Terminology in AI

Terminology in AI which deals with different scenarios of problem that an Intelligent Agent AKA AI need to handle as follows,

  1. Fully VS Partially Observable Environment:
    • Fully Observable Environment:
      > An agent can get complete sufficient data about the environment any given time to take optimal decision.

    Example : In card game in which agent can predict the card in persons hand at any given time.

    • Partially Observable Environment:
      >An agent can get only partial data about the environment and store the past and present state of the environment in a memory to take optimal decisions, This is the mostly often scenario in AI we need to handle.

    Example : In poker agent can predict the game as the persons past and present state to predict future move.

  2. Deterministic VS Stochastic Environment:

    • Deterministic Environment:
      > Environment that can be predicted the next state in any given time as the event are predictable

    Example: Chess, where ever the position of the piece still we can determine the next move of the opponent.

    • Stochastic Environment:
      >Environment that we cant predicted the next state as the outcome where random in nature.

    Example: Dice, As the game has random nature of dice that we cant predicted the next state deterministic instead of probability.

  3. Discrete VS Continuous Environment:

    • Discrete Environment:
      >Environment that has finite number of choices to predict the next state.

    Example: Chess, which has finite number of board positions we can predict the chess moves.

    • Continuous Environment:
      > Environment that has possible inifinite number of position or states that will make the decision process tricky.

    Example: Dart, which has infinite number of states and position of dart.

  4. Benign VS Adversarial Environment: – Weather[Random but not contradict] VS Chess

    • Benign Environment:
      > Environment the states that will not contradict its own rules/conditions byt its outcome result. Which make computing easier.

    Example: Weather, its state can be random and it will not contradict its own state like winter will not change into summer in a hour.

    • Adversarial Environment:
      > Environment the state will contridict the previous or present state of action. Which makes the computing harder.

    Example: Chess, the opponent state cant be the same / predictable all the time.

Hope you understood something out of it, Shout out comments and suggestions in comment.


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