Artificial Intelligence Program
$250-750 USD
Paid on delivery
(Modified Russel&Norvig, Page 119, 3.30) The traveling salesperson problem (TSP)
can be solved with the minimum- spanning-tree (MST) heuristic, which estimates the
cost of completing a tour, given that a partial tour has already been constructed. The MST
cost of a set of cities is the smallest sum of the link costs of any tree that connects all the
cities.
1 Model this problem as a search problem, including, how to define the state space,
start states, operators, path cost and goal state.
2 Show that how the MST heuristic can be derived from a relaxed version of the TSP,
and it is admissible.
3 Write a problem generator for instances of the TSP where cities are represented by
random points in the unit square. (You may use existing code with reference)
4 Find an efficient algorithm for constructing the MST, and use it with the A * graph
search to solve instances of the TSP. (You may use existing code with reference)
5 Implement an A* search algorithm to solve the problem. How far can you go with the
A* approach, as the problem size (#cities increases) increases, record the time
(measured by the number of nodes explored) and space (measured by the maximum
number of nodes stored in the open list during the program execution) needed for a
certain size of problem.
6 When the problem size increase, A* runs out of memory. So implement the SMA* to
solve the memory limit problem. Put a fixed limit on the number of nodes that you
can store in open list, show that with the same amount of memory available, SMA* is
able to solve larger size of problem while A* cannot solve. What is the solution found
by SMA*, is it optimal?
7 Sometimes you need find a solution within limited amount of time, you need trade off
the computation time with the solution quality. Modify the A* to an anytime A* and
try it with different w values, record the results you get. Show the multiple different
solutions found by the anytime A* as search time increases.
8 Implement a GUI that allows the choices of #cities, memory size (#nodes) and the
algorithm to run, and demonstrate the solution both graphically and numerically.
What to handled in:
1. A report that describes the design to solve the problem (Question 1). The
experimental results and answers to questions 4, 5, 6 and 7. Give reference to any
type of help you have used. (Hard copy of the report submitted by Due date)
2. The source code with documentation and instruction of how to run it. (Uploaded to
mycourse by Due date).
3. Demo your program to the instructor by Demo Due date.
Grading Policy:
1. Basic documentation on how to solve this problem using search. (20pt)
2. Implementation of the problem instance generator. (5pt)
3. Implementation of an efficient algorithm for constructing the MST. (5pt)
4. Working program with at least A* search and experiment results (25 pt)
5. SMA* works and show experiment results (20pt)
6. Anytime A* works and show experiment results (25pt)
7. A GUI Implementation (Extra 20 pt)
Search Code Available
Download the Search package (in Java) from MyCourse, which includes the
implementation of a set of general search framework. You can use this package as the
foundation of your coding, or you can choose to start from scratch if you prefer another
programming language.
To compile: javac search/*.java
To execute: java search/SearchApp
There are also search code available from the textbook website:
[login to view URL]
Note: these packages are complimentary and you are solely responsible for your own
code to work correctly and in time.
Project ID: #6573066
About the project
7 freelancers are bidding on average $432 for this job
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