1 Chapter 13 Artificial Intelligence and Expert Systems

2 Artificial Intelligence Attempt to emulate the human mind in machines Robotics –Robots used to replace human laborers Artificial Vision –Allows robots that move in space sense obstacles –Used in machines for sorting and identification

3 Natural Language Process –Programs that recognize human commands Expert Systems –Programs that simulate human expertise Neural Networks –Programs built to solve problems while learning and refining their knowledge Artificial Intelligence in Business

4 Fuzzy Logic: : Fuzzy Logic: : a Special research area in computer science that allows shades of gray and does not require conditions to be black/white, yes/no, or true/false Artificial Intelligence in Business

5 Development of Expert Systems What is Expertise? –Skill and knowledge whose input into a process results in performance high above the norm. Expert system provides: –Solutions –Explanations of Problem solutions Questions n Two distinctions from DSS –1. Has potential to extend manager’s problem-solving ability –2. Ability to explain how solution was reached Components of an expert system; numbers indicate the order of the processes

6 Development of Expert Systems Components of an expert system; numbers indicate the order of the processes –The interface or dialog –The knowledge base –The interface engine

7 IF-THEN Rules –Most popular method of knowledge representation – Also called production rules –Systems hold facts in the form of IF- THEN statements Knowledge Representation Methods

8 Knowledge Engineering –Knowledge engineer: programmer who specializes in developing ESs –Asking experts appropriate questions and translating into a knowledge base –Some ESs take years Knowledge Representation Methods A knowledge engineer must know what to ask, how to ask, and how to organize the answers into a knowledge base.

9 Forward Chaining –Result-driven Process Rule is evaluated as: –(1) true, (2) false, (3) unknown Rule evaluation is an iterative process When no more rules can fire, the reasoning process stops even if a goal has not been reached Start with inputs and work to solution Knowledge Representation Methods

10 Backward Chaining –Goal-driven process ¶Divide problem into sub problems ·Try to solve one sub problem ¸Then try another Knowledge Representation Methods Start with solution and work back to inputs

11 Knowledge Representation Methods Factors Justifying the Acquisition of Expert Systems What justifies the acquisition of an ES?

12 Expert Systems in Action Business areas using ESs –Telephone network maintenance –Credit evaluation –Tax planning –Medical diagnosis –Class selection for students

13 Limitations of Expert Systems LIMITATIONS: Can handle only narrow domains Do not possess common sense Have a limited ability to learn Can be large, lengthy, expensive Many managers unwilling to trust such systems