Knowledge-Based Systems in Business Workshop PAIW-April 2003 Jay E. Aronson Professor of Management Information Systems AI Faculty Fellow Director: Master of Internet Technology Program MIS@Terry College of Business jaronson@terry.uga.edu 706/542-0991 www.terry.uga.edu/people/jaronson/ ebiz.terry.uga.edu (MIT Program) Jay E. Aronson: KBS inBusiness Workshop: PAIW April 2003

Material Adapted From Turban, Efraim, and Jay E. Aronson, Decision Support Systems and Intelligent Systems, Prentice Hall, Upper Saddle River, NJ, 6th edition, 2001/7th edition, 2004.

Outline Artificial intelligence Expert system/knowledge-based systems Knowledge Engineering Knowledge Acquisition Knowledge Representation Inferencing Expert Systems Practicum Intelligent Systems Development

Artificial Intelligence (Simple Definition) Behavior by a machine that, if performed by a human being, would be called intelligent

Make machines more useful (entrepreneurial purpose) AI Objectives Make machines smarter (primary goal) Understand what intelligence is (Nobel Laureate purpose) Make machines more useful (entrepreneurial purpose)

AI Represents Knowledge as Sets of Symbols A symbol is a string of characters that stands for some real-world concept Examples Product Defendant 0.8 Chocolate

How AI Works AI Programs Manipulate Symbols to Solve Problems Symbols and Symbol Structures Form Knowledge Representation Artificial Intelligence Dealings Primarily with Symbolic, Nonalgorithmic Problem Solving Methods

Some Major AI Areas Expert Systems Natural Language Processing Speech Understanding (Smart) Robotics and Sensory Systems Neural Computing Fuzzy Logic Genetic Algorithms Intelligent Software Agents

Expert Systems/Knowledge-Based Systems Attempt to Imitate Expert Reasoning Processes and Knowledge in Solving Specific Problems Most Popular Applied AI Technology Enhance Productivity Augment Work Forces Narrow Problem-Solving Domain or Tasks Qualitative Problem-Solving Aspects

Expert Systems Provide Direct Application of Expertise Expert Systems Do Not Replace Experts, But Makes their Knowledge and Experience More Widely Available Permits Non Experts to Work Better

Expert Systems Expertise Transferring Expertise Inferencing Rules Explanation Capability

Human Expert Behaviors Recognize and formulating the problem Solve problems quickly and properly Explain the solution Learn from experience Restructure knowledge Break rules Determine relevance Degrade gracefully

Transferring Expertise Objective of an expert system To transfer expertise from an expert to a computer system and Then on to other humans (nonexperts) Activities Knowledge acquisition Knowledge representation Knowledge inferencing Knowledge transfer to the user Knowledge is stored in a knowledge base

Inferencing Reasoning (Thinking) The computer is programmed so that it can make inferences Performed by the Inference Engine

Rules IF-THEN-ELSE Explanation Capability By the justifier, or explanation subsystem ES versus Conventional Systems

Structure of Expert Systems Development Environment Consultation (Runtime) Environment

Three Major ES Components User Interface Inference Engine Knowledge Base

All ES Components Knowledge Acquisition Subsystem Knowledge Base Inference Engine User Interface Blackboard (Workplace) Explanation Subsystem (Justifier) Knowledge Refining System User Most ES do not have a Knowledge Refinement Component

Knowledge Base The knowledge base contains the knowledge necessary for understanding, formulating, and solving problems Two Basic Knowledge Base Elements Facts Special heuristics, or rules that direct the use of knowledge Knowledge is the primary raw material of ES Incorporated knowledge representation

Inference Engine The brain of the ES The control structure (rule interpreter) Provides methodology for reasoning

User Interface Language processor for friendly, problem-oriented communication NLP, or menus and graphics

The Human Element in Expert Systems Knowledge Engineer User Others

How Expert Systems Work Major Activities of ES Implementation and Use Development Consultation Improvement

ES Shell Includes All Generic ES Components But No Knowledge EMYCIN from MYCIN (E=Empty)

Expert Systems (Rule-Based) Shells/Software Development Packages Corvid Exsys K-Vision KnowledgePro XpertRule KBS G2 Guru CLIPS JESS Many More: Free and Costly

Problem Areas Addressed by Expert Systems Interpretation systems Prediction systems Diagnostic systems Design systems Planning systems Monitoring systems Debugging systems Repair systems Instruction systems Control systems

Expert Systems Benefits Improved Decision Quality Increased Output and Productivity Decreased Decision Making Time Increased Process(es) and Product Quality Capture Scarce Expertise Can Work with Incomplete or Uncertain Information Enhancement of Problem Solving and Decision Making Improved Decision Making Processes Knowledge Transfer to Remote Locations Enhancement of Other MIS

Lead to Improved decision making Improved products and customer service Sustainable strategic advantage May enhance organization’s image

