Artificial Intelligence AI Viva Questions with Answers semester 6 Computer Engineering mumbai university

Artificial Intelligence AI Viva Questions with Answers semester 6 Computer Engineering mumbai university

Introduction to Artificial Intelligence

Q1: What defines Artificial Intelligence (AI), and how does it distinguish itself from traditional computing paradigms?
A1: Artificial Intelligence (AI) is the simulation of human intelligence processes by machines, notably computer systems. Unlike traditional computing, AI enables machines to perform tasks that typically require human intelligence, such as learning, problem-solving, and natural language understanding.

Q2: Trace the historical progression of Artificial Intelligence (AI) research.
A2: AI research has evolved from its inception in the 1950s, marked by milestones such as the Turing Test, through periods of high expectations (“AI summers”) and skepticism (“AI winters”), to the contemporary era characterized by breakthroughs in machine learning, neural networks, and deep learning.

Q3: Can you categorize Intelligent Systems within the realm of Artificial Intelligence?
A3: Intelligent Systems in AI can be classified into categories such as Reactive Machines, Limited Memory systems, Theory of Mind, and Self-aware systems. These categories represent varying levels of AI sophistication, from reactive responses to complex cognitive abilities akin to human intelligence.

Q4: Elaborate on the essential components of an AI program.
A4: The key components of an AI program include perception (sensing and interpreting the environment), reasoning (making decisions based on available information), learning (adapting and improving performance over time), problem-solving (finding solutions to complex problems), and natural language processing (interpreting and generating human language).

Q5: What are the foundational pillars of Artificial Intelligence?
A5: Artificial Intelligence draws from disciplines such as mathematics, computer science, cognitive psychology, philosophy, linguistics, and neuroscience. These interdisciplinary foundations provide theories, algorithms, and methodologies crucial for the development of intelligent systems.

Q6: Enumerate and briefly explain some sub-areas of Artificial Intelligence.
A6: Sub-areas of AI encompass Machine Learning (training systems to learn from data), Natural Language Processing (enabling computers to understand and generate human language), Computer Vision (interpreting visual information), Robotics (programming autonomous machines), and Expert Systems (emulating human expertise using rules and knowledge bases).

Q7: What are some notable applications of Artificial Intelligence across industries?
A7: AI applications span diverse sectors, including healthcare (diagnosis and treatment optimization), finance (fraud detection and algorithmic trading), transportation (autonomous vehicles and route planning), customer service (chatbots and virtual assistants), and entertainment (recommendation systems and gaming).

Q8: Analyze the prevailing trends in Artificial Intelligence today.
A8: Current trends in AI encompass advancements in deep learning techniques, the fusion of AI with Internet of Things (IoT) technologies, the rise of explainable AI for transparency, and the ethical considerations surrounding AI development and deployment.

Q9: How do you envision Artificial Intelligence shaping future societies?
A9: AI is poised to revolutionize various facets of society, including healthcare (personalized medicine and disease prediction), education (adaptive learning platforms and intelligent tutoring systems), work (automation and augmentation of tasks), and sustainability (environmental monitoring and resource management).

Q10: Discuss some of the challenges and constraints confronting Artificial Intelligence.
A10: Challenges in AI include ethical concerns regarding algorithmic bias and fairness, the potential for job displacement due to automation, cybersecurity risks associated with AI-powered attacks, and the necessity for robust regulatory frameworks to govern AI development and deployment.

Intelligent Agents

Q1: Define the concept of Intelligent Agents and Environments in the context of Artificial Intelligence.
A1: Intelligent Agents are autonomous entities that perceive their environment, reason about their actions, and act to achieve their objectives. Environments represent the surroundings in which agents operate, providing sensory inputs and receiving actions from the agent.

Q2: Discuss the notion of rationality concerning Intelligent Agents.
A2: Rationality refers to the ability of an agent to select actions that maximize its expected performance measure based on the available information and the agent’s goals. A rational agent aims to make decisions that lead to the best possible outcomes in its environment.

Q3: Describe the nature of the environment as it pertains to Intelligent Agents.
A3: Environments in which Intelligent Agents operate can be characterized by properties such as observability (full vs. partial), determinism (deterministic vs. stochastic), episodicity (sequential vs. episodic), and dynamicity (static vs. dynamic). Understanding these characteristics helps in designing appropriate agent architectures.

Q4: Outline the typical structure of Intelligent Agents.
A4: Intelligent Agents consist of five components: sensors (to perceive the environment), actuators (to perform actions), a knowledge base (to store information), a decision-making mechanism (to choose actions), and a learning component (to adapt and improve over time).

