Artificial Intelligence PEAS Model Quiz
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Questions and Answers

What does the Performance Measure evaluate in an AI agent?

  • The quality of the sensors used
  • The outcomes of the agent’s actions against a predefined goal (correct)
  • The speed of the agent's operations
  • The complexity of the agent's environment

In the PEAS model, which component refers to the external factors an agent must consider?

  • Performance Measure
  • Environment (correct)
  • Sensors
  • Actuators

Which of the following best describes the role of actuators in an AI agent?

  • They collect data about the environment
  • They execute actions decided by the agent (correct)
  • They monitor the agent’s performance
  • They interpret sensory input for decision-making

What is the primary function of sensors in AI agents?

<p>To collect data from the environment (B)</p> Signup and view all the answers

How does the vacuum cleaner agent function based on its perceptions?

<p>It decides actions using predefined rules based on its percepts (D)</p> Signup and view all the answers

Which of the following statements is NOT true about the environment of an AI agent?

<p>It exclusively includes technological components (C)</p> Signup and view all the answers

What action would the vacuum cleaner agent take if it detected dirt?

<p>Initiate cleaning (B)</p> Signup and view all the answers

Which component of the PEAS model involves the operational methods of an agent?

<p>Actuators (D)</p> Signup and view all the answers

What criterion demonstrates that a computer is considered an intelligent entity in the context of Turing's test?

<p>A sufficient number of Player-A cannot distinguish the computer from the human. (A)</p> Signup and view all the answers

Which was the first artificial intelligence program created in the mid-1950s?

<p>Logic Theorist (D)</p> Signup and view all the answers

What was the major development that took place in 1956 regarding artificial intelligence?

<p>The term 'Artificial Intelligence' was officially coined. (B)</p> Signup and view all the answers

What significant event marked the beginning of the first AI winter?

<p>A significant decrease in funding for AI research. (B)</p> Signup and view all the answers

What characteristics did the WABOT-1 humanoid robot possess?

<p>It had a limb-control system, vision system, and conversation system. (C)</p> Signup and view all the answers

What led to the re-emergence of interest in AI in the early 1980s?

<p>The development of expert systems. (B)</p> Signup and view all the answers

In which year was the chatbot ELIZA developed?

<p>1966 (D)</p> Signup and view all the answers

What was one of the major features of high-level programming languages introduced around the birth of AI?

<p>They enhanced the ability to write complex data structures. (D)</p> Signup and view all the answers

What is the main goal of the Hill Climbing algorithm in relation to the Traveling-salesman Problem?

<p>Minimize the distance traveled (A)</p> Signup and view all the answers

Which statement is true about the backtracking behavior of the Hill Climbing algorithm?

<p>It does not backtrack as it has no memory of previous states. (B)</p> Signup and view all the answers

In the context of the state-space landscape diagram for Hill Climbing, what characterizes a global maximum?

<p>It is the state with the highest value of the objective function. (B)</p> Signup and view all the answers

What is a flat local maximum in the context of the Hill Climbing algorithm?

<p>A state where neighboring states all have the same value. (A)</p> Signup and view all the answers

What role does the Generate and Test method play in the Hill Climbing algorithm?

<p>It provides feedback to help decide the search direction. (A)</p> Signup and view all the answers

What is a primary disadvantage of the algorithm discussed?

<p>It can get stuck in an infinite loop. (C)</p> Signup and view all the answers

What does the function f(n) represent in the A* Search Algorithm?

<p>The path cost from the root node to the current node plus the estimated path cost to the goal. (A)</p> Signup and view all the answers

What is the time complexity of the discussed algorithm in the worst-case scenario?

<p>O(b^m) (D)</p> Signup and view all the answers

How does the A* Search Algorithm ensure it can find paths that are both optimal and complete?

<p>By combining uniform-cost search with a heuristic approach. (A)</p> Signup and view all the answers

What is one known issue with heuristic functions in this algorithm?

<p>A bad heuristic can increase time complexity dramatically. (D)</p> Signup and view all the answers

What data structures are utilized by the A* Search Algorithm during its execution?

<p>An open list and a closed list. (D)</p> Signup and view all the answers

Which function in the A* algorithm calculates the path cost from the start node to the current node?

<p>g(n) (D)</p> Signup and view all the answers

What is the space complexity of the algorithm in the worst-case scenario?

<p>O(b^m) (B)</p> Signup and view all the answers

What is the time complexity for Depth-Limited Search (DLS) for each depth?

<p>O(b^m) (A)</p> Signup and view all the answers

What principle does a Queue Data Structure follow?

<p>First In First Out (FIFO) (C)</p> Signup and view all the answers

Which of the following statements about Bidirectional Search is true?

