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Questions and Answers
What is used to refer to the information an agent’s sensors perceive?
What is used to refer to the information an agent’s sensors perceive?
What is an agent’s choice of action dependent on?
What is an agent’s choice of action dependent on?
How is an agent's behavior mathematically described?
How is an agent's behavior mathematically described?
In the vacuum-cleaner world example, what is an action the vacuum agent can take?
In the vacuum-cleaner world example, what is an action the vacuum agent can take?
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What defines a rational agent’s expected action?
What defines a rational agent’s expected action?
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Which condition is NOT considered when determining what is rational for an agent?
Which condition is NOT considered when determining what is rational for an agent?
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What contributes to the definition of a rational agent?
What contributes to the definition of a rational agent?
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Which of these statements about an agent's percept sequence is true?
Which of these statements about an agent's percept sequence is true?
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What differentiates a rational agent from a perfect agent?
What differentiates a rational agent from a perfect agent?
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In a fully observable environment, what characteristic is present?
In a fully observable environment, what characteristic is present?
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Which of the following best describes a partially cooperative multiagent environment?
Which of the following best describes a partially cooperative multiagent environment?
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What defines a deterministic environment?
What defines a deterministic environment?
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What is the PEAS framework used for?
What is the PEAS framework used for?
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How can an environment be classified as stochastic?
How can an environment be classified as stochastic?
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Which situation describes a single-agent environment?
Which situation describes a single-agent environment?
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What is a characteristic of a partially observable environment?
What is a characteristic of a partially observable environment?
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What characterizes episodic tasks compared to sequential tasks?
What characterizes episodic tasks compared to sequential tasks?
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What defines a dynamic environment for an agent?
What defines a dynamic environment for an agent?
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In which environment does the agent need to learn how it operates?
In which environment does the agent need to learn how it operates?
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Which of the following best describes simple reflex agents?
Which of the following best describes simple reflex agents?
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What is the main advantage of static environments for agents?
What is the main advantage of static environments for agents?
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How is the agent function related to the agent program?
How is the agent function related to the agent program?
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What is one challenge faced by AI when designing an agent program?
What is one challenge faced by AI when designing an agent program?
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Which type of agent programming requires classes of actions based on goals?
Which type of agent programming requires classes of actions based on goals?
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What distinguishes a simple reflex agent from a model-based reflex agent?
What distinguishes a simple reflex agent from a model-based reflex agent?
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What type of knowledge is required by a model-based reflex agent to update its internal state?
What type of knowledge is required by a model-based reflex agent to update its internal state?
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What is the primary function of the UPDATE-STATE function in model-based reflex agents?
What is the primary function of the UPDATE-STATE function in model-based reflex agents?
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In goal-based agents, what additional aspect is tracked alongside the state of the world?
In goal-based agents, what additional aspect is tracked alongside the state of the world?
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How do simple reflex agents generate their actions?
How do simple reflex agents generate their actions?
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What is the main role of the CONDITION-ACTION rule in simple reflex agents?
What is the main role of the CONDITION-ACTION rule in simple reflex agents?
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What do model-based reflex agents need to encode to track world states effectively?
What do model-based reflex agents need to encode to track world states effectively?
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What is a characteristic limitation of simple reflex agents?
What is a characteristic limitation of simple reflex agents?
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What does a utility-based agent use to choose actions?
What does a utility-based agent use to choose actions?
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What advantage does learning provide to agents in unknown environments?
What advantage does learning provide to agents in unknown environments?
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What role does the critic play in a learning agent?
What role does the critic play in a learning agent?
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Which representation splits each state into variables or attributes?
Which representation splits each state into variables or attributes?
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What is the primary responsibility of the problem generator in a learning agent?
What is the primary responsibility of the problem generator in a learning agent?
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How does the learning element influence the performance element in an agent?
How does the learning element influence the performance element in an agent?