Q5: Identify and briefly explain the different types of Agents.
A5: Types of Agents include Simple Reflex Agents (react to current perceptions), Model-Based Reflex Agents (maintain an internal state based on past perceptions), Goal-Based Agents (pursue specific objectives), Utility-Based Agents (maximize a utility function), and Learning Agents (improve performance through learning from experience).

Q6: Describe the role of Learning Agents in the realm of Intelligent Agents.
A6: Learning Agents are capable of improving their performance over time by learning from experience. They use feedback from the environment to update their knowledge and decision-making processes, allowing them to adapt to changing conditions and achieve better outcomes.

Q7: Explain how problems are solved through searching in the context of Intelligent Agents.
A7: Problem-solving agents employ search algorithms to explore a space of possible actions and their consequences in order to find solutions that achieve their goals. These algorithms systematically traverse the problem space, considering different sequences of actions until a satisfactory solution is found.

Q8: What are the primary steps involved in formulating problems for Problem-Solving Agents?
A8: Formulating problems typically involves identifying the initial state, defining the actions available to the agent, specifying the transition model (which describes how actions change the state), establishing the goal state, and determining the performance measure.

Q9: Can you provide examples of problems that can be solved using search algorithms?
A9: Examples of problems that can be solved through search include the Eight Puzzle (rearranging tiles to form a specified configuration), the Tower of Hanoi (moving disks between pegs), and the Shortest Path problem (finding the most efficient route between two points in a network).

Q10: Discuss the significance of Problem-Solving Agents in practical applications.
A10: Problem-solving agents are essential in various domains, including robotics (path planning and manipulation), logistics (resource allocation and scheduling), gaming (strategy development and decision-making), and automated planning (optimizing processes and workflows). They enable efficient and systematic problem-solving in complex environments.

Problem Solving

Q1: Explain the characteristics and application of Uninformed Search Methods in problem-solving.
A1: Uninformed search methods, such as Breadth First Search (BFS), Depth First Search (DFS), Depth Limited Search, and Depth First Iterative Deepening (DFID), explore the problem space without using any domain-specific knowledge. They are versatile and applicable to a wide range of problems but may suffer from inefficiency in large search spaces.

Q2: Compare and contrast Breadth First Search (BFS) and Depth First Search (DFS) algorithms.
A2: BFS expands nodes level by level, prioritizing breadth, while DFS explores as far as possible along each branch before backtracking, prioritizing depth. BFS guarantees the shortest path to the goal if the edge costs are uniform, whereas DFS may find a solution faster in certain scenarios but doesn’t guarantee optimality.

Q3: Discuss the Depth Limited Search algorithm and its significance in problem-solving.
A3: Depth Limited Search is a variation of DFS that limits the depth of exploration. It helps to mitigate the risk of infinite loops in infinite-depth search spaces while retaining the advantages of DFS in memory efficiency and space complexity.

Q4: Explain the concept and utility of Depth First Iterative Deepening (DFID) in problem-solving.
A4: DFID combines the benefits of DFS with the completeness of BFS by iteratively performing DFS with increasing depth limits. It allows for efficient exploration of large search spaces while guaranteeing optimality and completeness when the solution is found.

Q5: Describe Informed Search Methods and their role in problem-solving.
A5: Informed search methods, such as Greedy Best First Search, A* Search, and Memory Bounded Heuristic Search, utilize domain-specific knowledge or heuristics to guide the search process towards the most promising areas of the search space. They often lead to more efficient and effective search compared to uninformed methods.

Q6: Discuss the principles underlying Greedy Best First Search and its advantages.
A6: Greedy Best First Search selects the node that appears to be the closest to the goal based on a heuristic evaluation function. While it is not guaranteed to find the optimal solution, it often explores promising regions of the search space quickly, making it suitable for problems where a near-optimal solution is acceptable.

Q7: Explain the A* Search algorithm and its significance in problem-solving.
A7: A* Search is an informed search algorithm that combines the advantages of both BFS and Greedy Best First Search. It evaluates nodes based on a combination of the cost of reaching the node from the start and the estimated cost of reaching the goal from that node, using a heuristic function. A* guarantees both optimality and completeness under certain conditions.

Q8: Describe Memory Bounded Heuristic Search and its applications.
A8: Memory Bounded Heuristic Search aims to balance between memory usage and search efficiency by limiting the amount of memory allocated for storing search information. It is particularly useful in resource-constrained environments where memory availability is limited, such as embedded systems or mobile devices.

Q9: Explain the concept of Local Search Algorithms in the context of optimization problems.
A9: Local Search Algorithms, such as Hill Climbing, Simulated Annealing, and Genetic Algorithms, focus on improving a solution iteratively by making incremental changes. They are particularly effective for optimization problems where the goal is to find the best possible solution among a large set of potential solutions.

