Artificial Intelligence Viva Questions with Answers semester 5 mumbai university

Artificial Intelligence Viva Questions with Answers semester 5 mumbai university

Module 1: Introduction to Artificial Intelligence

Question 1:

What is Artificial Intelligence (AI)?

Answer 1:

AI refers to the simulation of human intelligence in machines, enabling them to perform tasks that typically require human intelligence, such as problem-solving and learning.

Question 2:

What are the two main perspectives in AI, as mentioned in this module?

Answer 2:

The two main perspectives in AI are “Acting and Thinking humanly” and “Acting and Thinking rationally.”

Question 3:

Explain “Acting and Thinking humanly” in the context of AI.

Answer 3:

Acting and Thinking humanly in AI refers to creating AI systems that mimic human behavior and cognition. These systems aim to imitate the way humans act and think.

Question 4:

Explain “Acting and Thinking rationally” in the context of AI.

Answer 4:

Acting and Thinking rationally in AI focuses on creating AI systems that make decisions and solve problems logically, based on rational principles, rather than imitating human behavior.

Question 5:

What is the history of AI, and when did it begin?

Answer 5:

The history of AI dates back to the mid-20th century when the term “Artificial Intelligence” was coined at a Dartmouth College conference in 1956. This marked the beginning of AI as a field of study.

Question 6:

What are some of the applications of AI mentioned in this module?

Answer 6:

Applications of AI include natural language processing, computer vision, robotics, expert systems, and more. AI is used in various fields such as healthcare, finance, and transportation.

Question 7:

What is the present state of AI as discussed in this module?

Answer 7:

The present state of AI is characterized by rapid advancements in machine learning and deep learning. AI technologies are being applied in real-world scenarios, and AI research continues to evolve.

Question 8:

Why is ethics in AI an important topic of discussion?

Answer 8:

Ethics in AI is important because AI systems can impact society, and ethical considerations are necessary to ensure that AI technologies are developed and used responsibly, without causing harm or bias.

Question 9:

What are some key challenges and limitations in the field of AI?

Answer 9:

Challenges in AI include understanding natural language, common-sense reasoning, and ethical concerns. AI systems also have limitations in terms of their ability to generalize and adapt to new situations.

Question 10:

What is the Turing Test, and how does it relate to AI?

Answer 10:

The Turing Test is a test of a machine’s ability to exhibit intelligent behavior indistinguishable from that of a human. It is used to evaluate a machine’s natural language processing and conversational abilities in the context of AI.

Question 11:

What role does machine learning play in AI?

Answer 11:

Machine learning is a subset of AI that focuses on training machines to learn from data and make predictions or decisions without being explicitly programmed. It is a crucial component of AI for pattern recognition and prediction tasks.

Question 12:

Explain the concept of “Strong AI” and “Weak AI.”

Answer 12:

Strong AI refers to AI systems with human-level intelligence and consciousness. Weak AI, on the other hand, refers to AI systems designed for specific tasks, lacking true consciousness.

Question 13:

What is the relationship between AI and robotics?

Answer 13:

AI and robotics are closely related, as AI technologies often power the decision-making and control systems in robots. Robots use AI to interact with the environment and perform tasks autonomously.

Question 14:

What are the potential benefits of AI in healthcare, as discussed in this module?

Answer 14:

AI can improve healthcare through faster diagnosis, personalized treatment recommendations, and data analysis. It can enhance patient care and medical research by processing vast amounts of medical data.

Question 15:

How can AI impact the job market and employment, and what considerations are important?

Answer 15:

AI may automate certain tasks, leading to job displacement in some industries. It’s essential to consider retraining and upskilling the workforce to adapt to the changing job market and harness the potential of AI in creating new opportunities.

Module 2: Intelligent Agents

Question 1:

What is the concept of an intelligent agent in the context of AI?

Answer 1:

An intelligent agent is a system that perceives its environment, makes decisions, and takes actions to achieve specific goals. It exhibits some level of autonomy and intelligence.

Question 2:

Explain the structure of an intelligent agent.

