Let’s first describe what is an agent in artificial intelligence. An intelligent agent is an autonomous entity which observes through sensors and acts upon an environment using actuators and directs its activity towards achieving goals. Intelligent agents may also learn or use knowledge to achieve their goals. They may be very simple or very complex.

When designing artificial intelligence solutions we need to consider aspects such as the the characteristics of the data (classified, unclassified, …), the nature of learning algorithms (supervised, unsupervised, …) and the nature of the environment on which the AI solution operates. We tend to spend big amounts of time in the first two aspects but it turns out, that the characteristics of the environment are one of the absolutely key elements to determine the right models for an AI solution. Understanding the characteristics of the environment is one of the first tasks that we need to do. From this point of view we can consider several categories.

Fully vs Partial observable

An environment is called fully observable if what your agent can sense at any point in time is completely sufficient to make an optimal decision. For example, we can imagina a card game where all the cards are on the table, the momentary site of all those cards is really sufficient to make an optimal choice.

An environment is called partialy observable where you need memory on the side of the agent to make the best possible decision. For example, in the poker game the cards are not openly on the table, and memorizing past moves will help you make a better decision.

Deterministic vs Stochastic

A deterministic environment is one where your agent’s actions uniquely detemine the outcome. For example, in the chess game there is really no randomness when you move a piece, the effect of moving a piece is completely predetermined and, no matter where I am going to move the same piece, the outcome is the same.

A stochastic enviroments there is a certain amount of radomness involved. Games that involve a dice, are stochastic. While you can still deterministically move your pieces, the outcome of an action also involves throwing the dice, and you cannot predict it.

Discrete vs Continuous

A discrete environment is one where you have finitely many action choices, and finitely many things you can sense. For example, the chess has finitely many board positions and finately many things you can do.

A continuous environment is one where the space of possible actions or things you could sense may be infinite. In the game of dards, throwing a dard we have infinite ways to angle it and accelerate it.

Benign vs Adversarial

In benign environments, the environment might be random, it might be stochastic, but it has no objective on its own that would contradict the own objective. Weather is benign, it might be ramdon, it might affect the outcome of your actions but it is not really out there to get you.

In adversarial environemnts, the opponent is really out to get you. In the game of chess the enviroment has the goal of defeat you. Obviously, it is much harder to find good actions in adversarial environments where the opponent actively observes you and counteracts what you are trying to achieve than in benign environments.

I have seen a few more classifications or specifications but, more or less, all of them list the same categories or very similar categories.

Note: Article based on my notes of the course Intro to Artificial Intelligence | Udacity