12 Uncertainty - Uncertainty Chapter 13 Outline Uncertainty...

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Uncertainty Chapter 13
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Outline Uncertainty Probability Syntax and Semantics Inference Independence and Bayes' Rule
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Uncertainty Let action A t = leave for airport t minutes before flight Will A t get me there on time? 1. partial observability (road state, other drivers' plans, etc.) 2. noisy sensors (traffic reports) 3. uncertainty in action outcomes (flat tire, etc.) 4. immense complexity of modeling and predicting traffic Hence a purely logical approach either 1. risks falsehood: “ A 25 will get me there on time”, or 2. leads to conclusions that are too weak for decision making: A 25 will get me there on time if there's no accident on the bridge and it doesn't rain and my tires remain intact etc etc.” ( A 1440 might reasonably be said to get me there on time but I'd have to stay overnight in the airport …)
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Methods for handling uncertainty Default or nonmonotonic logic: – Assume my car does not have a flat tire – Assume A 25 works unless contradicted by evidence Issues: What assumptions are reasonable? How to handle contradiction? Rules with fudge factors : A 25 | 0.3 get there on time Sprinkler | 0.99 WetGrass WetGrass | 0.7 Rain Issues: Problems with combination, e.g., Sprinkler causes Rain ?? Probability Model agent's degree of belief – Given the available evidence, A 25 will get me there on time with probability 0.04
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Probability Probabilistic assertions summarize effects of laziness : failure to enumerate exceptions, qualifications, etc. ignorance
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This note was uploaded on 04/05/2010 for the course CS 723 taught by Professor Sc during the Spring '10 term at Jaypee University IT.

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12 Uncertainty - Uncertainty Chapter 13 Outline Uncertainty...

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