INTELLIGENT SYSTEMS Artificial Intelligence Applications in Business

Learning Objectives Describe decision models and the benefits of computer supported decision making and experimentation Describe artificial intelligence (AI) Compare capabilities for natural (human) intelligence versus artificial intelligence Define an expert system and identify its components Discuss intelligent system examples that illustrate various forms of problem representation and reasoning Identify intelligent systems applications in business functional areas

Decision Models Data + models = decisions Models are representations of problems that vary by degree of abstraction Examples include: Iconic (scale) models – least abstract Car or house scale model Analog models Organizational charts, blueprints Mathematical (quantitative) models Linear programs, statistical models Mental models – most abstract Consumer behavior models Other examples include visualization methods, geographic information systems, and virtual reality

Benefits of Computer Supported Decision Systems Cost of virtual experimentation is lower Compresses time Manipulations are easier Cost of mistakes is lower Can evaluate risk and uncertainty Can compare a large number of alternatives Can be used for training

Intelligent Systems Intelligent systems is a term that best describes the various commercial applications of artificial intelligence Artificial intelligence (AI) is a subfield of computer science that is concerned with studying the thought processes of humans and re-creating the effects of those processes via machines, such as computers and robots AI’s ultimate goal is to build machines that will mimic human intelligence An interesting test to determine whether a computer exhibits intelligent behavior was designed by Alan Turing (the Turing test)

Comparison of the Capabilities of Natural versus Artificial Intelligence CapabilitiesNatural IntelligenceArtificial Intelligence Preservation of knowledgePerishablePermanent Duplication and sharing of knowledge Difficult, expensive, takes time Easy, fast, and cheap when in the right format Total cost of knowledgeCan be erratic and inconsistent Consistent and thorough Documentability of knowledge Difficult, expensiveFairly easy, inexpensive CreativityCan be very highLow, uninspired Use of sensory experiencesDirect and rich in possibilities Limited Recognizing patterns and relationships Fast, easy to explainGetting better, but not as good as humans ReasoningMakes use of a wide range of experiences Good only in narrow, focused, stable domains

Expert Systems When an organization has a complex decision to make or problem to solve, it often turns to experts for advice Expert systems (ESs) are computer systems that attempt to mimic human experts by applying expertise in a specific domain The transfer of expertise from an expert to a computer and then to the user involves four activities: Knowledge acquisition Knowledge representation Knowledge inferencing Knowledge transfer

Expert System Structure

Question? What makes a system “intelligent”?

Answer Intelligent systems include one, or more, of the following capabilities: Reasoning Deductive Inductive Analogical (extremely difficult to implement in AI systems) Rationality Efficient search for answers Learning Incorporate knowledge learned from past experience to improve decision making over time

Intelligent System Examples Machine (concept) learning Case-based reasoning Decision trees Other examples: Rule-based expert systems For example, a bird classification expert system Natural language processing (NLP) NLP involves the largest knowledge base and most complex inference processes How could intelligent systems be used in: Accounting? Marketing? Manufacturing? Computer network management?