Understanding AI Environments: Key Characteristics

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Key Characteristics of AI Environments

  • Fully Observable vs. Partially Observable: If an agent's sensors give it access to the complete state of the environment needed to choose an action, the environment is fully observable. Otherwise, it is partially observable.
  • Deterministic vs. Stochastic: An environment is deterministic if the next state of the environment is completely determined by the current state (plus the results of your actions) and the agent's action. In a stochastic environment, there are multiple, unpredictable random outcomes. Luck is a serious component.
  • Episodic vs. Sequential:
    • In an episodic environment, the agent's experience is divided into atomic episodes. Each episode consists of the agent perceiving and then performing a single action. Subsequent episodes do not depend on what actions occurred in previous episodes - no memory. The choice of action in each episode depends only on the episode itself, not the previous states (e.g., classifying images).
    • In a sequential environment, the agent engages in a series of connected episodes. The current decision can affect future decisions (e.g., chess and driving).
  • Static vs. Dynamic:
    • A static environment does not change while the agent is thinking (e.g., chess). The passage of time as an agent deliberates is irrelevant.
    • The environment is semi-dynamic if the environment itself doesn't change with the passage of time, but the agent's performance score does.
    • Dynamic when everything changes despite what actions you are currently doing.
  • Discrete vs. Continuous: If the number of distinct percepts and actions is limited (finite), the environment is discrete; otherwise, it is continuous.
  • Single Agent vs. Multi-Agent: If the environment contains other intelligent agents, the agent needs to be concerned about strategic, game-theoretic aspects of the environment (for either cooperative or competitive - adversarial agents). Most engineering environments don't have multi-agent properties, whereas most social and economic systems get their complexity from the interactions of (more or less) rational agents. Chess is a multi-agent system (since the human is to be considered as an intelligent agent).

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