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chapter04 - Chapter 4 Reasoning Under Uncertainty Expert...

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Chapter 4: Reasoning Under Uncertainty Expert Systems: Principles and Programming, Fourth Edition
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Expert Systems: Principles and Programming, Fourth Edition 2 Objectives Learn the meaning of uncertainty and explore some theories designed to deal with it Find out what types of errors can be attributed to uncertainty and induction Learn about classical probability, experimental, and subjective probability, and conditional probability Explore hypothetical reasoning and backward induction
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Expert Systems: Principles and Programming, Fourth Edition 3 Objectives Examine temporal reasoning and Markov chains Define odds of belief, sufficiency, and necessity Determine the role of uncertainty in inference chains Explore the implications of combining evidence Look at the role of inference nets in expert systems and see how probabilities are propagated
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Expert Systems: Principles and Programming, Fourth Edition 4 How to Expert Systems Deal with Uncertainty? Expert systems provide an advantage when dealing with uncertainty as compared to decision trees. With decision trees, all the facts must be known to arrive at an outcome. Probability theory is devoted to dealing with theories of uncertainty. There are many theories of probability – each with advantages and disadvantages.
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Expert Systems: Principles and Programming, Fourth Edition 5 What is Uncertainty? Uncertainty is essentially lack of information to formulate a decision. Uncertainty may result in making poor or bad decisions. As living creatures, we are accustomed to dealing with uncertainty – that’s how we survive. Dealing with uncertainty requires reasoning under uncertainty along with possessing a lot of common sense.
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Expert Systems: Principles and Programming, Fourth Edition 6 Theories to Deal with Uncertainty Bayesian Probability Hartley Theory Shannon Theory Dempster-Shafer Theory Markov Models Zadeh’s Fuzzy Theory
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Expert Systems: Principles and Programming, Fourth Edition 7 Dealing with Uncertainty Deductive reasoning – deals with exact facts and exact conclusions Inductive reasoning – not as strong as deductive – premises support the conclusion but do not guarantee it. There are a number of methods to pick the best solution in light of uncertainty. When dealing with uncertainty, we may have to settle for just a good solution.
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Expert Systems: Principles and Programming, Fourth Edition 8 Errors Related to Hypothesis Many types of errors contribute to uncertainty.
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