Fully Observable vs. Partially Observable Environment in AI
Aspects |
Fully Observable Environment |
Partially Observable Environment |
---|---|---|
Access |
Complete access to the environmentâs state |
Limited or incomplete access to environmentâs state |
Information Availability |
All relevant aspects are directly observable |
Some aspects may be obscured, uncertain, or missing |
Decision-Making |
Straightforward decision-making based on complete information |
More complex due to incomplete information |
Memory Requirement |
No or minimal memory requirements. |
Needed to track previous observation. |
Solution |
Optimal and Transparent. |
Sub-optimal and unexpected. |
Example |
Chess, Tic-tac-toe |
Poker Game, Autonomous driving, Robot navigation |
Fully Observable vs. Partially Observable Environment in AI
In AI, an environment serves as an external stimulus to which the agent perceives and reacts. Through sensors, an agent receives input from the environment, and through actuators, it executes actions. The environment sets the conditions for the agent to achieve its goals.
For instance, in the case of an autonomous vehicle, factors like road conditions, traffic, weather, and speed limits are considered. In essence, the environment presents a problem to which the agent seeks to provide a solution. It determines a condition for an agent to reach its goal.
In short, an environment is a problem to which the agent is a solution.
Task environments in AI can be categorized into several fundamental types, aiding in the design of agents based on specific techniques. One such categorization includes fully observable and partially observable environments.
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