CONCEPTS IN ARTIFICIAL INTELLIGENCE CAI104

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Artificial Intelligence is also known as AI and is a field of computer science that deals with Machine Learning. The main objective of AI is to make machines intelligent and solve problems the same as humans. AI is all about Learning, Thinking, Searching, Designing, and Storing things to recognize and identify the patterns. The machine must learn through experiences and mistakes.
A machine will be called Artificial Intelligence if it holds all the characteristics of human beings and behaves and solve problems just like human beings.

AI has the advantage of solving problems due to its effective methods solving problems.

The heuristic method aids in the understanding of a problem and the development of a solution based only on experiments and trial and error techniques. These heuristics, on the other hand, do not always deliver the best optimal solution to a particular situation. Instead, these unquestionably provide effective answers for achieving immediate objectives. As a result, when traditional methods fail to provide an effective solution to a problem, developers turn to them. Heuristics are used with optimization algorithms to improve efficiency because they only provide time-efficient solutions at the expense of accuracy.

In AI, searching is one of the most important approaches to solving any problem. These searching algorithms are used by rational agents or problem-solving agents to locate optimal solutions. These problem-solving agents frequently use atomic representation and are goal-oriented. Furthermore, based on the quality of the solution they supply, these searching algorithms have features such as completeness, optimality, time complexity, and space complexity.

Types of Searching Algorithms-

This is an Individual assignment and every individual would be given 50minutes to undertake the exam. With this assessment, you should demonstrate your understanding of AI history and theories as well as the ability to tackle and solve challenging problems using AI search methods. The assessment is intended to test:

Instructions-

In this assessment, you will be given a workbook that includes a set of questions to complete. You have to answer and show your work for the questions in the workbook. This assessment should be done individually.
With this assessment, you demonstrate your ability to tackle and solve several case studies using AI search methods, knowledge representation techniques, AI theories, logic, and reasoning approaches that you have learned throughout Modules 1-8.

Question1 – Use the search methods indicated below (1.1 – 1.5), to traverse the following state space tree (30%):
1.1 Breadth-first search
1.2 Depth-first search
1.3 Iterative deepening depth-first search
1.4 Greedy best-first search
1.5 A*

Question 2 – First fill out the following truth table, which will be your reference for the main parts of this question (30%):


Prove the following expressions using truth tables:

2.1 (p q) ∧ (p r) p (q ∧ r)

2.2 (p ∨ q) ∧ (p ∨ q) q

2.3 A = { p q,
q p,
p q }
C = p q

2.4 A = { q r,
q p,
(r p) }
C = p
Write a paragraph discussing the potential applications of logic. Give a real example in which the above variables (A, C, p, q, and r) can be replaced with real-world activities.

Question 3 – Translate the following sentences into first-order logic (just translate, no verification required for this question) (20%):
3.1 All scientists are smart.
3.2 There exists a smart scientist.
3.3 No scientist loves politics
3.4 Every scientist who publishes articles also publishes books

Question 4 – Translate the following expressions into logic. Then, verify whether the following
sentences and reasoning are valid (20%):
4.1 “If I'll go to the university then I'll take a taxi, or if I'll take a taxi then I'll go to the university”.
4.2 It is not true that Alex bought a car, or if he bought a car then he had money. Therefore Alex didn't have money.
Upon the completion of questions 4.1 and 4.2, please write a paragraph discussing the potential applications of reasoning.

Assessment3 – Project CAI104
In this assessment, you are to program an AI agent to solve a real-world, challenging case study. This assessment is done individually and you are to submit programs and supporting documents.
You are to create a robot path planner that is able to find an optimal path to navigate an
environment and reach a target. By completing this assessment, you will show your skills in
leveraging the best AI methods to solve a simplified real-world problem.

The maze can be seen in the image below. It can be seen that there are 12 rows and 24 columns, meaning there is a total of 288 blocks on the map. There are four different types of blocks in this map as follows:
Green: wall
White: space (void)
Red: initial position of the robot
Blue: the target

Instructions –
⦁ Write a reflective report detailing the experience of the development process.
The report should be 2000 words (10%+-) in length and include the following sections:
Overview
What went right
What went wrong
What you are not sure about
Conclusion
⦁ Appropriate, effective, and correct usage of pseudocode to design and develop and
algorithm
⦁ Effective use of search and or optimization algorithms in AI.
⦁ Good selection of search and/or optimization algorithms.

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