(23rd-February-2020)
An agent could assume there is no uncertainty, or it could take uncertainty in the domain into consideration. Uncertainty is divided into two dimensions: one for uncertainty from sensing and one for uncertainty about the effect of actions.
1. Sensing Uncertainty
2. Effect Uncertainty
The sensing uncertainty dimension concerns whether the agent can determine the state from the observations:
Fully observable is when the agent knows the state of the world from the observations.
Partially observable is when the agent does not directly observe the state of the world. This occurs when many possible states can result in the same observations or when observations are noisy.
Assuming the world is fully observable is often done as a simplifying assumption to keep reasoning tractable.
• In some cases an agent knows the effect of an action. That is, given a state and an action, it can accurately predict the state resulting from carrying out that action in that state.
The effect uncertainty dimension is that the dynamics can be
deterministic--when the state resulting from an action is determined by an action and the prior state or
stochastic--when there is only a probability distribution over the resulting states.
This dimension only makes sense when the world is fully observable. If the world is partially observable, a stochastic system can be modeled as a deterministic system where the effect of an action depends on some unobserved feature. It is a separate dimension because many of the frameworks developed are for the fully observable, stochastic action case.
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