Introduction to AI and Problem Solving

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Questions and Answers

Define artificial intelligence. Describe the fields and application areas of AI.

Artificial intelligence (AI) is the ability of a computer or a robot controlled by a computer to do tasks that are usually done by humans because they require human intelligence and discernment. Fields include machine learning, natural language processing, computer vision, and robotics. Application areas include healthcare, finance, transportation, and entertainment.

What is the Turing Test? What capabilities does a machine need to have to pass the Turing Test? Explain.

The Turing Test is a test of a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. A machine needs capabilities such as natural language processing, knowledge representation, automated reasoning, and machine learning to pass the Turing Test.

An agent consists of architecture and an agent program. Sketch the basic architecture of an intelligent agent. Give an example of an agent.

An agent's architecture includes sensors, actuators, and a control system. The agent program implements a mapping from percepts to actions. An example of an agent is a vacuum cleaner robot.

Is it possible to develop human level intelligence in a machine? What could be the challenges of such research?

<p>It is a topic of debate whether it is possible to develop human-level intelligence in a machine. Challenges include replicating human consciousness, emotions, and adaptability.</p> Signup and view all the answers

Describe the Turing Test. Do you think the test is an accurate measure of artificial intelligence? Explain.

<p>The Turing Test is a test of a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. Its accuracy as a measure of AI is debated. Some argue it only measures the ability to mimic human responses, not true intelligence.</p> Signup and view all the answers

Define and describe the difference between knowledge, belief, hypothesis, and data.

<p>Data are raw facts and figures. Knowledge is information that has been processed and organized. Belief is a subjective opinion or conviction. A hypothesis is a proposed explanation based on limited evidence.</p> Signup and view all the answers

Briefly explain with example: a) Declarative knowledge, b) Procedural Knowledge and c) Heuristic knowledge.

<p>Declarative knowledge involves knowing 'what' (e.g., 'Paris is the capital of France'). Procedural knowledge involves knowing 'how' (e.g., how to ride a bike). Heuristic knowledge is based on rules of thumb or experience (e.g., 'If it's raining, take an umbrella').</p> Signup and view all the answers

Briefly explain: Relational and procedural knowledge

<p>Relational knowledge represents relationships between entities.Procedural knowledge consists of knowing 'how' to do things; it involves a sequence of steps or actions.</p> Signup and view all the answers

Write a short note on human versus machine performance

<p>Human performance excels in areas requiring creativity, emotional intelligence, and adaptability. Machine performance excels in areas requiring speed, precision, and handling large amounts of data.</p> Signup and view all the answers

Write a short note on the Turing Test

<p>The Turing Test assesses a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. The machine must be able to fool a human evaluator into believing it is also human.</p> Signup and view all the answers

Model the water-jug problem, Missionary cannibal, Farmer Fox Goose Grain Problem as an AI production system.

<p>This involves defining states, operators, and goals to solve classic AI problems.</p> Signup and view all the answers

What is the difference between linear planning vs non-linear planning?

<p>Linear planning involves executing actions in a fixed sequence, while non-linear planning allows for parallel or interleaved action execution.</p> Signup and view all the answers

Compare forward and backward chaining

<p>Forward chaining starts with known facts and applies inference rules to derive new facts, while backward chaining starts with a goal and works backward to find evidence that supports it.</p> Signup and view all the answers

What is Means-End Analysis (MEA)?

<p>Means-End Analysis is a problem-solving technique that involves identifying differences between the current state and the goal state, and then applying operators to reduce these differences.</p> Signup and view all the answers

Write a short note on MYCIN

<p>MYCIN was an early expert system designed to diagnose bacterial infections.</p> Signup and view all the answers

What is intelligence? What is the difference between Human Intelligence and Machine Intelligence

<p>Intelligence is the ability to understand, learn, and reason. Human intelligence involves consciousness, emotions, and adaptability, while machine intelligence is based on algorithms and data processing.</p> Signup and view all the answers

What is Knowledge Representation and FOPL/FOL?

