Decision Support and Business Intelligence Systems (9 th Ed., Prentice Hall) Chapter 12: Artificial Intelligence and Expert Systems

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 12-2 Learning Objectives Understand the basic concept and objective of artificial intelligence (AI) Become accounted with the concepts and evolution of rule-based expert systems (ES) Understand the general architecture of rule-based expert systems Learn the knowledge engineering process, a systematic way to build ES

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 12-3 Learning Objectives Learn the benefits, limitations and critical success factors of rule-based expert systems for decision support Become familiar with proper applications of ES Learn the synergy between Web and rule-based expert systems within the context of DSS Learn about tools and technologies for developing rule-based DSS Develop familiarity with an expert system development environment via hands-on exercises

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 12-4 AI has many definitions… Behavior by a machine that, if performed by a human being, would be considered intelligent “…study of how to make computers do things at which, at the moment, people are better Theory of how the human mind works Artificial Intelligence (AI)

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 12-5 Make machines smarter (primary goal) Understand what intelligence is Make machines more intelligent and useful Signs of intelligence… Learn or understand from experience Make sense out of ambiguous situations Respond quickly to new situations Use reasoning to solve problems Apply knowledge to manipulate the environment AI Objectives

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 12-6 Turing Test for Intelligence A computer can be considered to be smart only when a human interviewer, “conversing” with both an unseen human being and an unseen computer, can not determine which is which. - Alan Turing Test for Intelligence

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 12-7 AI Concepts Reasoning Inferencing from facts and rules using heuristics or other search approaches Pattern Matching Attempt to describe and match objects, events, or processes in terms of their features and logical and computational relationships Knowledge Base

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 12-8 Artificial vs. Natural Intelligence Advantages of AI More permanent Ease of duplication and dissemination Less expensive Consistent and thorough Can be documented Can execute certain tasks much faster Can perform certain tasks better than many people

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 12-9 Philosophy Computer Science Electrical Engineering Physics Optics Management and Organization Theory Chemistry The AI Field  Statistics  Mathematics  Management Science  Management Information Systems  Computer hardware and software  AI is many different sciences and technologies  It is a collection of concepts and ideas

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Major… Expert Systems Natural Language Processing Speech Understanding Robotics and Sensory Systems Computer Vision and Scene Recognition Intelligent Computer-Aided Instruction Automated Programming Neural Computing Game Playing AI Areas

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Is a computer program that attempts to imitate expert’s reasoning processes and knowledge in solving specific problems Most Popular Applied AI Technology Enhance Productivity Augment Work Forces Expert systems do not replace experts, but Make their knowledge and experience more widely available, and thus Permit non-experts to work better Expert Systems (ES)

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Expert A human being who has developed a high level of proficiency in making judgments in a specific domain Expertise The set of capabilities that underlines the performance of human experts. Important Concepts in ES

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Transferring Expertise From expert to computer to nonexperts via zcrepresentation, inferencing, transfer Inferencing Knowledge = Facts + Procedures (Rules) Reasoning/thinking performed by a computer Rules (IF … THEN …) Explanation Capability (Why? How?) Important Concepts in ES

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Structure of ES Three major components in ES are: Knowledge base Inference engine User interface

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Interpretation systems Prediction systems Diagnostic systems Repair systems Design systems Planning systems Monitoring systems Debugging systems Instruction systems Control systems, … Problem Areas Addressed by ES

Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall Knowledge is not always readily available Expertise can be hard to extract from humans Fear of sharing expertise Conflicts arise in dealing with multiple experts ES work well only in a narrow domain of knowledge Experts’ vocabulary often highly technical Knowledge engineers are rare and expensive Lack of trust by end-users Problems and Limitations of ES