Csci 1200 Data Structures Spring 2018 Homework 6 Battleship

Csci 1200 Data Structures Spring 2018homework 6 Battleship Recurs

Implement a program to solve ship placement puzzles inspired by the Battleship game. The program should accept one or two command line arguments: a puzzle board file, and optionally, a flag to find all solutions. The code must place ships on the board satisfying row and column cell counts, with rules including no adjacent ships (including diagonally). The input file may specify constraints on certain cells or have unknown row/column sums and ship types that can vary in length from 1 to 7 cells. The implementation must use recursion in a non-trivial way, handle error checking, and produce solutions formatted exactly as specified. The program should output either a single solution, all solutions, or indicate no solutions, as per input arguments. Performance should be analyzed with order notation, focusing on variables such as board dimensions, number of ships, occupied cells, constraints, and unknowns. The solution must be complete, optimized, and capable of handling complex puzzles within reasonable time. Extra credit is available for efficient contest submissions with detailed performance analysis and interesting test cases.

Sample Paper For Above instruction

The classic game of Battleship has long been a popular pastime, not only as a competitive game but also as a puzzle that challenges logical reasoning and spatial visualization. This assignment revolves around creating an algorithm that autonomously solves Battleship-like puzzles by placing ships on a grid, adhering to constraints derived from the game rules and additional puzzle-specific conditions. The central challenge is to fill a grid with ships of varying lengths, ensuring all the rules are satisfied: ships must not be adjacent, counts per row and column must be met, and any imposed constraints on cell contents or ship positions must be respected. Furthermore, the complexity is increased through optional features such as uncertain row and column sums and variability in ship types, which require the program to handle ambiguity and generate multiple valid solutions.

The task involves reading a specially formatted input file that defines the puzzle parameters, including board size, ship counts, and constraints. The core logic demands a recursive backtracking approach that explores ship placements systematically, pruning invalid configurations early to avoid unnecessary computations. This recursive approach must incorporate advanced techniques such as constraint propagation, adjacency checking, and dynamic ship placement, which makes the solution non-trivial and demonstrates a deep understanding of recursion and combinatorial problem-solving.

Performance analysis is crucial, especially given the computational expense typical of such puzzles. The implementation should evaluate the problem's complexity in terms of variables like grid size, number of ships, imposed constraints, and unknown variables. By examining these factors, one can derive order notation estimates to predict the scalability and efficiency of the algorithm. For instance, the complexity may grow exponentially with the number of ships and grid size, but optimized pruning strategies and heuristic ordering can significantly reduce the search space.

Designing this solver also requires meticulous attention to format correctness, including exact output formatting, suitable ASCII-art visualization of solutions, and consistent handling of multiple solutions. Additional considerations include error checking, handling input variability, and ensuring the program's robustness under complex scenarios. For extra credit, performance optimization techniques such as memoization, advanced pruning, or heuristic ordering can be incorporated. Overall, this project integrates algorithm design, problem parsing, recursive search, and complexity analysis, culminating in a comprehensive exercise in problem-solving and software engineering.

References

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