<p>It can use BFS or DFS as search techniques. (C)</p> Signup and view all the answers

What is the space complexity of Bidirectional Search when BFS is used?

<p>O(b^(m/2)) (A)</p> Signup and view all the answers

Which operation adds an element to the top of a Stack?

<p>Push (D)</p> Signup and view all the answers

What is a significant advantage of using Bidirectional Search?

<p>It is typically faster than unidirectional search. (A)</p> Signup and view all the answers

What characteristic makes a heuristic admissible for a complete search?

<p>It must always underestimate the cost. (A)</p> Signup and view all the answers

What are the implications of using Depth-Limited Search regarding space complexity?

<p>Space complexity is O(b*m). (C)</p> Signup and view all the answers

What is the primary purpose of the Breadth-First Search (BFS) algorithm?

<p>To traverse a graph breadthwise. (B)</p> Signup and view all the answers

In comparison to other search methods, which statement about BFS is true?

<p>It is comparatively less efficient due to incurred costs. (D)</p> Signup and view all the answers

In a priority queue, how do elements with the same priority get served?

<p>They are dequeued in the order of their arrival. (D)</p> Signup and view all the answers

What is the time complexity of Bidirectional Search using DFS?

<p>O(b^m) (C)</p> Signup and view all the answers

Which of the following describes a key operational difference between a stack and a queue?

<p>Queues use FIFO while stacks use LIFO. (B)</p> Signup and view all the answers

What is the advantage of the ascending order priority queue?

<p>The element with the lowest value has the highest priority. (A)</p> Signup and view all the answers

What does the Enqueue operation do in a Queue?

<p>Adds an element to the rear. (A)</p> Signup and view all the answers

What happens during the Pop operation in a Stack?

<p>The last element added is removed. (B)</p> Signup and view all the answers

Flashcards

PEAS model

A framework for designing AI agents, encompassing Performance Measure, Environment, Actuators, and Sensors.

Performance Measure

A quantifiable way to evaluate how well an AI agent achieves its goal.

Environment (AI)

The external factors and conditions influencing an AI agent's actions. (e.g., layout of a room for a vacuum cleaner).

Actuators

Components that carry out the actions chosen by an AI agent.

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Sensors

Components that gather data from the environment for AI agents.

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Agent function

The process by which an AI agent maps percepts to actions.

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Percept

The information an AI agent receives from its sensors.

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Action

The agent's response or output.

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Breadth-First Search (BFS)

A graph traversal algorithm that explores a graph layer by layer, starting from a source node.

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Queue Data Structure

A linear data structure that follows the FIFO (First-In, First-Out) principle.

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Enqueue

Adding an element to the rear of a queue.

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Dequeue

Removing and returning the element from the front of a queue.

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Stack Data Structure

A linear data structure that follows the LIFO (Last-In, First-Out) principle.

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Hill Climbing

A search algorithm that iteratively moves towards a better solution by picking the neighbor with the highest value according to the objective function. It doesn't backtrack and aims for the best local solution.

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Push

Adding an element to the top of a stack.

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Local Maximum

A state in the search space that is better than its neighboring states, but there is another state with a higher value.

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Global Maximum

The state in the search space with the highest value for the objective function. It is the best possible solution among all states.

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Pop

Removing and returning the top element from a stack.

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Admissible Heuristic

A heuristic that never overestimates the cost to reach the goal.

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Flat Local Maximum

A state where the current state and all its neighbors have the same value for the objective function.

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Shoulder

A plateau region in the search space which has an uphill edge. It can lead to a better local maximum.

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DLS Time Complexity

The time complexity of Depth-Limited Search (DLS) is the sum of time complexities for each depth level. It grows exponentially with the depth of the tree.

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DLS Space Complexity

The space complexity of DLS is proportional to the maximum depth of the search tree and the branching factor. DLS uses more memory for deeper searches.

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Bidirectional Search

A search strategy that explores both forward from the start state and backward from the goal state simultaneously to find the solution path.

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Bidirectional Search Benefits

Offers faster search times and uses less memory compared to a single-direction search.

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Bidirectional Search Drawbacks

Can be complex to implement and requires knowing the goal state in advance.

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Priority Queue

A queue data structure where each element has a priority, and elements with higher priority are dequeued (removed) before lower priority elements.

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Priority Queue Properties

Elements have assigned priorities, higher priority elements are served first, elements with the same priority are served based on arrival order.

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Ascending Priority Queue

In an ascending priority queue, the element with the lowest value has the highest priority. Smaller values are served first.

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A* Search

A best-first search algorithm that finds optimal and complete paths to a goal, using both path cost (g(n)) and estimated cost to goal (h(n)).