Q10: Discuss the application of Adversarial Search in game playing scenarios.
A10: Adversarial Search involves two or more competing agents, such as in game playing scenarios. Techniques like Min-Max Search and Alpha-Beta Pruning are employed to efficiently explore the game tree and determine the best move for a player while considering the possible responses of the opponent. These techniques are fundamental in designing intelligent game-playing agents.

Knowledge and Reasoning

Q1: What are Knowledge-based Agents, and how do they operate in problem-solving?
A1: Knowledge-based agents utilize knowledge about the environment to make decisions and solve problems. They typically possess a knowledge base containing facts and rules and employ reasoning mechanisms to derive new information and take appropriate actions.

Q2: Provide a brief overview of Propositional Logic and its relevance in Artificial Intelligence.
A2: Propositional Logic deals with propositions or statements that can be either true or false. It provides a formal framework for representing and reasoning about the truth values of logical expressions, making it fundamental in various AI applications, such as knowledge representation and automated reasoning.

Q3: Explain the syntax and semantics of First-Order Logic (FOL).
A3: In FOL, statements can include variables, constants, predicates, functions, and quantifiers. The syntax defines how these elements can be combined to form valid expressions, while the semantics specify the meaning of these expressions in terms of truth values and interpretations.

Q4: Describe the process of inference in First-Order Logic, including Forward Chaining and Backward Chaining.
A4: Inference in FOL involves deriving new logical conclusions from existing knowledge. Forward Chaining starts with known facts and applies inference rules to generate new conclusions until the goal is reached. Backward Chaining starts with the goal and works backward through inference rules to find supporting evidence in the knowledge base.

Q5: Explain the concept of Knowledge Engineering in First-Order Logic.
A5: Knowledge Engineering involves the acquisition, representation, and utilization of knowledge in AI systems. In FOL, knowledge engineers design formal representations of domain knowledge using logical expressions, such as facts and rules, and develop inference mechanisms to derive new knowledge from existing knowledge.

Q6: What is Unification, and how is it utilized in First-Order Logic?
A6: Unification is the process of finding substitutions for variables in logical expressions to make them syntactically identical. In FOL, unification is used in inference mechanisms such as resolution to resolve conflicts between logical expressions and derive new conclusions.

Q7: Discuss the Resolution method in First-Order Logic and its significance in automated reasoning.
A7: Resolution is a fundamental inference rule in FOL that allows for the derivation of new logical consequences from a set of premises. It works by resolving conflicting clauses through unification, eventually leading to the discovery of new knowledge or the proof of a theorem.

Q8: Explain the concept of Uncertainty in Knowledge and Reasoning.
A8: Uncertainty refers to the lack of complete knowledge or precision in the information available to an AI system. In uncertain domains, AI systems must reason under conditions of incomplete or imprecise information, requiring techniques such as probability theory and belief networks.

Q9: How is knowledge represented in an uncertain domain, and what role do belief networks play?
A9: In uncertain domains, knowledge is represented using probabilistic models that capture the likelihood of different outcomes or states of the world. Belief networks, also known as Bayesian networks, are graphical models that represent probabilistic dependencies between variables, facilitating reasoning under uncertainty.

Q10: Describe the semantics of belief networks and how they support inference.
A10: The semantics of belief networks describe how the probability distributions over variables are defined based on the network structure and conditional probability tables. Belief networks support inference by allowing AI systems to calculate the probabilities of different outcomes given observed evidence, using techniques such as probabilistic inference algorithms.

Planning and Learning

Q1: Define the planning problem in the context of Artificial Intelligence.
A1: The planning problem involves generating a sequence of actions to achieve a desired goal in an environment with known or partially known dynamics. It is a fundamental task in AI, where agents must reason about future states and actions to achieve their objectives efficiently.

Q2: Discuss the concept of Planning with state space search and its significance.
A2: Planning with state space search involves exploring possible sequences of actions from an initial state to a goal state. It allows agents to systematically navigate through the space of possible plans, considering different action sequences and their consequences to find an optimal or satisfactory solution.

Q3: Explain the principles of Partial Order Planning and its advantages.
A3: Partial Order Planning allows for flexible planning by specifying only partial ordering constraints between actions, rather than a strict linear sequence. It offers advantages in handling uncertainty and parallelism in action execution, making it suitable for domains where the exact order of actions is not predetermined.

Q4: Describe Hierarchical Planning and its role in complex problem-solving.
A4: Hierarchical Planning involves organizing planning tasks into a hierarchy of subgoals and subplans. It simplifies complex planning problems by decomposing them into manageable subproblems, each of which can be solved independently, leading to more efficient and scalable planning solutions.

Q5: Explain the concept of Conditional Planning and its application.
A5: Conditional Planning involves generating plans that are contingent on the outcome of certain conditions or events. It allows agents to anticipate and adapt to changes in the environment by considering multiple possible scenarios and planning accordingly, enhancing their robustness and flexibility.

