Do the following exercises, saving your answers, both text and code, in a Python 3 Juypter notebook.

1. The earliest psychologists studied the human mind using introspection. Would this be a good way for researchers to gain insight into human cognitive processes for the purpose of modelling those processes? (Please give a one-to-two-paragraph answer.)

2. Implement a local search formulation of a TSP problem and use the AIMA hill-climbing and simulated annealing implementations to solve it. As part of your solution, do the following:

   • Describe your state and action representations.
   • Configure your system so that you can build TSP worlds of any number of cities with random distances between them. The distances are symmetric, that is the distance from city A to city B is the same as that from B to A. The distances can be random and thus may not be geographically coherent in 2D space. You may assume that the cities are completely interconnected.
   • Explain which local search algorithm works better and why.

   This is a famously hard problem, so there is no clear way to determine the optimal answer; program your system to do its best and see how well it can do on some large, random city configurations.

3. Formulate a course-scheduling domain and implement it using the AIMA constraint satisfaction framework. Note the following:

   • Your solution should represent 6–7 courses (e.g., cs108, cs112, …), 4–5 faculty (e.g., dschuurman, adams, …) course assignments for all courses (e.g., dschuurman teaches 108, adams teaches cs112, …), 4–5 (disjoint!) time slots (mwf900, tth1030, …) and the 2 CS classrooms (e.g., nh253, sb382).
   • The constraints are that:
     • each course should be offered exactly once by the assigned faculty member.
     • a faculty member can only teach one thing at a time.
     • a room can only have one class at each time.

   There are a number of ways to formulate this; choose one, document why you chose it, and demonstrate that it works.

Checking in

Submit your Jupyter notebook solution to cs344/homework1.ipynb (see the sample notebook file in the course repo). We will grade your work according to the following criteria:

• 20% — Exercise 1
• 40% — Exercise 2
• 40% — Exercise 3

See the policies page for homework due-dates and times.