Answer 2:

An intelligent agent typically consists of a sensor, which perceives the environment, and an actuator, which takes actions. It also has a decision-making component, known as the agent function.

Question 3:

What are the characteristics of intelligent agents?

Answer 3:

The characteristics of intelligent agents include autonomy, goal-oriented behavior, adaptability, and the ability to interact with the environment and other agents.

Question 4:

What are the four main types of agents mentioned in this module?

Answer 4:

The four main types of agents are Simple Reflex Agents, Model-Based Agents, Goal-Based Agents, and Utility-Based Agents.

Question 5:

Describe a Simple Reflex Agent and its working principle.

Answer 5:

A Simple Reflex Agent makes decisions based on the current percept and predefined condition-action rules. It reacts to the environment without considering past actions or future consequences.

Question 6:

What is a Model-Based Agent, and how does it differ from a Simple Reflex Agent?

Answer 6:

A Model-Based Agent maintains an internal state or model of the world, allowing it to consider past percepts and actions. It can make more informed decisions based on the model.

Question 7:

Explain the concept of a Goal-Based Agent.

Answer 7:

A Goal-Based Agent selects actions that will lead to desirable states or outcomes. It considers its goals and reasons about how to achieve them, making it more strategic than simple reflex agents.

Question 8:

What is a Utility-Based Agent, and what is its primary concern?

Answer 8:

A Utility-Based Agent evaluates the desirability of different states or outcomes using a utility function. Its primary concern is to maximize the expected utility, which quantifies the desirability of a state or outcome.

Question 9:

What are the different types of environments in which agents operate, as mentioned in this module?

Answer 9:

The different environment types include Deterministic, Stochastic, Static, Dynamic, Observable, Semi-observable, Single Agent, and Multi-Agent environments.

Question 10:

Explain the concept of a Deterministic environment.

Answer 10:

In a Deterministic environment, the outcome of an action is entirely determined by the current state and the action taken. There is no randomness or uncertainty in the environment.

Question 11:

What is a Stochastic environment, and how does it differ from a Deterministic environment?

Answer 11:

In a Stochastic environment, there is an element of randomness or uncertainty in the outcomes of actions. Unlike a Deterministic environment, the exact results of actions are not predetermined and may vary.

Question 12:

Explain the difference between a Static and a Dynamic environment.

Answer 12:

In a Static environment, the environment does not change while the agent is making decisions. In contrast, a Dynamic environment changes over time, and the agent must adapt to these changes.

Question 13:

What is an Observable environment, and why is it relevant to agents?

Answer 13:

An Observable environment allows an agent to have full access to all relevant information at each decision point. It is relevant because it impacts the agent’s ability to make informed decisions based on available information.

Question 14:

What is a Semi-observable environment, and what challenges does it present to agents?

Answer 14:

In a Semi-observable environment, not all information is available to the agent. This lack of complete information presents challenges as the agent must make decisions with incomplete knowledge of the environment.

Question 15:

Explain the difference between Single Agent and Multi-Agent environments.

Answer 15:

In a Single Agent environment, there is only one agent operating in the environment. In a Multi-Agent environment, multiple agents operate simultaneously, and their actions and interactions can impact each other’s goals and outcomes.

Module 3: Solving Problems by Searching

Question 1:

What is the definition of problem-solving by searching in AI?

Answer 1:

Problem-solving by searching involves finding a sequence of actions or steps that transforms an initial state into a goal state, while adhering to predefined rules and constraints.

Question 2:

What is state space representation in problem-solving?

Answer 2:

State space representation involves defining the various states that a problem can be in, the initial state, goal state, and the possible transitions or actions that can move the problem from one state to another.

Question 3:

How is a problem typically formulated as a state space search in AI?

Answer 3:

In AI, a problem is typically formulated as a state space search by defining the initial state, goal state, the set of possible actions, and the rules or constraints governing these actions. The search algorithm then seeks a path from the initial state to the goal state.

Question 4:

What are well-defined problems in the context of state space search?

Answer 4:

Well-defined problems have clearly defined initial and goal states, a finite set of actions, and defined rules for transitioning between states. These problems can be effectively solved using search algorithms.

