Types of Environments in AI

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. There are several types of environments:

·         Fully Observable vs Partially Observable

·         Deterministic vs Stochastic

·         Competitive vs Collaborative

·         Single-agent vs Multi-agent

·         Static vs Dynamic

·         Discrete vs Continuous

1. Fully Observable vs Partially Observable

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

·         Maintaining a fully observable environment is easy as there is no need to keep track of the history of the surrounding.

·         An environment is called unobservable when the agent has no sensors in all environments.

·         Example:

• Chess – the board is fully observable, so are the opponent’s moves
• Driving – the environment is partially observable because what’s around the corner is not known

2. Deterministic vs Stochastic

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

·         Stochastic environment is random in nature which is not unique and cannot be completely determined by the agent.

·         Example:
Chess – there would be only few possible moves for a coin at the current state and these moves can be determined
Self Driving Cars – the actions of a self driving car are not unique, it varies time to time

3. Competitive vs Collaborative

·         An agent is said to be in a competitive environment when it competes against another agent to optimize the output.

·         Game of chess is competitive as the agents compete with each other to win the game which is the output.

·         An agent is said to be in a collaborative environment when multiple agents cooperate to produce the desired output.

·         When multiple self-driving cars are found on the roads, they cooperate with each other to avoid collisions and reach their destination which is the output desired.

4. Single-agent vs Multi-agent

·         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 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 10 players in each team.

5. Dynamic vs Static

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

·         A roller coaster ride is dynamic as it is set in motion and the environment keeps changing every instant.

·         An idle environment with no change in it’s state is called a static environment.

·         An empty house is static as there’s no change in the surroundings when an agent enters.

6. Discrete vs Continuous

·         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.

·         The game of chess is discrete as it has only a finite number of moves. The number of moves might vary with every game, but still, it’s finite.

·         The environment in which the actions performed cannot be numbered ie. is not discrete, is said to be continuous.

·         Self-driving cars are an example of continuous environments as their actions are driving, parking, etc. which cannot be numbered.