Artificial Intelligence Interview Questions & Answers- Part 3

Answer:

The Tower of Hanoi is a mathematical puzzle demonstrating the use of recursion to develop an algorithm to solve a specific problem. The Tower of Hanoi puzzle can be solved by employing a decision tree and a breadth-first search (BFS) algorithm in AI.

Answer:

An expert system is an Artificial Intelligence program that possesses specialized knowledge in a particular domain and utilizes that knowledge to provide informed responses. These systems are designed to substitute for human experts and exhibit the following characteristics:

1. High performance
2. Prompt response time
3. Reliability
4. Understandability

Answer:

The advantages of employing an expert system include:

• Consistency
• Retention of knowledge
• Diligence
• Logical reasoning
• Multiple areas of expertise
• Ability to reason and provide explanations
• Rapid response
• Unbiased decision-making

Answer:

The A* algorithm is a widely used computer algorithm employed for finding the optimal path or traversing a graph, aiming to discover the most efficient route between different nodes or points.

Answer:

The breadth-first search (BFS) algorithm is utilized for exploring tree or graph data structures. It starts from the root node, visits neighboring nodes, and progressively moves to the next level of nodes. It generates one level of the tree at a time until the desired arrangement or solution is found. BFS ensures that the shortest path to the solution is obtained by utilizing the FIFO (first-in, first-out) data structure.

Answer:

The depth-first search (DFS) algorithm operates based on the LIFO (last-in, first-out) principle. It employs recursion and a stack data structure, processing nodes in a different order compared to BFS. The path from the root to leaf nodes is stored linearly in each iteration, resulting in a specific space requirement.

Answer:

In the bidirectional search algorithm, the search starts simultaneously from both the initial state and the goal state. The searches converge to identify a common state that connects the initial and goal states. Each search is performed up to only half of the total path, resulting in a more efficient search.

Answer:

The iterative deepening depth-first search algorithm involves repeating the search process for increasing levels of depth, such as level 1 and level 2, until the solution is found. Nodes are generated and saved in a stack until a single goal node is encountered.

Answer:

Fuzzy logic finds applications in various fields, including:

• Facial pattern recognition
• Control of air conditioners, washing machines, and vacuum cleaners
• Antiskid braking systems and transmission systems in vehicles
• Management of subway systems and unmanned helicopters
• Weather forecasting systems
• Project risk assessment
• Medical diagnosis and treatment planning
• Stock trading

Answer:

Partial-order planning involves solving a problem by approaching it sequentially to achieve the desired goal. The plan specifies all the actions required but only determines the order of actions when necessary.

Answer:

FOPL stands for First-Order Predicate Logic. It is a collection of formal systems where statements are divided into subjects and predicates. The predicates modify or define the properties of the subject, and each predicate refers to only one subject.

Answer:

The key difference between inductive, deductive, and abductive Machine Learning is as follows:

• Inductive machine learning learns from a set of instances to draw conclusions, such as statistical ML methods like K-nearest neighbor (KNN) or Support Vector Machine (SVM)
• On the other hand, deductive machine learning derives conclusions and improves them based on previous decisions. It utilizes decision trees in ML algorithms.
• Abductive machine learning uses deep learning techniques to derive conclusions based on various instances like deep neural networks.

Answer:

The different algorithm techniques in Machine Learning are as follows:

• Supervised Learning
• Unsupervised Learning
• Semi-supervised Learning
• Reinforcement Learning
• Transduction
• Learning to Learn

Answer:

Deep Learning is Machine Learning subset that includes the creation of artificial neural networks with multiple layers. It leverages self-learning capabilities based on previous instances and offers high accuracy in tasks.

Answer:

Below are the differences between supervised, unsupervised, and reinforcement learning:

• Supervised learning contains both predictors ad predictions, unlike unsupervised learning, that only contains predictors and reinforcement learning that can achieve state-of-the-art results in any task.
• Supervised learning uses linear and logistic regression, support vector machine, and Naive Bayes. On the other hand, unsupervised learning uses K-means clustering and dimensionality reduction algorithms. In comparison, reinforcement learning uses Q-learning, state-action-reward-state-action (SARSA), and Deep Q Network (DQN).
• Supervised learning is used for image recognition, speech recognition, forecasting, etc. Unsupervised learning is used for pre-processing data and pre-training supervised learning algorithms. Whereas reinforcement learning is utilized for warehouses, inventory management, delivery management, power system, financial systems, etc.

Answer:

Naive Bayes is a powerful predictive modeling algorithm in Machine Learning. It encompasses a set of algorithms based on the common principle of Bayes’ Theorem. The fundamental assumption of Naive Bayes is that each feature independently contributes equally to the outcome.

Answer:

The Backpropagation Algorithm is an iterative Neural Network algorithm primarily used for processing noisy data and identifying unrecognized patterns. It consists of three layers: input, hidden, and output layers. The input layer receives input values, the hidden layer processes the data, and the output layer produces the final output. Backpropagation is commonly used in tasks such as speech recognition, image processing, and optical character recognition (OCR).

Answer:

In Artificial Intelligence, weights determine the influence of inputs on the output in neural networks. During training, algorithms adjust these weights to minimize errors and achieve the desired output. For example, in the Backpropagation algorithm, if there is an error in the output, the algorithm propagates the error backward through the network, adjusting the weights to optimize the output.

Answer:

A perceptron is an algorithm designed to simulate the human brain’s ability to understand and classify information. It is used for supervised classification, where input data is classified into several possible non-binary outputs.

Answer:

Yes, KNN is different from K-means clustering in the following ways:

• KNN is supervised, whereas K-means clustering is unsupervised
• KNN often uses as classification algorithms, while K-means clustering is used as clustering algorithms.
• KNN is a simple and minimal training model, but K-means clustering is a hard and exhaustive training model.
• KNN is used in the regression and classification of the known data; in contrast K-means clustering is used in population demographics, anomaly detection, market segmentation, social media trends, etc.