Page 106 - DCAP506_ARTIFICIAL_INTELLIGENCE

Artificial Intelligence Dinesh Kumar, Lovely Professional University

Notes Unit 8: Statistical Reasoning

CONTENTS
Objectives
Introduction
8.1 Probability and Bayes Theorem

8.1.1 Probability
8.1.2 Bayes Theorem
8.2 Certainty Factors and Rule Based Systems

8.2.1 Reasoning with Certainty Factors
8.2.2 Overcoming the Bayes Rule Shortcomings
8.3 Bayesian Networks
8.3.1 Implementation
8.3.2 Reasoning in Bayesian Nets

8.4 Fuzzy Logic
8.4.1 Fuzzy Set Theory
8.5 Summary

8.6 Keywords
8.7 Review Questions
8.8 Further Readings

Objectives

After studying this unit, you will be able to:

 Understand the probability & Bayes theorem
 Discuss the certainty factors and rule based systems
 Illustrate the Bayesian network
 Understand the fuzzy logic and applications

Introduction

The (Symbolic) methods fundamentally symbolize uncertainty principle as being True, False,
or Neither True nor False. Some methods also had problems with Incomplete Knowledge and
Contradictions in the knowledge.
Statistical methods give a method for showing principles that are not certain (or uncertain) but
for which there may be some assisting (or contradictory) confirmation. Statistical methods
propose benefits in two wide scenarios: The first one is Genuine Randomness where card games
are a good instance. We may not be competent to forecast any outcomes with certainty but we
have knowledge regarding the possibility of certain items (such as like being dealt an ace) and
we can exploit this. The second one is Exceptions. Symbolic methods can symbolize this. However,

100 LOVELY PROFESSIONAL UNIVERSITY