1. Define Task Environment ?

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An environment in artificial intelligence is the surrounding of the agent. The agent takes input from the environment through sensors and delivers the output to the environment through actuators.

2. Define Fully Observable vs Partially Observable ?

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When an agent sensor is capable to sense or access the complete state of an agent at each point in time, it is said to be a fully observable environment else it is partially observable.

Examples: Chess – the board is fully observable, so are the opponent’s moves

3.Define Deterministic vs Non-Deterministic ?

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When a uniqueness in the agent’s current state completely determines the next state of the agent, the environment is said to be deterministic.

• The non-deterministic environment is random in nature which is not unique and cannot be completely determined by the agent.

• Examples: Chess – there would be only a few possible moves for a coin at the current state and these moves can be determined

4.Define Single-agent vs Multi-agent?

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An environment consisting of only one agent is said to be a single-agent environment. A person left alone in a maze is an example of the single-agent system.

An environment involving more than one agent is a multi-agent environment.The game of football is multi-agent as it involves 11 players in each team.

5.Define Dynamic vs Static?

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An environment that keeps constantly changing itself when the agent is up with some action is said to be dynamic.

An idle environment with no change in its state is called a static environment.

6.Define Discrete vs Continuous?

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If an environment consists of a finite number of actions that can be deliberated in the environment to obtain the output, it is said to be a discrete environment. E.g: The game of chess .

The environment in which the actions performed cannot be numbered ie. is not discrete, is said to be continuous. Example : Self-driving cars

7.Define Episodic vs non-Episodic?

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Agents action depends only on an “episode” i.e. snapshot of the environment i.e. history dependent. Web search – episodic.

In an episodic environment, an agent's current action will not affect a future action, whereas in a non-episodic environment, an agent's current action will affect a future action and is also called the sequential environment. Chess - non-episodic.

8.Define PEAS

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PEAS stands for Performance measure, Environment, Actuator, Sensor.
1. Performance Measure: Performance measure is the unit to define the success of an agent.Performance varies with agents based on their different precept.

2. Environment: Environment is the surrounding of an agent at every instant. It keeps changing with time if the agent is set in motion.There are 5 major types of environments:      Fully Observable & Partially Observable
     Episodic & Sequential
     Static & Dynamic
     Discrete & Continuous
     Deterministic & Stochastic

3. Actuator: Actuator is a part of the agent that delivers the output of an action to the environment.

4. Sensor: Sensors are the receptive parts of an agent which takes in the input for the agent.

9.Define path cost

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It assigns a numeric cost to each path that follows the goal. The problem-solving agent selects a cost function, which reflects its performance measure. Remember, an optimal solution has the lowest path cost among all the solutions.