What is partial observability in agents?
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In Artificial Intelligence, partial observability refers to situations where an agent cannot access the complete state of its environment at any given time. That is, the agent only receives incomplete, noisy, or indirect information about the environment, making decision-making more challenging.
🔹 Definition:
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An environment is partially observable if the agent’s sensors do not provide full knowledge of the environment’s current state.
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The agent must infer or estimate missing information to act effectively.
🔹 Characteristics:
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Uncertainty in Perception – The agent doesn’t have full knowledge of all relevant variables.
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Memory or History May Be Needed – To make good decisions, the agent may need to consider past observations, not just the current one.
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Probabilistic Reasoning – Often uses models like belief states or Bayesian filters to estimate the hidden parts of the environment.
🔹 Examples:
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Robot Navigation: A robot with limited sensors cannot see the entire room and must infer obstacles or the location of objects.
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Poker Game: Players don’t see opponents’ cards, only their actions, so decisions are based on probabilities.
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Self-driving Cars: Cannot perceive all conditions perfectly due to sensor limitations or occlusions.
✅ Contrast with Full Observability:
| Feature | Full Observability | Partial Observability |
|---|---|---|
| Knowledge of state | Complete | Incomplete or noisy |
| Decision-making | Can rely only on current state | Requires inference or memory |
| Example | Chess | Poker, real-world robotics |
In short:
Partial observability occurs when an agent cannot see the full environment, and must rely on estimation, memory, and probabilistic reasoning to make decisions.
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