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        CS170程序代做、Python編程設計代寫

        時間:2024-02-11  來源:合肥網hfw.cc  作者:hfw.cc 我要糾錯



        Project 1: Search Algorithms in a Grid
        Environment and Path-finding
        CS170 Artificial Intelligence, UCR, Winter 2024
        1 Introduction
        Explore the world of search algorithms in a grid-based environment. In this
        project, you will implement different search strategies to navigate from a
        starting cell to a target cell, while encountering obstacles and open paths.
        This hands-on exercise aims to deepen your understanding of the fundamental search algorithms frequently employed in Artificial Intelligence. This
        project also involves determining the shortest path based on the search algorithm you use for exploring the route.
        2 Code Structure
        You’ll be given a code template containing the SearchAlgorithms class. Your
        task is to fill in the methods corresponding to each search algorithm and
        ensure they return both the status of the target’s discovery and the final
        state of the grid:
        • uniform search(): Implement the Uniform Search algorithm.
        • dfs(): Implement the DepthFirst Search algorithm.
        • bfs(): Implement the BreadthFirst Search algorithm.
        • best first(): Implement the Best First Search algorithm, based on
        a heuristic you design or choose. Use the Manhattan distance as the
        heuristic.
        1
        • a star(): Implement the A Search algorithm, combining both cost
        and heuristic (Manhattan distance).
        • agreedy(): Implement the Greedy Search algorithm, focusing solely
        on the heuristic (Manhattan distance).
        For algorithms that use a priority queue, utilize the heapq module from
        Python’s standard library to manage the queue efficiently. The grid is represented as a list of lists, containing:
        • s: Starting position.
        • t: Target or goal position.
        • 0: Empty cells that you can traverse.
        • -1: Walls or obstacles that you cannot traverse.
        As you traverse the grid, mark the order of cells you visit by replacing the
        0s with consecutive numbers. The starting and target positions, represented
        by s and t, should remain unchanged.
        3 Requirements
        • Follow the provided class and method names precisely. This ensures
        compatibility with the autograder on Gradescope.
        • The function signatures or class names must not be altered.
        • Use the Manhattan distance as the heuristic for the Best First, A*, and
        Greedy algorithms.
        • Utilize the heapq module for implementing priority queues in applicable
        algorithms.
        • Each search algorithm function must return a tuple containing two
        elements: a numeric indicator and the final state of the grid. The
        numeric indicator should be 1 if the target is found, and -1 if it is
        not found. The final state of the grid should display the marked cells
        according to the path found by the search algorithm. For instance,
        2
        if the target is found, the function could return (1, grid), where 1
        represents the successful search and grid represents the final state of
        the board.
        • When adding to your queue or stack, follow this order: Right, Down,
        Left, Up, or the reverse. The order can be reversed as well.
        • Return the shortest path from the source to the target using a list of
        如有需要,請加QQ:99515681 或WX:codehelp

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