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Introduction to AI > Introduction to AI > Flashcards

Introduction to AI Flashcards

to give information on the introduction, Definition, Future of Artificial Intelligence, Characteristics, Typical Intelligent agents, Problem-solving approach, Problem-Solving methods, example problems – searching for solutions.

1
Q

What is Artificial Intelligence (AI)?

A

AI refers to the simulation of human intelligence in machines that are programmed to think and act like humans. It involves tasks such as problem-solving, decision-making, language understanding, and visual perception.

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2
Q

What is the future of AI?

A

AI is expected to revolutionize many industries, from healthcare to transportation, through advancements in machine learning, robotics, natural language processing, and decision-making systems. The future holds potential for AI to work alongside humans, solving more complex problems with minimal human intervention.

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3
Q

What are the key characteristics of AI?

A

AI systems exhibit:

  • Learning: Acquiring knowledge and improving over time.
  • Reasoning: Using logic to infer conclusions.
  • Problem-solving: Tackling specific issues and generating solutions.
  • Perception: Interpreting sensory inputs (e.g., visual or audio).
  • Natural Language Processing: Understanding and generating human language.
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4
Q

What are typical intelligent agents?

A

Intelligent agents are systems capable of perceiving their environment, taking actions, and improving performance based on feedback. Examples include robots, chatbots, and self-driving cars.

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5
Q

What is the problem-solving approach in AI?

A

AI uses methods like search algorithms, logic, and heuristics to solve complex problems. AI systems analyze possible actions and predict outcomes before making decisions.

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6
Q

What is exhaustive search?

A

Exhaustive search (or brute-force search) systematically evaluates all possible solutions to a problem without any filtering or heuristics. It is simple but can become computationally expensive with large problem spaces.

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7
Q

What are some example problems where AI uses searching for solutions?

A

Examples include:

Tic-Tac-Toe: Using exhaustive search to evaluate all possible game states.
Water Jug Problem: Using Breadth-First Search to find solutions.
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8
Q

What are the rules of the Tic-Tac-Toe game?

A

Tic-Tac-Toe is a 2-player game on a 3x3 grid. Players alternate placing X or O, and the goal is to get 3 marks in a row (horizontally, vertically, or diagonally). If the board is full and no one wins, the game ends in a tie.

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9
Q

Where is exhaustive search useful in the real world?

A

While impractical for complex games, exhaustive search is valuable in smaller problems, such as Tic-Tac-Toe or situations where understanding all outcomes is crucial. It also serves as a foundation for more efficient algorithms like minimax in game AI and pathfinding problems.

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10
Q

What is the Water Jug Problem?

A

The Water Jug Problem involves two jugs of different capacities and a target amount of water. Breadth-First Search (BFS) explores all possible states by filling, emptying, or transferring water between jugs to reach the target.

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11
Q

What is a problem-solving approach in AI?

A

It involves defining a problem, developing a search strategy, and applying that strategy to find the solution.

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12
Q

What are key characteristics of intelligent agents?

A

Intelligent agents perceive their environment, make decisions, and act rationally to achieve their objectives.

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13
Q

What are problem-solving methods in AI?

A

They include techniques like searching, logical reasoning, and knowledge representation to solve complex problems.

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14
Q

What is exhaustive search?

A

Exhaustive search (or brute-force search) systematically evaluates all possible solutions to a problem without optimization, which can be computationally expensive for large spaces.

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15
Q

Name and describe a typical intelligent agent.

A

A typical intelligent agent is a chatbot that can communicate, gather information, and respond based on the input it receives.

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16
Q

How does AI use exhaustive search in Tic-Tac-Toe?

A

AI evaluates all possible moves and outcomes recursively, aiming for the most favorable result, such as winning or forcing a tie.

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17
Q

What is BFS?

A

BFS is a search algorithm that explores all nodes at the current depth level before moving on to nodes at the next depth level, ensuring the shortest path is found.

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18
Q

How is the Water Jug Problem solved using BFS?

A

The Water Jug Problem is solved by modeling the problem as a graph of states, where BFS explores possible jug capacities to find the desired water amount.

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19
Q

What are the limitations of exhaustive search?

A

Exhaustive search becomes computationally infeasible for large problems due to the exponential growth of the search space.

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20
Q

What are real-world applications of exhaustive search?

A

It’s used in smaller, controlled environments like puzzle-solving and simple games but is often replaced by more efficient algorithms in complex systems.

21
Q

What is an agent in the context of artificial intelligence?

A

An agent is an entity that perceives its environment through sensors and acts upon that environment through actuators, aiming to achieve specific goals.

22
Q

What are the main types of agents in AI?

