CS6262 Assignment 1 Solution

into the goal state.
Your Agent
Starter Code
In BlockWorldAgent.py, your solve() method will have two parameters: the initial configuration of blocks, and the goal configuration of blocks. Configurations will be represented by lists of lists of characters, where each character represents a different block (e.g. “A” would be Block A). Within each list, each subsequent block is on top of the previous block in the list; the first block in the list is on the table. For example, this list would represent the configuration shown above: two stacks, one with D on B and B on C, and the other with just A:
[["C", "B", "D"], ["A"]]
Returning Your Solution
Your solve() method should return a list of moves that will convert the initial state into the goal state. Each move should be a 2-tuple. The first item in each 2-tuple should be what block is being moved, and the second item should be where it is being moved to—either the name of another block or “Table” if it is to be put into a new pile.
For example, imagine the following initial and target state:
Initial: [["A", "B", "C"], ["D", "E"]]
Goal: [["A", "C"], ["D", "E", "B"]]
Put in simple terms, the goal here is to move Block B from the middle of the pile on the left and onto the top of the pile on the right.
Given that, this sequence of moves would be an acceptable solution:
("C", "Table")
("B", "E")
("C", "A")
Submitting Your Solution
To submit your agent, go to the course in Canvas and click Gradescope on the left side. Then, select CS7637 if need be.
You will see an assignment named Mini-Project 2. Select this project, then drag your BlockWorldAgent.py file into the autograder. If you have multiple files, add them to a zip file and drag that zip file into the autograder.
When your submission is done running, you’ll see your results.
How You Will Be Graded
Your agent will be run against 20 pairs of initial and goal configurations. 8 of these will be the same time every time your agent is tested; these are present in the original BlockWorldAgent.py file. The remaining 12 will be randomly generated, with up to 26 blocks each.
You can earn up to 40 points. You will earn 1 point for each of the 20 pairs of configurations you solve correctly (meaning that your solution does in fact transform the initial state into the goal state), and an additional point for each of the 20 configurations you solve optimally (in the minimum number of moves).
Your Report
How does your agent work? Does it use Generate & Test? Means-Ends Analysis?
Some other approach?
How well does your agent perform? Does it struggle on any particular cases? How efficient is your agent? How does its performance change as the number of blocks?
Does your agent do anything particularly clever to try to arrive at an answer more efficiently?
How does your agent compare to a human? Does your agent solve the problem the same way you would?
Tip: Remember, we want to see how you put the content of this class into action when designing your agent. You don’t need to use the principles and methods from the lectures precisely, but we want to see your knowledge of the content reflected in your terminology and your reflection.
Submission Instructions
Complete your assignment using JDF, then save your submission as a PDF. Assignments should be submitted to the corresponding assignment submission page in Canvas. You should submit a single PDF for this assignment. This PDF will be ported over to Peer Feedback for peer review by your classmates. If your assignment involves things (like videos, working prototypes, etc.) that cannot be provided in PDF, you should provide them separately (through OneDrive, Google Drive, Dropbox, etc.) and submit a PDF that links to or otherwise describes how to access that material.
This is an individual assignment. All work you submit should be your own. Make sure to cite any sources you reference, and use quotes and in-line citations to mark any direct quotes.
Late work is not accepted without advanced agreement except in cases of medical or family emergencies. In the case of such an emergency, please contact the Dean of

Students.
Grading Information
Your report is worth 50% of your mini-project grade. As such, your report will be graded on a 40-point scale coinciding with a rubric designed to mirror the questions above. Make sure to answer those questions; if any of the questions are irrelevant to the design of your agent, explain why.
Peer Review
After submission, your assignment will be ported to Peer Feedback for review by your classmates. Grading is not the primary function of this peer review process; the primary function is simply to give you the opportunity to read and comment on your classmates’ ideas, and receive additional feedback on your own. All grades will come from the graders alone. See the course participation policy for full details about how points are awarded for completing peer reviews.