Question 5:

How are search strategies in AI evaluated in terms of performance?

Answer 5:

Search strategies in AI are evaluated based on criteria such as time complexity (how long it takes to find a solution), space complexity (how much memory it uses), completeness (whether it guarantees a solution if one exists), and optimality (whether it finds the best solution).

Question 6:

What is Depth First Search (DFS) in uninformed search, and how does it work?

Answer 6:

DFS explores as far as possible along a branch before backtracking. It uses a stack to store the next nodes to be expanded and is not guaranteed to find the shortest path.

Question 7:

What is Breadth First Search (BFS) in uninformed search, and how does it work?

Answer 7:

BFS explores all neighbor nodes before moving to the next level. It uses a queue to maintain the order of exploration and guarantees the shortest path if one exists.

Question 8:

Explain Depth Limited Search and its purpose.

Answer 8:

Depth Limited Search is a modified DFS that limits the depth of exploration. It is used to prevent DFS from going too deep and potentially getting stuck in infinite loops or deep paths.

Question 9:

What is Iterative Deepening Search (IDS), and why is it useful?

Answer 9:

IDS combines the benefits of DFS and BFS by iteratively increasing the depth limit of search. It ensures that the shortest path is found while keeping the memory usage low.

Question 10:

What is Uniform Cost Search (UCS) in uninformed search, and how does it work?

Answer 10:

Uniform Cost Search explores the node with the lowest path cost. It guarantees the optimal path by always expanding the cheapest available option.

Question 11:

Explain the concept of Bidirectional Search in uninformed search.

Answer 11:

Bidirectional Search explores from both the initial state and the goal state simultaneously and terminates when the two searches meet. It can be more efficient in finding the shortest path when the search space is vast.

Question 12:

What is the role of a heuristic function in informed search?

Answer 12:

A heuristic function estimates the cost from the current state to the goal state. It guides informed search algorithms by helping them prioritize nodes that appear promising in reaching the goal faster.

Question 13:

What is an admissible heuristic in the context of informed search?

Answer 13:

An admissible heuristic never overestimates the true cost to reach the goal from a given state. It ensures that informed search algorithms like A* will always find the optimal path.

Question 14:

Explain the concept of Greedy Best First Search in informed search.

Answer 14:

Greedy Best First Search selects the node that appears closest to the goal based on the heuristic function. It often finds a solution quickly but may not guarantee optimality.

Question 15:

What is A* Search in informed search, and why is it considered advantageous?

Answer 15:

A* Search combines the advantages of both Uniform Cost Search and Greedy Best First Search. It uses a combination of actual path cost and heuristic information to find the optimal path efficiently. It is considered advantageous because it is both complete and optimal when using an admissible heuristic.

Module 4: Knowledge and Reasoning

Question 1:

What is the significance of knowledge in artificial intelligence, and why is it essential?

Answer 1:

Knowledge is crucial in AI because it provides the foundation for intelligent decision-making and problem-solving. It enables AI systems to understand, reason, and make informed choices, improving their overall performance.

Question 2:

What are some of the key issues in knowledge representation in AI?

Answer 2:

Issues in knowledge representation include choosing appropriate formalisms, handling uncertainty, scalability, and the ability to reason effectively from the represented knowledge.

Question 3:

What are Knowledge Representation Systems, and what is their role in AI?

Answer 3:

Knowledge Representation Systems are frameworks or languages used to encode and store knowledge in a way that AI systems can understand. They play a vital role in facilitating reasoning and problem-solving in AI.

Question 4:

What are some properties of Knowledge Representation Systems in AI?

Answer 4:

Properties of Knowledge Representation Systems include expressiveness, inferential adequacy, and efficiency in terms of knowledge storage and retrieval.

Question 5:

Explain Propositional Logic (PL) in AI, including its syntax and semantics.

Answer 5:

Propositional Logic deals with propositions, which are statements that can be true or false. Its syntax includes logical connectives (e.g., AND, OR, NOT), and its semantics define how truth values are assigned to propositions based on the connectives.

