Artificial Intelligence, P.I Robbie Nakatsu AIMS 2710

Artificial Intelligence--AI The techniques and software that enable computers to mimic human behavior in various ways. A major thrust in this field is to develop computer functions associated with human intelligence. . Some Types of AI Expert Systems Natural Language Processing Machine Learning Robotics Intelligent Agents Logic Reasoning

AI is like magic! “Any sufficiently advanced technology is indistinguishable from magic.” --Arthur C. Clark, 1962

Robotics in Business: A Jobless Future? Robotics in agriculture: Crops like wheat, corn, and cotton can be planted, maintained and harvested mechanically. Amazon is heavily invested in robotics. Its robots are designed to move materials/products within warehouses. Momentum Machines has built a machine that is capable producing 360 hamburgers per hour.

An Expert System is an AI program that emulates the decision-making ability of a human expert An expert system captures expertise from a human expert and applies it to a problem.

An Expert System can perform diagnostic and prescriptive tasks like: Auditing and tax planning Diagnosing illnesses Commercial loan decisions Determining the cause of machine failure What is the difference between a diagnostic and prescriptive task?

People In An Expert System Domain Expert - the person who knows how to solve the problem without the aid of IT. Knowledge Engineer - the person who works with domain experts to capture knowledge they possess. The knowledge engineer builds the expert system. End User - the person who uses the expert system to solve a problem.

Components of an Expert System

Components Defined KNOWLEDGE BASE - stores the domain expertise (e.g., a collection of If-Then rules). INFERENCE ENGINE - processes the domain expertise and your problem facts to reach a conclusion. WORKING MEMORY – short term memory of the expert system; contains all the facts (initial facts as well as new facts). USER INTERFACE – part of the expert system that you use to run a consultation.

Representing Expertise as a Collection of Rules IF the light is green THEN Go through the intersection If the light is red THEN STOP If the light is yellow AND there is time to go through intersection before the light turns red THEN If the light is yellow AND there is not time to go through intersection before the light turns red THEN

A More Complex Example IF 1. The infection that requires therapy is meningitis AND 2. The patient has evidence of serious skin or soft tissue infection AND 3. Organisms were not seen on the stain of the culture AND 4. The type of infection is bacterial THEN There is evidence that the organism that might be causing the infection is Staphylococcus coagpos (0.75) or Streptococcus (0.5)

Inference Engine It is the part of the Expert System that processes the problem facts and searches for rules in the knowledge base to reach a final recommendation for a user. Two inferencing strategies : Forward Chaining is a data-driven approach in which you start with the initial problem facts, and then try to draw conclusions from them using the rules of the knowledge base. Backward Chaining is a goal-driven approach in which you start with some kind of expectation of what is to occur, or hypothesis, and then find rules that either support or contradict your hypothesis.

Illustrating Forward and Backward Chaining Knowledge Base R1: IF A and C, THEN E R2: IF D and C, THEN F R3: IF B and E, THEN F R4: IF B, THEN C R5: IF F, THEN G Two Problems: Forward Chain: Assume B and D Backward Chain: Prove or disprove G, and assume A and B

Expert System Opportunities Any activity where human experts are overburdened, undersupplied, or expensive are good candidates for ES. Expertise might be scarce in some organizations (can propagate the expertise through the use of an ES). An ES might also be used to enhance the role of an expert by providing the necessary assistance.

Benefits of Expert Systems Increased output and productivity Reduced costs, including decreased personnel required Fewer errors Better and more consistent decision-making Knowledge transfer to remote locations Formalization of organizational knowledge

Questions for thought What are some problems and limitations of expert systems? Can expert systems solve all kinds of problems?