With an example each, describe any four (4) properties of a task environment.

Sure, here are four properties of a task environment with examples:

1. Fully Observable vs Partially Observable: A task environment is fully observable if the agent can get complete, accurate, up-to-date information about the environment's state from its percepts. For example, in a game of chess, a player can see the entire board and all the pieces, making it fully observable. On the other hand, in a game of poker, a player cannot see the other players' cards, making the environment partially observable.

2. Deterministic vs Stochastic: A task environment is deterministic if the next state of the environment is completely determined by the current state and the action executed by the agent. For example, in the game of Sudoku, the placement of a number in a cell deterministically defines the state of the game. In contrast, in a stochastic environment, the next state cannot be predicted perfectly. For example, in the stock market, even if we know the current state, we cannot predict the next state with certainty.

3. Episodic vs Sequential: In an episodic task environment, the agent's current decision does not affect future decisions. For example, in a factory quality control scenario, the decision to pass or fail a single item does not depend on previous items. In a sequential environment, the current action could affect all future decisions. For example, in a game of chess, every move can drastically change the course of the game.

4. Static vs Dynamic: In a static environment, the environment does not change while the agent is deliberating. For example, a crossword puzzle is static because the puzzle does not change while the player is thinking about their next move. In a dynamic environment, the environment can change while the agent is thinking. For example, in a self-driving car scenario, the environment (other cars, pedestrians, traffic lights) can change as the car is deciding its next action.