A

Simple Reflex Agents: Act based on the current percept.
Model-Based Reflex Agents: Maintain an internal state to consider past percepts.
Goal-Based Agents: Act to achieve specific goals.
Utility-Based Agents: Choose actions based on a utility function to maximize satisfaction.
Learning Agents: Improve their performance based on past experiences.

23
Q

What is a simple reflex agent?

A

A simple reflex agent responds to current percepts with predefined rules, without considering past actions or states.

24
Q

How do model-based reflex agents differ from simple reflex agents?

A

Model-based reflex agents maintain an internal representation of the world, allowing them to account for past actions and percepts.

25
Q

What defines a goal-based agent?

A

A goal-based agent takes actions to achieve specific goals, using knowledge of the environment to plan its actions.

26
Q

What is the role of a utility-based agent?

A

Utility-based agents evaluate the desirability of different states and choose actions that maximize their overall utility or satisfaction.

27
Q

What distinguishes learning agents from other types of agents?

A

Learning agents can adapt and improve their performance over time by learning from experiences and feedback from the environment.

28
Q

What are the key characteristics of learning agents?

A

Adaptability: They can learn and improve in new environments.
Feedback Utilization: They use feedback to adjust their actions.
Performance Improvement: They enhance their effectiveness over time.

29
Q

What is the role of the critic in a learning agent?

A

The critic evaluates the agent’s actions based on a fixed performance standard and provides feedback on how well the agent is performing.

30
Q

What does the performance element do in a learning agent?

A

The performance element is responsible for selecting external actions based on the knowledge acquired through learning.

31
Q

Can you give an example of a problem-solving agent?

A

A navigation system that helps users find the best route to a destination by analyzing the current map and traffic conditions.

31
Q

What is the function of the problem generator in a learning agent?

A

The problem generator suggests actions that can lead to new and informative experiences, helping the agent to learn more effectively.

32
Q

Why is problem understanding crucial for problem-solving agents?

A

Problem understanding allows agents to make informed decisions based on the environment and the potential consequences of their actions.

33
Q

How do problem-solving agents formulate their goals?

A

They establish clear objectives, such as reaching a destination or completing a task, to guide their decision-making process.

34
Q

Provide an example of a utility-based agent.

A

A personal assistant app that recommends actions based on user preferences, prioritizing tasks that maximize the user’s overall satisfaction.

35
Q

What is Breadth-First Search (BFS)?

A

BFS is an algorithm used to explore a graph or tree data structure by visiting all neighboring nodes at the current depth before moving on to nodes at the next depth level.

36
Q

Why is BFS important in AI?

A

BFS is essential for solving problems such as finding the shortest path, checking graph connectivity, and traversing all vertices of a graph.

37
Q

What type of search algorithm is BFS classified as?

A

BFS is classified as an uninformed or blind search algorithm, as it operates solely based on the connectivity of nodes without heuristic knowledge.

38
Q

When is BFS optimal?

A

BFS is optimal for unweighted graphs where all actions have the same cost and when the path cost is a non-decreasing function of the depth of the node.

39
Q

What is the time complexity of the BFS algorithm?

A

The time complexity is O(bd)O(bd), where bb is the branching factor (number of nodes at each level) and dd is the depth of the shallowest solution.

40
Q

What is the space complexity of BFS?

A

The space complexity is O(bd)O(bd), as it requires memory for storing all the nodes at the current depth.

41
Q

What does it mean that BFS is complete?

A

BFS is complete, meaning it will find a solution if the shallowest goal node is at some finite depth.

42
Q

What are the main steps in the BFS algorithm?

A

Create a queue QQ.
Enqueue the root node into QQ and mark it as visited.
While QQ is not empty:
a. Dequeue a node from QQ.
b. Explore all unvisited neighbors of the dequeued node.
c. Enqueue each unvisited neighbor into QQ and mark it as visited.

43
Q

How does the queue change in a BFS example?

A

Initially, the queue contains the root node. As nodes are dequeued, their neighbors are added, creating a sequence of visited nodes in the order they are explored.

44
Q

What are some applications of the BFS algorithm?

A

Crawlers in search engines for indexing web pages.
GPS navigation systems for finding neighboring locations.
Finding the shortest path and minimum spanning tree in unweighted graphs.
Broadcasting in networking to communicate packets across nodes.

45
Q

What is the Water Jug Problem?

A

The Water Jug Problem involves having a 4-gallon and a 3-gallon jug, with the goal of measuring exactly 2 gallons in the 4-gallon jug.

46
Q

How is the state represented in the Water Jug Problem?

A

The state is represented as (x,y)(x,y), where xx is the content of the 4-gallon jug and yy is the content of the 3-gallon jug.

47
Q

What are the initial and goal states in the Water Jug Problem?

A

Start State: (0,0)(0,0) (both jugs empty)
Goal State: (2,n)(2,n) (2 gallons in the 4-gallon jug)

Introduction to AI (4 decks)

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