Some Major Differences Between Expert Systems And Other Systems:

MSE614 – Intelligent Manufacturing Spring 2008 – Ileana Costea, Ph.D. Session #1 - Lecture

 Introduction to course  Division in groups (4-5 students per group)  Introduction to Artificial Intelligence (AI) o Lecture (no transparencies, lecture written on the board; some material from the lecture is given below; the rest was taken by students as notes in class)

o Class Exercises

Introduction to Artificial Intelligence (AI) (Read and study Preface and Chapter 1 in textbook Luger)

 What is AI, what are Expert Systems (ES), examples of ES and AI applications

 What is intelligence (natural vs. artificial)

Natural intelligence does have several advantages over AI:

 Natural intelligence is creative, whereas AI is rather uninspired. The ability to acquire knowledge is inherent in human beings, but with AI, tailored knowledge must be built into a carefully constructed system.

 Natural intelligence enables people to benefit from and use sensory experience directly, whereas most AI systems must work with symbolic input.  Perhaps most important, human reasoning is able to make use at all times of a wide context of experience and bring that to bear on individual problems; in contrast, AI systems typically gain their power by having a very narrow focus.

The advantages of natural intelligence over AI result in the many limitations of expert systems, and will be discussed later in the course.

Computers can be used to collect information about objects, events, or processes; and, of course, computers can process large amounts of information more efficiently than people can. People, however, instinctively do some things that have been very difficult to program into a computer: they recognize relationships between things; they sense qualities; and they spot patterns that explain how various items relate to each other.

Newspaper photographs are nothing more than collections of minute dots, yet without any conscious effort, people discover the patterns that reveal faces and other objects in those photos. Similarly, one of the ways that humans make sense of the world is by recognizing the relationships and patterns that help give meaning to the objects and events that they encounter.

 Some major differences between expert systems and other systems:

AI Conventional Processing mainly symbolic processing numeric processing non-algorithmic emphasis algorithmic heuristics are a key element heuristics are seldom used emphasis is on knowledge emphasis is on models and data

AI can be documented. Decisions made by a computer can be easily documented by tracing the activities of the system. Natural intelligence is P( difficult to reproduce; for example , a person may reach a conclusion but at some later date may be unable to re-create the reasoning process that led to that conclusion or to even recall the assumptions that were a part of the decision.

If computers are to become more intelligent, they must be able to make the same kinds of associations among the qualities of objects, events, and processes that come so naturally to people.

Knowledge in AI - Definitions

In the field of information systems it is customary to distinguish between

 Data The term data refers to numeric (or alphanumeric) strings that by themselves do not have a meaning. They can be facts or figures to be processed.  Information Information is data organized so that it is meaningful to the person receiving it.  Knowledge (K) has several definitions. According to the Webster’s New World Dictionary of the American language, knowledge is:  A clear and certain perception of something  Understanding  Learning  All that has been perceived or grasped by the mind  Practical experience, skill  Acquaintance or familiarity  Cognizance; recognition  Organized information applicable to problem solving Knowledge is also information that has been organized and analyzed to make it understandable and applicable to problem solving or decision making. The collection of knowledge related to a problem (or an opportunity) to be used in an AI system is called a knowledge base (KB). Most knowledge bases are limited in that they typically focus on some specific subject or domain. Once a knowledge base is built, AI techniques are used to give the computer inference capability. The computer will then be able to make inferences and judgments based on facts and relationships contained in the KB.

Key Terms Algorithm Intuition Sensory system Artificial intelligence Knowledge (K) Speech recognition Common Sense Knowledge Base (KB) Speech understanding Conventional Programming Machine learning State Space Representation Data Natural language Symbol Expert system Natural language processing Symbol structure Inferencing Numeric processing Symbolic processing Information Pattern matching Turing Test Information overload Programming language Visual recognition Intelligent-computer-aided Robotics Vision Systems instruction Search