Develop an AI technique and solve real-world case study

Reference no: EM133035211

MFA501 Solve an AI Problem set - Laureate International Universities

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

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 languageWe assumed the image is represented in a 2D matrix as follows:

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

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.

Attachment:- Assessment _Scenario.rar

Reference no: EM133035211

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