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Artificial Intelligence: Modern Approaches (4th Edition) Reading Analysis Chapter 2

Artificial Intelligence: A Modern Approach (4th Edition)#

Chapter 2: Intelligent Agents#

What is an Intelligent Agent?#

  • An intelligent agent refers to a program or robot that can autonomously perform tasks.

Components of an Intelligent Agent#

  • Perception: An intelligent agent perceives the state of the environment through sensors.
    • Visual sensors: such as cameras.
    • Audio sensors: such as microphones.
    • Tactile sensors: such as touchscreens and force sensors.
    • Geolocation sensors: such as GPS.
  • Reasoning: An intelligent agent makes inferences and judgments based on the perceived information.
    • Logical reasoning: deriving conclusions using logical rules.
    • Probabilistic reasoning: making inferences using probability statistics.
    • Machine learning: making inferences by learning and acquiring knowledge.
  • Action: An intelligent agent changes the environment by executing operations.
    • Actuators: such as motors and robotic arms.
    • Communication devices: such as Wi-Fi and Bluetooth.

Classification of Intelligent Agents#

  • Simple reflex agents: They directly execute actions based on current perceptions.
  • Model-based agents: They build an internal model from observations of the environment to better perform actions.
    • Model: an abstract description of the environment.
    • Uses: predicting environmental changes, planning actions.
      • Environmental model: maps, scenes, etc.
      • Action model: how to execute tasks.
  • Learning agents: They improve performance through learning, including model-based learning and model-free learning.
    • Model-based learning: learning using an environmental model.
      • Supervised learning: learning from labeled data.
      • Reinforcement learning: learning through rewards and punishments.
    • Model-free learning: learning without using an environmental model, directly from interactions.
      • Unsupervised learning: learning by discovering patterns in data.
      • Deep learning: learning through simulating neural networks.
  • Autonomous agents: They can set goals, make plans, and self-evaluate and adjust.
    • Goals: autonomous, long-term objectives.
      • Long-term goals: achieving specific states or completing specific tasks.
      • Short-term goals: specific plans to achieve long-term goals.
    • Plans: sequential actions taken to achieve goals.
      • Plan formulation: creating feasible plans.
      • Plan execution: executing plans and continuously adjusting.
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