Show the source code and out puts
CSIT-357: Artificial Intelligence Assignment 2 - Implementation of Search Methods This lab assignment focuses on search methods in Al. You are required to work in teams of 3 to 4 students and implement any one uninformed (deterministic) search method and any one informed (heuristic-based / stochastic) search method in any programming language, e.g., C, Java etc. The search methods are listed below in the two different categories. Please note that you must implement at least one between Uniform Cost and A since they involve the most computation. Uninformed / Deterministic Search Methods Depth First Search Breadth First Search Depth Limited Search Uniform Cost Search (Branch and Bound) Informed / Stochastic Search Methods (with heuristics) Greedy Search (Best First) A* Search Hill Climbing Search (Gradient Descent) You can follow the traveling salesperson example studied in class as the domain for the search problem. You can follow the traveling salesperson example studied in class as the domain for the search problem, Alternatively, you can implement the search methods in the context of any other application, e.g., building a GPS, finding parking, searching for a web page etc. The program requirements are as follows. Input: Start State. Intermediate States, Goal Test, Path Cost for both informed and uninformed methods Search Heuristic for informed methods. Processing: Any one uniformed and any one informed search method to go from the start state to the goal state. Output: Path(s) traced from start state to goal state, Total path cost. It is not required to develop a GUI in the program, it is sufficient to show the paths by listing the states from start to goal as a program output. You must present your assignment in class using slides and demo and bring a printout of the same slides with demo snapshots, one per team. Slides must include: Explanation of your Search Problem, Path Cost and Heuristics, Search Map (can be drawn by you in ppt. etc..) with and without heuristics, Search Tree for each method (also drawn by you, you need not automate the actual tree in the program), Relevant Parts of Source Code for the Searches, Demo of Searches live, and the demo snapshots on slides, CSIT-357: Artificial Intelligence Assignment 2 - Implementation of Search Methods This lab assignment focuses on search methods in Al. You are required to work in teams of 3 to 4 students and implement any one uninformed (deterministic) search method and any one informed (heuristic-based / stochastic) search method in any programming language, e.g., C, Java etc. The search methods are listed below in the two different categories. Please note that you must implement at least one between Uniform Cost and A since they involve the most computation. Uninformed / Deterministic Search Methods Depth First Search Breadth First Search Depth Limited Search Uniform Cost Search (Branch and Bound) Informed / Stochastic Search Methods (with heuristics) Greedy Search (Best First) A* Search Hill Climbing Search (Gradient Descent) You can follow the traveling salesperson example studied in class as the domain for the search problem. You can follow the traveling salesperson example studied in class as the domain for the search problem, Alternatively, you can implement the search methods in the context of any other application, e.g., building a GPS, finding parking, searching for a web page etc. The program requirements are as follows. Input: Start State. Intermediate States, Goal Test, Path Cost for both informed and uninformed methods Search Heuristic for informed methods. Processing: Any one uniformed and any one informed search method to go from the start state to the goal state. Output: Path(s) traced from start state to goal state, Total path cost. It is not required to develop a GUI in the program, it is sufficient to show the paths by listing the states from start to goal as a program output. You must present your assignment in class using slides and demo and bring a printout of the same slides with demo snapshots, one per team. Slides must include: Explanation of your Search Problem, Path Cost and Heuristics, Search Map (can be drawn by you in ppt. etc..) with and without heuristics, Search Tree for each method (also drawn by you, you need not automate the actual tree in the program), Relevant Parts of Source Code for the Searches, Demo of Searches live, and the demo snapshots on slides
