Types of environment in Artificial Intelligence
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The document discusses various types of environments in artificial intelligence, categorizing them as fully observable or partially observable, deterministic or non-deterministic, episodic or non-episodic, static or dynamic, discrete or continuous, and single agent or multiple agents. Examples illustrate each classification, such as chess for fully observable and deterministic environments, and poker for partially observable environments. Overall, understanding these distinctions is critical for designing effective AI systems.
Types of environment in Artificial Intelligence
- 1. Types Of Environment In AI 1
- 2. CONTENT Fully Observable / Partially Observable Deterministic / Non-deterministic Episodic / Non-episodic(Sequential) Static / Dynamic Discrete / Continuous Single agent / Multiple agents 2
- 3. Fully Observable/Partially Observable An agent’s sensors give it access to complete state of the environment at each point in time, then we say that the task environment is fully observable; otherwise it is only partially observable. Chess is fully observed: A player gets to see the whole board. Poker is partially observable: A player gets to see only his own cards, not the cards of everyone in the game. 3
- 4. Deterministic/Non-Deterministic If the next state of the environment is completely determined by the current state and the actions of the agent, then the environment is deterministic; otherwise it is non-deterministic. Examples: Deterministic environment: Tic Tac Toe game Self-driving vehicles are a classic example of Non- Deterministic AI processes. 4
- 5. Episodic / Non-episodic(Sequential) The agent's experience is divided into atomic "episodes" (each episode consists of the agent perceiving and then performing a single action) and the choice of action in each episode depends only on the episode itself. Episodic environments are much simpler because the agent does not need to think ahead. Sequential if current decisions affect future decisions, or rely on previous ones. Examples: Episodic environment: mail sorting system Non-episodic environment: chess game 5
- 6. Static / Dynamic If the environment does not change while an agent is acting, then it is static; otherwise it is dynamic. OR An environment is static if only the actions of an agent modify it. It is dynamic on the other hand if other processes are operating on it. Dynamic if the environment may change over time. Static if nothing (other than the agent) in the environment changes. Examples: (Static): If we add 2+2=4 this will remain same they will never be change… (Dynamic): Playing football game, other players make it dynamic.Every action there will be new reaction.. 6
- 7. Discrete / Continuous If there are a limited number of distinct, clearly defined, states of the environment, the environment is discrete. E.g. A game of chess or checkers where there are a set number of moves Continuous = Signals constantly coming into sensors, actions continually changing. E.g. Taxi driving. There could be a route from to anywhere to anywhere else. OR Simple Driving a car 7
- 8. Single agent / Multiple agents An agent operating by itself in an environment is single agent! Multi agent is when other agents are present! Examples: (Static): Automated Taxi Car, Crossword Puzzle (Multi agent): Other players in a football team (or opposing team) 8
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