Reference no: EM133035186
MFA501 Solve an AI Problem set - Laureate International Universities
Assessment: Solve an AI Problem set
Project and supporting document
Learning Outcome 1: Formulate key mathematical concepts used in Artificial Intelligence.
Learning Outcome 2: Interpret and transmit standard mathematical notations and terminologies in statistics, probabilities, linear algebra, vectors, matrices, differential calculus, and logical reasoning.
Learning Outcome 3: Compute accurately standard computations in statistics, probabilities, linear algebra, vectors, matrices and differential calculus.
Task Summary
The second assessment allowed you to show your understanding on how to choose the correct AI method to solve simple case studies. Assessment 3 has been designed to assess your ability in choosing and using the best AI methods to solve larger and more challenging problems. The AI method that you will be using is more challenging to implement as compared to those in the second assessment. Solving real-world problems using AI techniques requires more sophisticated skills, and you will demonstrate these in this assessment.
Your subject facilitator will provide you with the scenario/topic/focus for this assessment during the first few weeks of this subject. Regardless of the content that will be provided, you will have to first understand the problem and formulate it in a suitable manner for AI techniques. The next step is to choose the correct AI method to solve the problem. As a part of development, you have to test and analyse the performance of the AI technique to ensure that it gives optimal results. In summary, you will be required to program an AI method, solve a problem using it, and write a reflective report.
Context
Facilitator will advise you of the exact task you are required to complete during the first few weeks of the subject.
Task Instructions
The source code that you will be submitting should be free of build warnings, build errors, and all intermediate files (.obj, .pdb, etc), crashes, and errors (compile, run-time, logical, etc.). Your code should be structured and written with the best practices in the field of programming. There should be enough number of comments in the source files to show your understanding of the program. Any third-party code should be appropriately attributed.
When you submit the electronic version of your project make sur to use the following names:
• Name the source code folder as: Source - Student Name
• Name the solution as: YourGameName.sln
Learning Outcome 1: Formulate key mathematical concepts used in Artificial Intelligence.
Learning Outcome 2: Interpret and transmit standard mathematical notations and terminologies in statistics, probabilities, linear algebra, vectors, matrices, differential calculus, and logical reasoning.
Learning Outcome 3: Compute accurately standard computations in statistics, probabilities, linear algebra, vectors, matrices and differential calculus.
Assessment: Solve an AI Problem set
Task Summary
In this assessment, you are expected to implement an AI algorithm to reconstruct a binary image represented in a 2D array. This assessment is to be completed individually and you are to submit programs and supporting documents via the assessment link in Blackboard. Please refer to the Task Instructions for details on how to complete this assessment.
This assessment is intended to determine:
• Your understanding of the theories and mathematical notations covered in Module 1 to 11
• Your ability to formulate and frame a simplified real-world problem for an AI problem solving technique
• Your ability to choose a suitable AI technique for the problem
• Your ability to implement an AI problem solving technique in a modern programming language
Context
This summative project assesses your skills to use the mathematical models covered in Module 1 to 11 to develop an AI technique and solve a simplified real-world case study. You are required to develop an algorithm to reconstruct a binary image. To represent a binary image, you can use an array. For instance, the following binary image can be reconstructed with a 10x10 matrix:
Your learning facilitator will assign you one of the following algorithms to use as the base algorithm for this problem:
• Hill climbing
• Simulating annealing
• Genetic algorithm
Note that your algorithm does not have to be complete, meaning that it does not have to find the best solution 100% of the times. You can find an approximate of the final image as long as the algorithm shows consistent improvement. You will be minimizing the objective function as much as
you can. After implementation and testing your algorithm, write a reflective analysis detailing the experience of the development process. The report needs to be at least 1000 words in length and include the following sections:
• Overview
• Justifications and elaborations on the mathematical approaches and models used to solve the cases study
• Justifications and elaborations on the programming methods and practices used to implement the mathematical approaches and models
• What went right
• What went wrong
• What you are not sure about
• Conclusion
Attachment:- Solve an AI Problem set.rar