Q6: What are the different forms of Learning in Artificial Intelligence?
A6: Learning in AI can take various forms, including supervised learning (learning from labeled data), unsupervised learning (learning from unlabeled data), reinforcement learning (learning from interaction with the environment), and semi-supervised learning (combining labeled and unlabeled data).

Q7: Discuss the Theory of Learning in the context of AI.
A7: The Theory of Learning in AI aims to understand the principles and mechanisms underlying the learning process in intelligent systems. It encompasses theoretical frameworks, such as computational learning theory and statistical learning theory, which study the computational capabilities and limitations of learning algorithms.

Q8: Explain PAC Learning and its significance in machine learning.
A8: PAC (Probably Approximately Correct) Learning is a theoretical framework that formalizes the conditions under which a learning algorithm can produce accurate predictions with high probability based on a limited amount of data. It provides theoretical guarantees on the sample complexity and computational efficiency of learning algorithms.

Q9: Provide an introduction to statistical learning and its applications.
A9: Statistical learning involves the study of algorithms and techniques for extracting patterns and making predictions from data. It encompasses a wide range of methods, such as regression, classification, clustering, and dimensionality reduction, which find applications in fields such as finance, healthcare, and natural language processing.

Q10: Introduce the concept of reinforcement learning and its core components.
A10: Reinforcement Learning is a paradigm of machine learning where agents learn to make decisions by interacting with an environment and receiving feedback in the form of rewards. It consists of three core components: the agent, the environment, and the reward signal, which together form the basis for learning from trial and error.

AI Applications

A. Introduction to NLP

Q1: What are Language Models, and how do they contribute to Natural Language Processing (NLP)?
A1: Language Models are statistical models that learn the probability distribution of sequences of words in a language. They play a crucial role in various NLP tasks such as text generation, machine translation, and speech recognition by enabling computers to understand and generate human language more effectively.

Q2: Explain the concept of Grammars in the context of Natural Language Processing (NLP).
A2: Grammars are formal systems used to describe the structure and syntax of languages. In NLP, grammars are employed to define the rules and constraints governing the composition of sentences and phrases in natural languages, facilitating tasks such as parsing and language generation.

Q3: What is Parsing, and how does it relate to Natural Language Processing (NLP)?
A3: Parsing is the process of analyzing the grammatical structure of sentences to determine their syntactic relationships. In NLP, parsing plays a crucial role in tasks such as syntactic analysis, semantic interpretation, and information extraction, enabling computers to understand the meaning and structure of human language.

B. Robotics

Q4: Define Robotics and discuss its significance in the field of Artificial Intelligence.
A4: Robotics is a multidisciplinary field involving the design, construction, and operation of robots to perform tasks autonomously or semi-autonomously. It intersects with AI by integrating perception, decision-making, and action execution capabilities into robotic systems, enabling them to interact with and adapt to their environment.

Q5: What components make up Robot hardware, and how do they contribute to robot functionality?
A5: Robot hardware typically consists of sensors (such as cameras, lidars, and accelerometers), actuators (such as motors and manipulators), processors (such as microcontrollers and CPUs), and power sources. These components work together to enable robots to perceive their environment, make decisions, and execute actions.

Q6: Discuss the range of problems that Robotics can solve in various domains.
A6: Robotics can address a wide range of problems across domains such as manufacturing (automating assembly lines), healthcare (assisting with surgeries and patient care), logistics (automating warehouse operations), agriculture (harvesting crops), exploration (mapping and navigating unknown environments), and entertainment (providing interactive experiences).

C. AI applications in Healthcare, Retail, Banking

Q7: How is Artificial Intelligence applied in the Healthcare industry?
A7: In healthcare, AI is used for tasks such as medical imaging analysis (diagnosing diseases from X-rays and MRI scans), drug discovery (identifying potential drug candidates), personalized medicine (tailoring treatments to individual patients), virtual health assistants (providing patient support and monitoring), and healthcare management (optimizing hospital operations).

Q8: What are some applications of Artificial Intelligence in the Retail sector?
A8: In retail, AI is employed for tasks such as demand forecasting (predicting customer preferences and inventory needs), recommendation systems (suggesting products based on user behavior), supply chain optimization (streamlining logistics and inventory management), personalized marketing (targeting customers with relevant offers), and customer service automation (using chatbots for support).

Q9: Discuss the role of Artificial Intelligence in the Banking industry.
A9: In banking, AI is utilized for tasks such as fraud detection (identifying suspicious transactions and activities), risk assessment (evaluating loan applications and creditworthiness), customer service (providing personalized assistance and support), algorithmic trading (automating financial trading decisions), and regulatory compliance (ensuring adherence to legal and regulatory requirements).