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JA6. Learning Journal 6

The Learning Journal is a tool for self-reflection on the learning process. The Learning Journal will be assessed by your instructor as part of your Final Grade.

Answer the following questions

1. Describe what you did. You need to describe what you did and how you did it

This was the 4th week of this course; it was all about features and constraints. I started by reading the material provided by the learning guide and then started working on the discussion assignment. The discussion assignment was solving N-queens problem using constraint satisfaction problem (CSP). The programming assignment involved solving the problem of coloring neighboring regions with different colors using a CSP solver.

2. Describe your reactions to what you did

I found the discussion assignment to be quite interesting. I had to think about the problem in a different way than I usually do. I had to think about the problem in terms of variables, domains, and constraints. Technically speaking, we have been working with constraints for a very long time, but it was never formally defined like how we did today.

3. Describe any feedback you received or any specific interactions you had while participating discussion forum or the assignment Discuss how they were helpful

For the discussion assignment, I expected more participants to include coding or diagrams to explain their stand on the topic; however, only a few did. I found the questions to be open-ended which made it hard to really evaluate the responses. I think it would be better if we specified a small N, like 2 or 3, so that the expected diagrams to be of a manageable size and easier to evaluate.

4. Describe your feelings and attitudes

I have mixed feelings; I am happy to have formally defined the CSP and solved problems using its techniques. I am worried that I started losing track of the topics we have covered so far. I found the algorithms mentioned in this week are complex to grasp, and I spent more than 12 hours on the programming assignment alone; I learned a lot, but I am worried about the upcoming weeks.

5. Describe what you learned. You can think of one or more topics and explain your understanding in writings

This week was all about constraint satisfaction problems (CSP). I learned that a CSP consists of a set of variables, a domain for each variable, and a set of constraints. A variable is a symbol that can take on values from a set of all possible values called the domain subject to passing the constraints which specify combinations of assignments that are legal (Poole & Mackworth, 2017).

A solution is a total assignment that satisfies all of the constraints. I learned that there are multiple methods to solve a CSP, such as generate-and-test algorithms, local search, hill climbing, stochastic local search, simulated annealing, and minimax algorithm. I also learned that the consistency algorithms are best thought of as operating over a constraint network defined as a bipartite graph, with the two parts consisting of the variable nodes and the constraint nodes; each arc goes from a variable node to a constraint node (Poole & Mackworth, 2017).

Generate-and-test algorithms use brute force techniques to start trying solutions one by one and passing constraints one at a time until a solution is found. Local search algorithms, on the other hand, do some analysis on the problem before trying their first guess; hence, they start with a random solution and then incrementally change the solution until a solution is found that meets the constraints. Hill climbing is an example of a local search algorithm that starts with a random solution within a specific range and then incrementally changes the solution as it gets closer to a solution or backtracks if it gets further away from a solution; It may get trapped in a local maximum or minimum; hence, the selection of neighbors uses randomization to avoid this (Taipala, 2014).

Simulated annealing is a stochastic local search algorithm that uses randomization and a temperature variable to control the amount of randomness in the selection of neighbors. The temperature slowly decreases over time, as we get closer to a solution, and also avoid getting trapped in a local maximum or minimum. The minimax algorithm is used in two-player turn-based games such as Tic-Tac-Toe, Backgammon, Mancala, Chess, etc. In Minimax, the two players are called maximizer and minimizer; the maximizer tries to get the highest score possible while the minimizer tries to do the opposite and get the lowest score possible (Taipala, 2014).

6. Did you face any challenges while doing the discussion or the development assignment? Were you able to solve it by yourself?

The programming assignment was particularly challenging. I spent more than 12 hours on it alone. The problem requires taking a map of neighboring regions and coloring them with different colors such that no two neighboring regions have the same color. Each time you start coloring a connected region, you must follow the colors in a specific order; and start again with first color in the next isolated region.

I solved the problem by converting the map into a graph where each region is a node and connections between regions are edges. I then used a CSP solver to solve the problem called colorChooser that takes in a graph and a vertex to color and returns the color to use.

I also had a problem with the programming assignment as I have little experience with java, hence I had to do it with a familiar language first (Python) and then convert it to Java. I also implemented my own data structures for for the Graph and other classes rather than using built-in ones which is a great learning experience.

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