Question 6:

What is a tautology in Propositional Logic, and how is it determined?

Answer 6:

A tautology is a proposition that is always true, regardless of the truth values of its constituent propositions. It is determined by constructing a truth table that shows all possible combinations of truth values for the propositions and verifying that the proposition always evaluates to true.

Question 7:

What is the concept of validity in Propositional Logic?

Answer 7:

A proposition is considered valid if it is true in all possible interpretations. In other words, its conclusion always follows from its premises, making it a valid argument.

Question 8:

What is Predicate Logic (FOPL), and how does it differ from Propositional Logic?

Answer 8:

Predicate Logic, or First-Order Predicate Logic (FOPL), extends Propositional Logic by introducing variables and quantifiers (e.g., ∀ for “for all” and ∃ for “there exists”). It allows for more complex statements and is more expressive than Propositional Logic.

Question 9:

Explain the concept of quantification in Predicate Logic, and provide examples of quantifiers.

Answer 9:

Quantification involves specifying how many or which objects a statement applies to. For example, “∀x P(x)” means “For all x, P(x),” and “∃x Q(x)” means “There exists an x such that Q(x).” These are universal and existential quantifiers, respectively.

Question 10:

What are Forward Chaining, Backward Chaining, and Resolution in First-Order Predicate Logic (FOPL), and how do they differ?

Answer 10:

– Forward Chaining starts with known facts and derives new conclusions until the goal is reached. – Backward Chaining starts with the goal and works backward to find a sequence of inferences. – Resolution is a theorem proving method that seeks to derive a contradiction from a set of clauses and the negation of a goal. It is used to determine the satisfiability of a set of clauses.

Question 11:

What is logic programming, and how does it relate to First-Order Predicate Logic (FOPL)?

Answer 11:

Logic programming, such as Prolog, is a programming paradigm that is based on the principles of FOPL. It uses rules and facts represented in FOPL to perform automated reasoning and solve problems.

Question 12:

How is Knowledge Representation in AI different when using FOPL compared to Propositional Logic?

Answer 12:

Knowledge representation in FOPL allows for the representation of more complex and structured knowledge compared to Propositional Logic. FOPL can represent relationships, variables, and quantifiers, making it more expressive and suitable for a wider range of problems.

Question 13:

Explain the concept of Adversarial Search Techniques in AI.

Answer 13:

Adversarial Search Techniques are used in AI for competitive scenarios, such as game playing. They involve searching for the best moves while considering an opponent’s actions. Techniques like Mini-max Search and Alpha-Beta Pruning are commonly used in adversarial search.

Question 14:

What is Mini-max Search, and how does it work in adversarial game playing?

Answer 14:

Mini-max Search is a decision-making algorithm used in adversarial games. It maximizes the potential gain for the current player while minimizing the potential loss by assuming the opponent makes optimal moves. It constructs a game tree and selects the best moves by evaluating leaf nodes.

Question 15:

Explain the concept of Alpha-Beta Pruning in adversarial game playing.

Answer 15:

Alpha-Beta Pruning is an optimization technique used in Mini-max Search. It prunes branches of the game tree that do not affect the final decision, significantly reducing the number of nodes that need to be evaluated, making the search more efficient.

Module 5: Reasoning Under Uncertainty

Question 1:

Why is handling uncertain knowledge important in artificial intelligence?

Answer 1:

Handling uncertain knowledge is essential in AI because many real-world problems involve incomplete or ambiguous information. AI systems need to reason and make decisions in the presence of uncertainty.

Question 2:

What are random variables in the context of uncertainty?

Answer 2:

Random variables are variables that can take on different values with a certain probability distribution. They are used to model uncertainty in AI and represent events or outcomes that are not fully deterministic.

Question 3:

Explain the concepts of prior and posterior probability.

Answer 3:

Prior probability represents the initial probability of an event before new information is considered. Posterior probability is the updated probability of the event after taking new evidence or information into account.

Question 4:

What is inference using the full joint distribution, and when is it used?

