I2ai at TU München | Flashcards & Summaries

# Lernmaterialien für i2ai an der TU München

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Agent function

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Maps any given percept sequence to an action

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Rational Agent

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Maximize its performance measure, given the prior percept sequence and its built-in knowledge

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Omniscient agent

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Knows the actual outcome of its actions, which is impossible in reality.

Example: Just imagine you know the outcome of betting money on something.

A rational agent (unequal omniscient agent) maximizes expected performance.

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Learning

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Rational agents are able to learn from perception, i.e., they improve their knowledge of the environment over time.

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Autonomy

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Rational agent is autonomous if

• less dependent on prior knowledge
• uses newly learned abilities instead.
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PEAS

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performance,

environment,

actuators,

sensors

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Properties of Task Environments

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• Fully observable vs.partially observable
• Single agent vs.multi agent
• Deterministic vs.stochastic
• Episodic vs.sequential
• Discrete vs.continuous
• Static vs.dynamic
• Known vs.unknown
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Properties of Task Environments:

Fully observable vs.partially observable

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Fully observable

Agent can detect the complete state of the environment

Partially observable otherwise.

Example: The vacuum-cleaner world is partially observable since the robot only knows whether the current square is dirty.

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Properties of Task Environments:

Single agent vs.multi agent

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Multi agent environment

Contains several agents

Single agent environment otherwise.

Example: The vacuum-cleaner world is a single agent environment. A chess game is a two-agent environment.

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Properties of Task Environments:

Deterministic vs.stochastic

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Deterministic

Next state is fully determined by its current state and the action of the agent

Stochastic otherwise.

Example: The automated taxi driver environment is stochastic since the behavior of other traffic participants is unpredictable. The outcome of a calculator is deterministic.

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Properties of Task Environments:

Episodic vs.sequential

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Episodic

actions taken in one episode (in which the robot senses and acts) does not affect later episodes

Sequential otherwise.

Example: Detecting defective parts on a conveyor belt is episodic. Chess and automated taxi driving are sequential.

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Percept sequence

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Complete history of its perception

Vacuum cleaner example: [A,Dirty],[A,Clean],[B,Clean],[A,Clean].

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Q:

Agent function

A:

Maps any given percept sequence to an action

Q:

Rational Agent

A:

Maximize its performance measure, given the prior percept sequence and its built-in knowledge

Q:

Omniscient agent

A:

Knows the actual outcome of its actions, which is impossible in reality.

Example: Just imagine you know the outcome of betting money on something.

A rational agent (unequal omniscient agent) maximizes expected performance.

Q:

Learning

A:

Rational agents are able to learn from perception, i.e., they improve their knowledge of the environment over time.

Q:

Autonomy

A:

Rational agent is autonomous if

• less dependent on prior knowledge
• uses newly learned abilities instead.
Q:

PEAS

A:

performance,

environment,

actuators,

sensors

Q:

Properties of Task Environments

A:
• Fully observable vs.partially observable
• Single agent vs.multi agent
• Deterministic vs.stochastic
• Episodic vs.sequential
• Discrete vs.continuous
• Static vs.dynamic
• Known vs.unknown
Q:

Properties of Task Environments:

Fully observable vs.partially observable

A:

Fully observable

Agent can detect the complete state of the environment

Partially observable otherwise.

Example: The vacuum-cleaner world is partially observable since the robot only knows whether the current square is dirty.

Q:

Properties of Task Environments:

Single agent vs.multi agent

A:

Multi agent environment

Contains several agents

Single agent environment otherwise.

Example: The vacuum-cleaner world is a single agent environment. A chess game is a two-agent environment.

Q:

Properties of Task Environments:

Deterministic vs.stochastic

A:

Deterministic

Next state is fully determined by its current state and the action of the agent

Stochastic otherwise.

Example: The automated taxi driver environment is stochastic since the behavior of other traffic participants is unpredictable. The outcome of a calculator is deterministic.

Q:

Properties of Task Environments:

Episodic vs.sequential

A:

Episodic

actions taken in one episode (in which the robot senses and acts) does not affect later episodes

Sequential otherwise.

Example: Detecting defective parts on a conveyor belt is episodic. Chess and automated taxi driving are sequential.

Q:

Percept sequence

A:

Complete history of its perception

Vacuum cleaner example: [A,Dirty],[A,Clean],[B,Clean],[A,Clean].

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## Das sind die beliebtesten StudySmarter Kurse für deinen Studiengang i2ai an der TU München

Für deinen Studiengang i2ai an der TU München gibt es bereits viele Kurse, die von deinen Kommilitonen auf StudySmarter erstellt wurden. Karteikarten, Zusammenfassungen, Altklausuren, Übungsaufgaben und mehr warten auf dich!

## Das sind die beliebtesten i2ai Kurse im gesamten StudySmarter Universum

##### IA

Fachhochschule Aachen

TU München