Artificial Intelligence Model Question Solution BITM 5th Sem
Brief answer questions:
1.) What is Cognitive Science?
Thank you for reading this post, don't forget to subscribe!Cognitive Science is the interdisciplinary study of the mind and intelligence, combining knowledge from psychology, neuroscience, linguistics, philosophy, computer science, and anthropology to understand how humans think, learn, and process information.
2.) How does Rule base agent work?
A rule-based agent works by using a set of predefined “if-then” rules to determine actions based on current conditions or percepts from the environment. It selects actions by matching inputs to these rules to produce a response.
3.) Define Omniscience.
Omniscience refers to the hypothetical ability of an agent to have complete and perfect knowledge of all aspects of the environment and consequences of its actions, which is ideal but not possible in real-world AI systems.
4.) Define Constraint satisfaction problem with example.
A Constraint Satisfaction Problem (CSP) is a type of problem where the goal is to find a solution that satisfies a set of constraints or conditions.
5.) What is CNF form?
CNF (Conjunctive Normal Form) is a way of structuring logical expressions where a formula is composed of a conjunction (AND) of disjunctions (ORs) of literals. It is commonly used in automated reasoning and propositional logic.
6.) How do you define fuzzy logic?
Fuzzy logic is a form of logic used in AI that allows reasoning with uncertain or imprecise information by assigning degrees of truth ranging from 0 to 1, rather than using binary true/false logic.
7.) Define Version space.
Version space refers to the subset of all hypotheses that are consistent with the observed training data in machine learning. It represents the possible hypotheses that can explain the input-output examples provided.
8.) What is rational agent?
A rational agent is an AI system that always selects actions that maximize its performance measure based on the knowledge it has, acting to achieve the best possible outcome.
9.) What do you mean by Machine Learning?
Machine Learning is a branch of artificial intelligence where computers learn from data to identify patterns and make decisions or predictions without being explicitly programmed for each task.
10.) “NLP is important field in AI” Why?
Natural Language Processing (NLP) is important in AI because it enables machines to understand, interpret, and respond to human language, making human-computer interaction more natural and effective in applications like chatbots, voice assistants, and translation tools.
Short Answers Questions
11.) How syntactic and semantic analyses are performed in natural language processing?
In Natural Language Processing (NLP), syntactic analysis involves checking the grammatical structure of sentences using parsing techniques to ensure the correct arrangement of words. It builds a syntax tree to represent the sentence structure.
Semantic analysis, on the other hand, focuses on understanding the meaning of the sentence by analyzing relationships between words, resolving ambiguities, and identifying the roles of entities in the sentence. Together, they allow machines to understand both structure and meaning of language.
12.) What do you mean by Rational Agent? What are differences between Utility based Agent and Model based Agent?
A rational agent is an AI system that selects actions that maximize its performance based on the information it has. It aims to achieve the best possible outcome.
- A Utility-based agent evaluates multiple possible states and chooses the one with the highest utility (satisfaction or benefit).
- A Model-based agent maintains an internal model of the environment and uses it to predict outcomes of its actions, even when not all information is available.
- The key difference is that utility-based agents focus on choosing the best outcome, while model-based agents focus on understanding and simulating the environment.
13.) What is state space representation? Illustrate with one example.
State space representation is a mathematical model used in AI to describe all possible configurations (states) of a problem, the actions that can be taken from each state, and the goal state.
Example: In the 8-puzzle problem, each arrangement of tiles is a state, moving a tile is an action, and the goal state is the configuration where all tiles are in order.
14.) What is forward chaning? Explain with appropriate example.
Forward chaining is an inference method in rule-based systems where reasoning starts from known facts and applies inference rules to derive new facts until a goal is reached.
Example:
If we know:
- Rule: If a person is coughing and has fever, then they may have flu.
- Facts: John is coughing and John has fever.
Forward chaining applies the rule to deduce: John may have flu.
15.) “Some time quantity became quality” Justify statement by the help of definition of AI.
The statement means that accumulation of large amounts of data (quantity) can lead to significant improvements in performance and behavioral intelligence (quality) of AI systems.
In AI, this is evident in machine learning, where systems trained on vast datasets can make better decisions, recognize patterns more accurately, and behave more intelligently—mimicking human cognitive functions, thus improving the quality of AI through sheer data quantity.
16.) How does Convolution Neural Network work? Explain.
A Convolutional Neural Network (CNN) is a type of deep learning model designed for image and pattern recognition. It works by using convolutional layers that apply filters (kernels) to extract important features (like edges, textures) from input images.
These layers are followed by pooling layers that reduce the dimensionality and help in generalizing the features. Finally, fully connected layers classify the extracted features into categories. CNNs are widely used in facial recognition, object detection, and medical image analysis.
Long Answer Questions
17.) How is machine learning is related to Artificial Intelligence? Explain.
Artificial Intelligence (AI) is a broad field of computer science focused on building systems that can perform tasks typically requiring human intelligence, such as reasoning, decision-making, and problem-solving. Machine Learning (ML) is a subset of AI that enables machines to learn from data and improve their performance without being explicitly programmed for every specific task.
In AI, traditional approaches relied on hard-coded rules to perform tasks. However, these approaches became inadequate for complex and dynamic environments. ML solves this limitation by using algorithms that identify patterns and make predictions from large datasets.
Example:
- In traditional AI: A chess-playing system follows pre-programmed rules.
- In ML-based AI: A system like AlphaZero learns to play chess by playing millions of games and improving based on the outcomes.
Relationship Summary:
- AI is the broader concept; ML is a technique to achieve AI.
- ML provides AI systems with adaptability and learning capabilities.
- Deep Learning (a subfield of ML) powers many modern AI applications like speech recognition and autonomous driving.
18.) Convert following sentences into Predicate Logic Knowledge base:
- Brothers are siblings.
- “Sibling” is reflexive.
- One’s mother is one’s female parent.
- A cousin is a child of a parent’s sibling.
- All BIM students study AI and JAVA.
19.) Represent the following sentences into a semantic network.
- Birds are animals.
- Birds have feathers, fly and lay eggs.
- Albatros is a bird.
- Donald is a bird.
- Tracy is an albatross.
20.) What difference between computer vision and machine vision? Explain with its application areas.
Comprehensive Questions
21.) From the given graph [Figure1: Graph of cities], Trace graph and calculate actual path from Dang to KTM using following searching method and Compare their performance.
- A*
- Uniform Cost Search
22.) How do you define problem? What are criteria for defining problem? Compare Constraint Satisfaction Problem and Real World Problem in detail with appropriate example.
In Artificial Intelligence, a problem is defined as a situation where an agent seeks to achieve a goal from a given initial state by performing a series of actions or operations within defined constraints.
Criteria for Defining a Problem:
To properly define a problem in AI, the following criteria must be specified:
- Initial State: The starting point of the problem.
- Goal State(s): The desired outcome(s) or condition(s) to achieve.
- Actions/Operators: Possible moves or transitions that can be applied to move from one state to another.
- State Space: All the possible states reachable from the initial state using valid actions.
- Path Cost: A function to assign a numeric cost to a path, useful in optimization.
- Constraints (if any): Rules or conditions that must be satisfied at every step or in the final solution.
Comparison: Constraint Satisfaction Problem (CSP) vs Real-World Problem