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What defines an atomic representation of a state in the context of agents?
What defines an atomic representation of a state in the context of agents?
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What is a key characteristic of structured representations in intelligent agents?
What is a key characteristic of structured representations in intelligent agents?
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Study Notes
Intelligent Agents
- An agent is anything that perceives its environment through sensors and acts upon it through actuators.
- A percept is the content of an agent's sensors.
- A percept sequence is the agent's complete history of everything it has perceived.
- An agent's choice of action at any given instant can depend on its built-in knowledge, the entire percept sequence observed up to that point, but not on anything it hasn't perceived.
- An agent's behavior is described by an agent function that maps any given percept sequence to an action.
- An agent program implements the agent function.
- An agent architecture is the physical computing device that runs the program with sensors and actuators.
- Agent = Architecture + Program
- The agent program takes the current percept as input and returns an action to the actuators.
- The difference between agent program (current percept) and the agent function (entire percept history).
Rationality
- Rationality at any moment depends on:
- Performance measure (success criterion)
- Prior knowledge of the environment
- The actions the agent can perform
- The agent's percept sequence to date
- A rational agent selects an action that is expected to maximize its performance measure given the available evidence and built-in knowledge, for every possible percept sequence.
Environment Properties
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1. Fully Observable vs. Partially Observable:
- Fully observable: agent's sensors provide complete information about the environment's state at each point in time.
- Partially observable: sensors don't provide a complete state, or state is missing due to noisy/inaccurate sensors
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2. Single-agent vs. Multiagent:
- Single-agent: an agent solving a crossword puzzle
- Multiagent: competitive (chess) and partially cooperative (taxi driving)
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3. Deterministic vs. Nondeterministic:
- Deterministic: environment's next state is fully determined by the current state and the agent's action
- Nondeterministic: environment's next state isn't fully determined (not predictable)
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4. Episodic vs. Sequential:
- Episodic: agent's experience is divided into atomic episodes (e.g., each episode is independent from the others; one choice/action).
- Sequential: current decision affects all future decisions (e.g., a game of chess).
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5. Static vs. Dynamic:
- Static: environment doesn't change while the agent is deliberating.
- Dynamic: environment changes during deliberation.
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6. Discrete vs. Continuous:
- Discrete: a finite set of possible states and actions.
- Continuous: infinite number of possible states and actions
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7. Known vs. Unknown:
- Known: outcomes or outcome probabilities are known.
- Unknown: agent needs to learn how the environment works.
Agent Programs (Structures)
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Simple Reflex Agents:
- Agents select actions based solely on the current percept.
- They ignore the rest of the percept history.
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Model-based Reflex Agents:
- Maintain some internal state to reflect aspects of the environment that are not present in the current percept. Maintain a model of how the world evolves.
- Use a "transition model" (how the world changes), and a "sensor model" (how the world state reflects in agent's perceptions). A key function is "UPDATE-STATE".
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Goal-based Agents:
- Track current world state and a set of goals.
- Choose actions that will lead to achieving goals.
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Utility-based Agents:
- Track world state, and use a "utility function" to measure preferences among states.
- Choose actions to maximize expected utility.
Learning Agents
- Any type of agent can be a learning agent (or not).
- Learning allows agents to operate in initially unknown environments and become more competent than their initial knowledge alone might allow. Learning agents have a "performance element" program to select actions and a "learning element" to improve performance.
- A "critic" provides feedback on agent performance to the learning element. A "problem generator" suggests actions to gather new, informative experiences.
Agent Representation
- Atomic: No internal structure; each state is indivisible.
- Factored: States broken into variables with values.
- Structured: Representations like relational databases, representing objects and relationships.
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Description
Explore the fundamentals of intelligent agents, including their perception, behavior, and rationality. This quiz delves into how agents operate through sensors and actuators and the functions that guide their actions based on percept sequences. Test your understanding of these concepts and their implications in artificial intelligence.