<p>Knowledge representation is the method used to encode information for AI systems. FOPL/FOL (First-Order Predicate Logic/First-Order Logic) is a formal language for representing knowledge.</p> Signup and view all the answers

What are the advantages of FOPL over Proposition Logic with examples?

<p>FOPL can represent objects, relations, and quantifiers, while Proposition Logic can only represent simple propositions. FOPL provides more expressiveness and flexibility in representing complex knowledge.</p> Signup and view all the answers

Describe how to convert into CNF and Proof by Resolution

<p>Conversion to CNF (Conjunctive Normal Form) involves eliminating implications, moving negations inward, standardizing variables, and distributing conjunctions over disjunctions. Proof by Resolution involves negating the goal, adding it to the set of clauses, and repeatedly resolving clauses until a contradiction is derived.</p> Signup and view all the answers

What is a Semantic Network and what are Frames for Knowledge Representation?

<p>A semantic network represents knowledge as nodes (concepts) and edges (relationships). Frames represent knowledge as a collection of attributes and their values, organizing information about objects or situations.</p> Signup and view all the answers

What are Skolemization and CNF Conversion Steps?

<p>Skolemization involves replacing existentially quantified variables with Skolem functions or constants. CNF (Conjunctive Normal Form) conversion steps include eliminating implications, moving negations inward, standardizing variables, and distributing conjunctions over disjunctions.</p> Signup and view all the answers

What is Heuristic Search? How does A* Search and Greedy Search differ?

<p>Heuristic search uses heuristics to guide the search process. A* Search evaluates nodes by combining the cost to reach the node and the estimated cost to the goal, while Greedy Search only considers the estimated cost to the goal.</p> Signup and view all the answers

What is the Mini-Max Algorithm? What are its limitations and how to overcome them by Alpha Beta pruning?

<p>The Mini-Max Algorithm is a decision-making algorithm for game playing. Its limitations include high computational complexity. Alpha-Beta pruning reduces the search space by eliminating branches that cannot influence the final decision.</p> Signup and view all the answers

What is Uncertainty and Bayes Theorem? Example of Bayes Belief Network

<p>Uncertainty refers to situations where the outcome is not deterministic. Bayes Theorem provides a way to update beliefs based on new evidence. Bayes Belief Network represents probabilistic relationships among variables.</p> Signup and view all the answers

Write a short note on Case-Based Reasoning

<p>Case-Based Reasoning (CBR) is a problem-solving approach that involves retrieving and adapting solutions from similar past cases.</p> Signup and view all the answers

What is a Neural Network and Perceptron? Explain its working.

<p>A neural network is a computational model inspired by the structure and function of the human brain. A perceptron is a single-layer neural network that performs binary classification.</p> Signup and view all the answers

Describe Genetic Algorithms and it's steps. Explain working with an example.

<p>Genetic Algorithms are optimization algorithms inspired by natural selection. Steps include initialization, selection, crossover, mutation, and evaluation.</p> Signup and view all the answers

Write short notes on GA Operators

<p>GA (Genetic Algorithm) operators include selection, crossover, and mutation. Selection chooses individuals for reproduction, crossover combines genetic information, and mutation introduces random changes.</p> Signup and view all the answers

Describe Back-propagation Neural Network

<p>Back-propagation is a supervised learning algorithm used to train neural networks by iteratively adjusting the weights based on the error between the predicted and actual outputs.</p> Signup and view all the answers

Why is Multilayer Perceptron preferred over Single layer?

<p>Multilayer Perceptrons (MLPs) can learn complex non-linear relationships, while single-layer perceptrons can only learn linear relationships.</p> Signup and view all the answers

Describe Expert System and its characteristics.

<p>An expert system is a computer program that emulates the decision-making ability of a human expert. Characteristics include domain-specific knowledge, reasoning capabilities, and explanation facilities.</p> Signup and view all the answers

Describe the Architecture of Expert System

<p>The architecture of an expert system includes a knowledge base, inference engine, and user interface.</p> Signup and view all the answers

What is NLP? Explain steps involved in NLP.

