an **unlocking mechanism** to dynamically assess paths, mark them as blocked or clear, and find optimal routes. It seeks to avoid unnecessary exploration, making it faster and more "intelligent" in open mazes, and able to "unlock" paths in complex or looping mazes.

### How Smart UnlockedGent Works

1. **Initialization**: Define starting point \( S \) and destination \( D \) on the grid. Initialize a priority queue to manage nodes based on distance to \( D \).

2. **Heuristic Function**: Use a heuristic, such as Manhattan distance, to estimate the distance from each node to the destination \( D \).

3. **Path Rating (Unlocking Mechanism)**: Each path is rated based on:
    - **Directness** (distance to destination)
    - **Openness** (number of adjacent nodes)
    - **Backtrack Avoidance** (identifying loops or areas already explored)
   
   This helps prioritize paths that are more likely to lead closer to \( D \) without unnecessary diversions.

4. **Memory and Tracking**:
    - Maintain a set of "locked" paths for backtracked or unreachable areas.
    - Store a stack of nodes for possible backtracking if the current path becomes a dead end.
   
5. **Adaptive Node Scoring**:
    - Each time a path leads to a dead-end or loops back to itself, **decrease its score** in memory.
    - Allow for re-exploration of previously explored nodes only if they show potential based on updated conditions.

6. **Node Exploration**:
    - Expand from the current node by evaluating neighbors.
    - Nodes are added to the queue based on a combined score of heuristic distance and path rating.
    - Avoid nodes marked as "locked" unless no other paths are available.

7. **Backtracking Strategy**:
    - When encountering a dead end, backtrack to the last known node with alternative unexplored paths.
    - Dynamically "unlock" backtracked nodes by updating their scores to reflect any new possible paths.

8. **Goal Reached**:
    - If the destination \( D \) is reached, the solution path is constructed from the sequence of nodes traversed.

### Key Features of Smart UnlockedGent

- **Efficiency in Large, Open Mazes**: Reduces exploration in open areas by only exploring the shortest and most promising paths.
- **Intelligent Backtracking**: Minimizes unnecessary re-exploration through the unlocking and path rating system.
- **Adaptive Pathfinding**: Adjusts the path dynamically based on current maze conditions, avoiding traps and efficiently navigating loops.

### Pseudocode

Here's a simple outline in pseudocode:

```python
function SmartUnlockedGent(start, destination, maze):
    initialize queue with start node, score 0
    initialize visited set, locked paths set, backtrack stack
    
    while queue is not empty:
        current = dequeue(queue)
        
        if current == destination:
            return construct_path(current)
        
        mark current as visited
        neighbors = get_neighbors(current, maze)
        
        for neighbor in neighbors:
            if neighbor in locked paths:
                continue
            
            score = heuristic(neighbor, destination) + path_rating(neighbor)
            
            if neighbor not in visited or better_score(score, neighbor):
                set_score(neighbor, score)
                add to queue based on score
                update backtrack stack
            
            if is_dead_end(neighbor):
                lock_path(neighbor)
                backtrack_to_last_open_node()
                
    return "No Path Found"
```

### Advantages

- **Adaptive Memory**: Avoids redundant exploration and enables revisiting paths when new conditions make them optimal.
- **High Efficiency**: Prioritizes paths smartly, making it particularly efficient in complex mazes.
- **Dynamic Decision-Making**: Adjusts path ratings based on real-time evaluation, leading to a "smart" and flexible exploration.