Implementation of a kind of goal based agent: Problem solving agent.
The program has two types of searches implemented: Uninformed (IDS) and Informed (A*) search. A-star search has four different heuristics: two good and two not-so-good heuristics to analyze how a good heuristic can give better results.
Two problems formulated are: Maze solver (Problem.py) and N-queeens Game (nqueens.py). For maze solver, Program analyzes how one search algorithm (IDS) is not adequate, whereas the other search algorithm(A-star) gives us the best result. Any search problem formulated within these five terms: Initial state, Actions, Result/Transition Function, Goal test, Path cost, will be solved by this Problem Solving Agent (Provided Informed or Uninformed Search algorithm is adequate to solve the problem).
Two problems formulated are, Maze solver (Problem.py) and N-queeens Game (nqueens.py).
Maze Solver
Given a maze of 0s representing obstacles and 1s representing path, the program finds the shortest path (with A star search) and a path (not necessarily shortest, with IDS) from top left corner to right bottom corner.
- File to run: Problem.py
- sys arguments: input maze file of format .txt (check sample inputs)
N Queens Problem
Given n-queens problem where number of queens is specified by the user, the program finds the solution. What is the N-queens problem: https://en.wikipedia.org/wiki/Eight_queens_puzzle
- File to run: nqueens.py
- Sys argument: Number of queens to be placed on the board