<p>Natural Language Processing (NLP) is a field of AI that deals with the interaction between computers and human language. Steps involved include tokenization, parsing, semantic analysis, and generation.</p> Signup and view all the answers

What are the various ambiguities in NLP?

<p>Ambiguities in NLP include lexical ambiguity (words with multiple meanings), syntactic ambiguity (multiple possible sentence structures), and semantic ambiguity (multiple possible interpretations).</p> Signup and view all the answers

Flashcards

Artificial Intelligence (AI)

The art and science of creating machines that can perform tasks that typically require human intelligence.

Turing Test

A test of a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human.

Agent Architecture vs. Program

Architecture refers to the hardware components, while the program implements the agent's decision-making process.

Knowledge

Facts, information, and skills acquired through experience or education.

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Belief

A proposition believed to be true by an agent.

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Hypothesis

A tentative conjecture explaining an observation, phenomenon, or scientific problem that can be tested by further observation, investigation, or experimentation.

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Data

Raw, unorganized facts that need to be processed.

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Declarative Knowledge

Expressed as statements of facts (e.g., a cat is an animal).

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Procedural Knowledge

Expressed as a set of procedures or rules (e.g., how to ride a bike).

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Heuristic Knowledge

Based on experience or trial-and-error (e.g., a rule of thumb).

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Study Notes

Introduction to AI

  • Definition of Artificial Intelligence (AI) is needed.
  • Description of AI fields and application areas is required.
  • Turing Test needs definition.
  • Capabilities for a machine to pass the Turing Test must be identified.
  • Sketch the basic architecture of an intelligent agent.
  • An agent consists of architecture and an agent program.
  • Provide an example of an agent.
  • Evaluate the possibility of achieving human-level intelligence in machines.
  • Identify the challenges in such research.
  • Assess the Turing Test as an accurate measure of AI.
  • Distinguish between knowledge, belief, hypothesis, and data.
  • Briefly explain: Declarative knowledge, Procedural knowledge, and Heuristic knowledge with examples.

Problem Solving

  • Model the water-jug problem as an AI production system.
  • Model the Missionary cannibal problem as an AI production system.
  • Model the Farmer Fox Goose Grain Problem as an AI production system.
  • Identify differences between Linear Planning and Non-Linear Planning.
  • Compare Forward and Backward Chaining.
  • Explain Means End Analysis (MEA) with an example.
  • Note on MYCIN.

Intelligence

  • What is Intelligence?
  • Differences between Human Intelligence and Machine Intelligence need to be explained.

Knowledge Representation

  • Define Knowledge Representation.
  • Define First-Order Predicate Logic (FOPL)/First-Order Logic (FOL).
  • Discuss the advantages of FOPL over Proposition Logic with examples.
  • Learn how to convert to Conjunctive Normal Form (CNF).
  • Learn Proof by Resolution with practice questions.
  • Study Semantic Networks and Frames for Knowledge Representation.
  • Understand Skolemization and CNF Conversion Steps.

Inference and Reasoning

  • Note the differences between Heuristic Search, A* Search, and Greedy Search, using one example.
  • Describe the Mini-Max Algorithm and its limitations, with how to overcome them by Alpha Beta pruning.
  • Define Uncertainty and Bayes' Theorem, with an example of a Bayes Belief Network.
  • Short note on Case-Based Reasoning definition is required.

Machine Learning

  • Describe Neural Networks and Perceptrons, explaining how they work.
  • Explain Genetic Algorithms (GA) and their steps with an example.
  • Short notes on Genetic Algorithm (GA) Operators are needed.
  • Instructions to maximize equations using the GA Algorithm and to check class notes and question banks.

Applications of AI

  • Back-propagation Neural Network details are needed.
  • Multilayer Perceptron (MLP) is preferred over a single layer.
  • Define Expert Systems and their characteristics.
  • Explain the architecture of Expert Systems.
  • What is Natural Language Processing (NLP)? Explain the steps involved in NLP.
  • Identify and explain the various ambiguities in NLP.
  • Draw a parse tree for a sentence, referring to examples.

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