Understanding AI: Types, Applications, and Search Algorithms

Posted on Apr 21, 2024 in Computers

Q1) What is AI? Explain Categorization of Intelligent Systems

Artificial Intelligence Explained:

Artificial intelligence (AI) involves creating intelligent machines capable of performing tasks typically associated with human intelligence, such as learning, problem-solving, reasoning, decision-making, perception, and understanding.

Approaches to Achieving AI:

  • Machine learning: Training algorithms on data to learn specific tasks.
  • Deep learning: Using artificial neural networks to mimic the human brain.
  • Natural language processing: Enabling computers to understand human language.
  • Robotics: Building robots that interact with the physical world.

Categorization of Intelligent Systems:

Capability:

  • Artificial Narrow Intelligence (ANI): Systems designed for specific tasks (e.g., chess programs, spam filters).
  • Artificial General Intelligence (AGI): Hypothetical systems with human-level intelligence.

Functionality:

  • Reactive systems: Respond to the environment without memory or learning (e.g., thermostats).
  • Limited memory systems: Store and access past experiences (e.g., self-driving cars).
  • Theory of mind systems: Understand and model mental states of others (under development).

Learning approach:

  • Supervised learning: Learning from labeled data (e.g., image recognition).
  • Unsupervised learning: Finding patterns in unlabeled data (e.g., recommendation systems).
  • Reinforcement learning: Learning through trial and error (e.g., game-playing AI).

Q2) Explain Applications of AI

AI has a wide range of applications across various industries:

1. Healthcare:

  • Diagnosis and treatment
  • Drug discovery

2. Finance:

  • Algorithmic trading
  • Fraud detection

3. Education:

  • Personalized learning
  • Automated grading

4. Retail:

  • Recommendation systems
  • Inventory management

5. Autonomous Vehicles:

  • Self-driving cars

6. Customer Service:

  • Chatbots
  • Voice assistants

7. Manufacturing:

  • Predictive maintenance
  • Quality control

8. Cybersecurity:

  • Threat detection
  • Authentication

9. Marketing:

  • Targeted advertising
  • Market forecasting

10. Agriculture:

  • Precision farming

Q3) What is PEAS? State PEAS of an automated taxi driver

PEAS stands for Performance, Environment, Actuators, and Sensors. It describes the essential elements of an intelligent agent.

PEAS of an Automated Taxi Driver:

Performance:

  • Safely transport passengers
  • Minimize travel time, maximize fuel efficiency, provide comfort, follow traffic laws

Environment:

  • Roads, vehicles, weather, traffic signals

Actuators:

  • Steering, accelerator, brake, signals, horn, communication system

Sensors:

  • Cameras, LiDAR, radar, GPS, speedometer, engine sensors

Q4) Explain different Types of Agents

  1. Simple Reflex Agents: React to current percepts using condition-action rules.
  2. Model-Based Reflex Agents: Maintain an internal model of the environment.
  3. Goal-Based Agents: Make decisions based on explicit goals.
  4. Utility-Based Agents: Evaluate the desirability of outcomes.
  5. Learning Agents: Improve performance through learning.
  6. Rational Agents: Maximize expected utility.
  7. Reactive Agents: Respond directly to stimuli without internal state.
  8. Deliberative Agents: Plan and analyze before making decisions.
  9. Autonomous Agents: Operate independently.
  10. Multi-Agent Systems: Multiple agents interacting in a shared environment.

Q5) What is Uninformed Search? Explain Depth Limited Search

Uninformed Search:

Algorithms that explore a problem space without specific knowledge, relying on the problem structure.

Depth-Limited Search (DLS):

A depth-first search with a predefined depth limit to prevent infinite loops.

Features:

  • Prevents infinite loops
  • Space-efficient
  • Potentially incomplete

Applications:

  • Game playing
  • Route planning
  • Error detection

Q6) What is Informed Search? Explain Best First Search

Informed Search:

Search algorithms guided by heuristics that estimate the cost or distance to the goal.

Best-First Search:

Selects the most promising node for expansion based on a heuristic evaluation function.

Characteristics:

  • Guided by heuristics
  • Greedy approach

Advantages:

  • More efficient than uninformed search
  • Incorporates domain-specific knowledge

Disadvantages:

  • Does not guarantee optimality
  • Effectiveness depends on heuristic quality