Answer 4:

Inference using the full joint distribution involves calculating probabilities for all possible combinations of variables in a system. It is used when a complete understanding of the joint probabilities of variables is required, but it can be computationally expensive for large systems.

Question 5:

Explain Bayes’ Rule and its use in AI.

Answer 5:

Bayes’ Rule is a formula for updating probabilities based on new evidence. In AI, it is used to update prior probabilities with new data or observations, making it a fundamental tool for probabilistic reasoning and belief updating.

Question 6:

What are Bayesian Belief Networks (BBNs), and how are they used in AI?

Answer 6:

BBNs are graphical models that represent probabilistic relationships between variables. They are used in AI for modeling and reasoning under uncertainty, allowing efficient inference by exploiting conditional independence among variables.

Question 7:

How does reasoning in Belief Networks differ from reasoning in full joint distributions?

Answer 7:

Reasoning in Belief Networks is more efficient than reasoning in full joint distributions because BBNs exploit conditional independence to simplify the calculations. This makes them suitable for modeling complex systems with fewer computational resources.

Question 8:

What is the role of conditional probability in Bayesian Belief Networks?

Answer 8:

Conditional probability in BBNs represents the probability of a variable given the values of its parent variables in the network. It is used to make probabilistic inferences in BBNs by updating probabilities based on new evidence or observations.

Question 9:

What are some real-world applications where reasoning under uncertainty is crucial in AI?

Answer 9:

Reasoning under uncertainty is crucial in applications like medical diagnosis, financial risk assessment, natural language processing, autonomous robotics, and recommendation systems, where uncertainty is inherent in the data and decision-making process.

Question 10:

How can the use of Bayesian Belief Networks improve decision-making in uncertain environments?

Answer 10:

BBNs provide a structured framework for representing and updating knowledge under uncertainty. By modeling relationships and dependencies among variables, they enable AI systems to make more informed and rational decisions based on probabilistic reasoning.

Question 11:

What are some challenges in probabilistic reasoning and uncertainty modeling in AI?

Answer 11:

Challenges include handling large-scale networks, learning the structure and parameters of BBNs from data, dealing with rare events, and coping with situations where the data is incomplete or noisy.

Question 12:

How does the use of Bayesian Belief Networks support decision-making in medical diagnosis?

Answer 12:

BBNs can integrate patient data, medical knowledge, and test results to provide probabilities for different diagnoses. They assist clinicians in making informed decisions about treatments and interventions by quantifying uncertainty in the diagnostic process.

Question 13:

Explain the concept of Markov Blanket in Bayesian Belief Networks.

Answer 13:

A Markov Blanket for a variable in a BBN consists of its parents, its children, and the other parents of its children. It is a set of variables that, when known, makes the variable conditionally independent of all other variables in the network. It is useful for simplifying probabilistic inferences.

Question 14:

How can Bayesian Belief Networks assist in financial risk assessment?

Answer 14:

BBNs can model the relationships between various financial variables and market factors, helping financial institutions assess and manage risks. They provide a structured way to understand how changes in one variable may impact others, allowing for more informed risk management decisions.

Question 15:

What is the concept of sensitivity analysis in Bayesian Belief Networks, and why is it important?

Answer 15:

Sensitivity analysis involves studying how changes in the probabilities or values of variables affect the network’s outcomes. It is important for understanding the robustness of decisions made under uncertainty and for identifying critical variables in the decision-making process.

Module 6: Planning and Learning

Question 1:

What is the planning problem in AI, and why is it significant?

Answer 1:

The planning problem involves determining a sequence of actions to achieve a desired goal in an environment. It is essential in AI for tasks such as robotics and autonomous systems to plan and execute actions efficiently.

Question 2:

Explain the difference between Partial Order Planning and Total Order Planning in AI.

Answer 2:

Partial Order Planning allows actions to be executed in a flexible order, while Total Order Planning specifies a fixed sequence of actions. Partial Order Planning is more suitable for complex tasks where the order of actions can vary.

Question 3:

What is Learning in AI, and how does it contribute to the development of intelligent systems?

