Answers.Body: <p>When an agent senses its environment, it may receive different percepts or observations even if it is in the same state and takes the same SENSING function. This uncertainty could be due to various factors such as sensor noise, environmental variability, or partial observability.</p>
  <p>Imagine a robot navigating through a room with obstacles. The robot is equipped with a camera sensor to detect obstacles in its path. However, due to the nature of the environment and sensor noise, the camera may produce different observations even when the robot is in the same position and orientation. The robot's decision-making process would then need to take into account this uncertainty in sensing by considering all possible percepts returned by the PERCEPTS function. This allows the robot to make more robust decisions despite the inherent uncertainty in its sensing capabilities.</p>
  
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Body: <p>The <a href="https://aima.cs.berkeley.edu/" rel="nofollow noreferrer">Artificial Intelligence: A Modern Approach, 4th  edition</a> book introduces a PERCEPTS function that returns a set of possible percepts when the sensing is nondeterministic.</p>
  <p>Could someone please give a simple example of nondeterministic sensing?</p>
  
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Title: Could someone give a simple example of nondeterministic sensing?
UserDisplayName: user153245
ThreadText: # Could someone give a simple example of nondeterministic sensing?
  
  (Asked by: username_0 on 2024-04-16)
  
  The [Artificial Intelligence: A Modern Approach, 4th edition](https://aima.cs.berkeley.edu/) book introduces a PERCEPTS function that returns a set of possible percepts when the sensing is nondeterministic.
  
  Could someone please give a simple example of nondeterministic sensing?
  
  **Tags:** search
  
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  ## 1 answers
  
  ### Answer by username_1 on 2024-04-16. Score: 0
  
  When an agent senses its environment, it may receive different percepts or observations even if it is in the same state and takes the same SENSING function. This uncertainty could be due to various factors such as sensor noise, environmental variability, or partial observability.
  
  Imagine a robot navigating through a room with obstacles. The robot is equipped with a camera sensor to detect obstacles in its path. However, due to the nature of the environment and sensor noise, the camera may produce different observations even when the robot is in the same position and orientation. The robot's decision-making process would then need to take into account this uncertainty in sensing by considering all possible percepts returned by the PERCEPTS function. This allows the robot to make more robust decisions despite the inherent uncertainty in its sensing capabilities.
  
  
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