Lecture 18-Uncertainty

# Lecture 18-Uncertainty - CS 561: Artificial Intelligence...

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CS 561: Artificial Intelligence Instructor: Sofus A. Macskassy, macskass@usc.edu TAs: Nadeesha Ranashinghe ( nadeeshr@usc.edu ) William Yeoh ( wyeoh@usc.edu ) Harris Chiu ( chiciu@usc.edu ) Lectures: MW 5:00-6:20pm, OHE 122 / DEN Office hours: By appointment Class page: http://www-rcf.usc.edu/~macskass/CS561-Spring2010/ This class will use http://www.uscden.net/ and class webpage - Up to date information - Lecture notes - Relevant dates, links, etc. Course material: [AIMA] Artificial Intelligence: A Modern Approach, by Stuart Russell and Peter Norvig. (2nd ed)

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CS561 - Lecture 18 - Macskassy - Spring 2010 2 Outline [AIMA Ch 13] Uncertainty Probability Syntax and Semantics Inference Independence and Bayes' Rule
CS561 - Lecture 18 - Macskassy - Spring 2010 3 Uncertainty Let action A t = leave for airport t minutes before flight Will A t get me there on time? Problems: 1) partial observability (road state, other drivers' plans, etc.) 2) noisy sensors (KCBS traffic reports) 3) uncertainty in action outcomes (at 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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CS561 - Lecture 18 - Macskassy - Spring 2010 4 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 AtAirportOnTime Sprinkler ! 0:99 WetGrass WetGrass ! 0:7 Rain Issues: Problems with combination, e.g., Sprinkler causes Rain ?? Probability Given the available evidence, A 25 will get me there on time with probability 0:04 Mahaviracarya (9th C.), Cardamo (1565) theory of gambling ( Fuzzy logic handles degree of truth NOT uncertainty e.g., WetGrass is true to degree 0:2 )
CS561 - Lecture 18 - Macskassy - Spring 2010 5 Probability Probabilistic assertions summarize effects of laziness : failure to enumerate exceptions, qualications, etc. ignorance : lack of relevant facts, initial conditions, etc. Subjective or Bayesian probability: Probabilities relate propositions to one's own state of knowledge e.g., P ( A 25 |no reported accidents) = 0:06 These are not claims of a “probabilistic tendency” in the current situation (but might be learned from past experience of similar situations) Probabilities of propositions change with new evidence: e.g., P ( A 25 |no reported accidents, 5 a.m.) = 0:15 (Analogous to logical entailment status KB ® , not truth.)

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## This note was uploaded on 08/26/2010 for the course CSCI 561 taught by Professor Staff during the Spring '08 term at USC.

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Lecture 18-Uncertainty - CS 561: Artificial Intelligence...

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