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f(n) in A*

The estimated total path cost to the goal for a node 'n', calculated as f(n) = g(n) + h(n), where g(n) is the cost from start to 'n' and h(n) is the estimated cost from 'n' to the goal.

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Heuristic Function (h(n))

A function that estimates the cost of the shortest path from a current node 'n' to the goal node. It's a 'guess' of the remaining cost.

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Open List (A*)

A data structure that holds nodes to be evaluated in A*. It is usually a priority queue ordered by the f(n) value.

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Closed List (A*)

A data structure that holds nodes that have already been evaluated in A*. It prevents revisiting nodes.

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A* Worst Case

A* can behave like an unguided depth-first search, leading to high time and space complexity due to a poor heuristic function or infinite loop.

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A* Completeness

A* may not find a solution if it enters an infinite loop, making it incomplete.

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A* Optimality

A* guarantees a good solution, but not necessarily the optimal one, depending on the quality of the heuristic function.

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Study Notes

Search Algorithm Terminologies

  • Search Space: A collection of potential solutions a system may have.
  • Start State: The initial state from which an agent begins the search.
  • Goal State: The desired condition/outcome of the search problem.
  • Goal Test: A function that checks if the current state meets the goal criteria.
  • Search Tree: A tree representation of the search problem. The root node is the initial state.
  • Actions: A set of possible steps or operations an agent can take.
  • Transition Model: A description of how actions change the state of the system.
  • Path Cost: A function that assigns a cost to each path in the search tree.
  • Solution: An action sequence that leads from the start state to the goal state.

Properties for Search Algorithms

  • Completeness: A search algorithm is complete if, when a solution exists, it's guaranteed to find it.
  • Optimality: A search algorithm is optimal if it always finds the solution with the lowest cost.
  • Time Complexity: How much time it takes for an algorithm to complete its task.
  • Space Complexity: The maximum memory space required by the algorithm during the search.

Types of Search Algorithms

  • Uninformed Search (Blind Search): These algorithms don't use any problem-specific information.
  • Informed Search: They use problem-specific information, heuristics, to guide the search process.

Queue Data Structure

  • A queue follows the FIFO (First-In, First-Out) principle.
  • Enqueue: Adds an element to the rear of the queue.
  • Dequeue: Removes and returns the element from the front of the queue.

Stack Data Structure

  • Stack follows the LIFO (Last-In, First-Out) principle.
  • Push: Adds an element to the top of the stack.
  • Pop: Removes and returns the top element from the stack.

Breadth-First Search (BFS)

  • Explores the search space level by level.
  • Guarantees finding the shortest path in unweighted graphs
  • Completeness: Guaranteed to find a solution.

### Depth-First Search (DFS)
  • Often uses stacks or iterative implementations.
  • Explores as deeply as possible along each branch before backtracking.
  • NOT guaranteed to find the shortest path.
  • Completeness: Not complete if the search space is infinite or has loops (can get stuck in an infinite branch).

### Depth-Limited Search
  • Implements a limitation on the depth of search (to avoid infinite loops).
  • Usually used to modify DFS to be complete.

### Iterative Deepening Depth-First Search (IDDFS)
  • Combines the benefits of BFS (completeness) and DFS (memory efficiency).
  • Gradually increases the depth limit for DFS searches until a goal is found.

### Bidirectional Search
  • Runs two simultaneous searches starting from the start and goal states.
  • Stops when the two searches meet, thus reducing the search space.

Heuristic Search Algorithms

  • Uses heuristics to direct the search process, improving efficiency.
  • Examples: Best-First Search (Greedy Search) and A* Search.
  • Prioritizes paths that appear to be closest to the goal.
  • Completeness: Not guaranteed to find the solution.
  • Optimality: Not optimal.

### A* Search
  • Estimates the total cost of a solution path.
  • Use a heuristic to guide the search.
  • Guarantee optimality.

Hill Climbing Algorithm

  • A type of local search technique.
  • Aims to find a 'peak' in a search space by continuously moving towards better states.
  • NOT guaranteed to find the best solution only one local maximum.
  • May get stuck in local maxima, meaning a better solution is available but it cannot find.

Mean-Ends Analysis

  • A problem-solving technique in AI.
  • It aims to reduce the difference between the current state and the goal state.

Knowledge-Based Agents

  • Relies on a knowledge base and inference engine.
  • Knowledge base: Stores facts, rules, and other relevant information.
  • Inference engine: Uses logic and reasoning to derive new knowledge and make decisions.
  • Methods of knowledge representation: Declarative approach, procedural approach, and other representation methods.

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Description

This quiz tests your understanding of the PEAS (Performance measure, Environment, Actuators, and Sensors) model in artificial intelligence. You'll explore key components and functions of AI agents, such as their sensors and actuators, and the role of external factors. Let's see how well you grasp these fundamental concepts!

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