Answer 3:

Learning in AI involves acquiring knowledge or improving performance from data or experience. It is a fundamental component for enabling AI systems to adapt, make decisions, and improve their performance over time.

Question 4:

What is a Learning Agent, and how does it differ from a traditional AI agent?

Answer 4:

A Learning Agent is an AI system that can adapt and improve its behavior based on data and experience. It differs from traditional AI agents as it has the capability to learn and refine its knowledge and actions, making it more flexible and intelligent.

Question 5:

Explain the concepts of Supervised, Unsupervised, Semi-Supervised Learning, and Reinforcement Learning in AI.

Answer 5:

– Supervised Learning: It involves learning from labeled data, where the model predicts outcomes based on input features. – Unsupervised Learning: It deals with finding patterns and structure in unlabeled data. – Semi-Supervised Learning: It combines labeled and unlabeled data for training. – Reinforcement Learning: It focuses on learning through interaction with an environment, receiving rewards or penalties for actions taken.

Question 6:

What is Ensemble Learning in AI, and how does it improve predictive accuracy?

Answer 6:

Ensemble Learning involves combining multiple models to make predictions. It improves accuracy by reducing bias and variance, leading to more robust and reliable predictions by considering a variety of perspectives.

Question 7:

What are Expert Systems in AI, and how do they mimic human expertise?

Answer 7:

Expert Systems are AI systems that emulate the decision-making abilities of human experts in specific domains. They mimic human expertise by using knowledge bases, inference engines, and rules to make decisions and solve problems.

Question 8:

Explain the components of an Expert System, including the Knowledge Base, Inference Engine, User Interface, and Working Memory.

Answer 8:

– Knowledge Base: Contains domain-specific knowledge and facts. – Inference Engine: Performs reasoning and makes decisions based on the knowledge in the knowledge base. – User Interface: Allows interaction with users to input queries and receive responses. – Working Memory: Stores temporary information and data during the reasoning process.

Question 9:

How are Expert Systems developed in AI, and what is the process involved?

Answer 9:

Expert Systems are developed through a process that includes knowledge acquisition, knowledge representation, rule-based reasoning, and system testing and evaluation. This process ensures that the system can effectively replicate human expertise in a given domain.

Question 10:

What is the role of knowledge representation in Expert Systems, and how is it used for decision-making?

Answer 10:

Knowledge representation is crucial in Expert Systems to encode domain-specific knowledge in a format that the system can understand. This knowledge is used by the inference engine to make informed decisions and provide expert-level advice.

Question 11:

How does Partial Order Planning differ from Total Order Planning, and in what scenarios are they each useful?

Answer 11:

Partial Order Planning allows flexibility in the order of actions, making it suitable for tasks with uncertain sequencing. Total Order Planning specifies a fixed sequence and is useful for well-defined, deterministic tasks where the order is critical.

Question 12:

Explain the concept of Reinforcement Learning, and provide an example of its application in AI.

Answer 12:

Reinforcement Learning involves an agent learning by taking actions in an environment and receiving rewards or penalties. An example application is training autonomous agents, such as self-driving cars, to make decisions based on feedback from the environment.

Question 13:

How does Semi-Supervised Learning combine aspects of both Supervised and Unsupervised Learning in AI?

Answer 13:

Semi-Supervised Learning uses a combination of labeled and unlabeled data for training. It incorporates supervised learning by using labeled data and unsupervised learning by finding patterns in the unlabeled data, making it a versatile learning approach.

Question 14:

What is the significance of User Interface in Expert Systems, and how does it facilitate interaction with users?

Answer 14:

The User Interface in Expert Systems allows users to input queries, receive responses, and interact with the system. It simplifies user interaction, making the expert system accessible and user-friendly for non-experts in the domain.

Question 15:

In what domains or industries have Expert Systems found practical applications, and how have they improved decision-making?

Answer 15:

Expert Systems have been applied in various domains, including healthcare for medical diagnosis, finance for risk assessment, manufacturing for quality control, and customer support for automated assistance. They have improved decision-making by providing expert-level advice, reducing errors, and increasing efficiency in these domains.