Teaching students different local search algorithm requires careful definition of a non-convex problem and appropriate scaffolding. In this submission, we share our assignment in our artificial intelligence course, which uses Ackley Surface and scaffolding for students to implement 1) Stochastic hill climbing with re-starts, 2) Simulated Annealing, and 3) Local Beam Search. Additional resources are provided to instructors to adapt and modify the local search landscape function as well as resources to teach students the concepts needed for completing the assignment.
Local search algorithms
The target audience of this assignment is undergraduate CS or engineering students taking an AI course.
|Strengths||Adaptable, modular, scaffolded. An instructor handout has been provided to outline the background information that students need before working on the assignment. Additionally, the instructor handout provides option to change the landscape to other non-convex functions.|
|Weaknesses||Additional local search algorithms can be added to the assignment.|
|Dependencies||Python programming language|
The scaffolded instructions in the handouts will make the learning experience smooth and engaging.
|Instructor Handout||Instructor Handout|