Artificial Intelligence Question Bank

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Course Outline

  1. Intelligent Agents and Environments
  2. Search-Based Problem Solving
  3. Heuristics and Online Search
  4. Game Playing and CSP
  5. Logical Reasoning and Knowledge Agents
  6. Probabilistic Reasoning

1. Intelligent Agents and Environments

Key Concepts & Definitions

  • Intelligent agent : Perceives its environment through sensors and acts on that environment through actuators.
  • PEAS framework : Specifies an agent's Performance measure, Environment, Actuators, and Sensors.
  • Rational agent : Selects actions expected to maximize its performance measure given its percept sequence and available knowledge.
  • Agent architecture : Intelligent agent architectures determine how percepts are transformed into actions, and they include simple reflex, model-based, goal-based, and utility-based designs.

Essential Points

  • Task environments may be deterministic, where the next state is fixed by the current state and action, or stochastic, where outcomes involve uncertainty.

2. Search-Based Problem Solving

📌 Uninformed search uses no problem-specific heuristic information, whereas informed search uses additional knowledge to guide exploration.

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Aperçu du QCM

1. How does an intelligent agent interact with its environment?

2. Which set of components is represented by the PEAS framework?

3. What criterion does a rational agent use when selecting an action?

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Aperçu des flashcards

How does an intelligent agent perceive its environment?

Through sensors.

What does the PEAS framework specify for an agent?

Performance measure, Environment, Actuators, and Sensors.

What action does a rational agent select?

The action expected to maximize its performance measure.

What determines a deterministic task environment?

The next state is fixed by the current state and action.

What characterizes a stochastic task environment?

Outcomes involve uncertainty.

What do intelligent agent architectures determine?

How percepts are transformed into actions.

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