Problems and Limitations of Expert Systems Knowledge is not always readily available Expertise can be hard to extract from humans Expert system users have natural cognitive limits ES work well only in a narrow domain of knowledge Knowledge engineers are rare and expensive Lack of trust by end-users ES may not be able to arrive at valid conclusions ES sometimes produce incorrect recommendations

Expert System Success Factors Most Critical Factors Champion in Management User Involvement and Training Plus The level of knowledge must be sufficiently high There must be (at least) one cooperative expert The problem must be qualitative (fuzzy) not quantitative The problem must be sufficiently narrow in scope The ES shell must be high quality, and naturally store and manipulate the knowledge A friendly user interface Important and difficult enough problem

For Success 1. Business applications justified by strategic impact (competitive advantage) 2. Well-defined and structured applications

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Knowledge Acquisition Knowledge Engineering Knowledge acquisition, representation, validation, inferencing, explanation and maintenance Involves the cooperation of human experts Synergistic effect

Knowledge Engineering Process Activities Knowledge Acquisition Knowledge Validation Knowledge Representation Inferencing Explanation and Justification

Knowledge Engineering Process validation (test cases) Sources of knowledge (experts, others) Knowledge Acquisition Encoding Knowledge base Knowledge Representation Explanation justification Inferencing

Knowledge Acquisition Methods Manual (Interviews) Semiautomatic (Expert-driven) Automatic (Computer Aided - Induction driven)

Interviews Most Common Knowledge Acquisition: Face-to-face interviews Interview Types Unstructured (informal) Semi-structured Structured

Before a knowledge engineer interviews the expert(s) Recommendation Before a knowledge engineer interviews the expert(s) 1. Interview a less knowledgeable (minor) expert Helps the knowledge engineer Learn about the problem Learn its significance Learn about the expert(s) Learn who the users will be Understand the basic terminology Identify readable sources 2. Next read about the problem 3. Then, interview the expert(s) (much more effectively)

Induction/Knowledge Table Example Induction tables (knowledge maps) focus the knowledge acquisition process Choosing a hospital clinic facility site

Knowledge Table Exercise Choose a restaurant for lunch in Athens

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Knowledge Representation Once acquired, knowledge must be organized for use

Introduction A good knowledge representation naturally represents the problem domain An unintelligible knowledge representation is wrong Most artificial intelligence systems consist of Knowledge Base Inference Mechanism (Engine)

Many knowledge representation schemes Knowledge Base Forms the system's intelligence source Inference mechanism uses to reason and draw conclusions Inference mechanism: Examines the knowledge base to answer questions, solve problems or make decisions within the domain Many knowledge representation schemes Can be programmed and stored in memory Are designed for use in reasoning Major knowledge representation schemas: Production rules Frames

Production Rules Condition-Action Pairs IF this condition (or premise or antecedent) occurs, THEN some action (or result, or conclusion, or consequence) will (or should) occur IF the stop light is red AND you have stopped, THEN a right turn is OK

Each production rule in a knowledge base represents an autonomous chunk of expertise When combined and fed to the inference engine, the set of rules behaves synergistically Rules can be viewed as a simulation of the cognitive behavior of human experts Rules represent a model of actual human behavior

Forms of Rules IF premise, THEN conclusion IF your income is high, THEN your chance of being audited by the IRS is high

More on Rules Inclusion of ELSE More Complex Rules IF your income is high, OR your deductions are unusual, THEN your chance of being audited by the IRS is high, OR ELSE your chance of being audited is low More Complex Rules IF credit rating is high AND salary is more than $30,000, OR assets are more than $75,000, AND pay history is not "poor," THEN approve a loan up to $10,000, and list the loan in category "B.” Action part may have more information: THEN "approve the loan" and "refer to an agent"

Advantages of Rules Easy to understand (natural form of knowledge) Easy to derive inference and explanations Easy to modify and maintain Easy to combine with uncertainty Rules are frequently independent

Limitations of Rules Complex knowledge requires many rules Builders like rules (hammer syndrome) Search limitations in systems with many rules

Multiple Knowledge Representations Rules + Frames Others Knowledge Representation Must Support Acquiring knowledge Retrieving knowledge Reasoning

Considerations for Evaluating a Knowledge Representation Naturalness, uniformity and understandability Degree to which knowledge is explicit (declarative) or embedded in procedural code Modularity and flexibility of the knowledge base Efficiency of knowledge retrieval and the heuristic power of the inference procedure

Production rules and frames works well in practice No single knowledge representation method is ideally suited by itself for all tasks Multiple knowledge representations: each tailored to a different subtask Production rules and frames works well in practice Object-oriented knowledge representations Hypermedia

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Inference Techniques

Reasoning in Artificial Intelligence Knowledge must be processed (reasoned with) Computer program accesses knowledge for inferencing Inference engine or control program Rule interpreter (in rule-based systems) Directs search through the knowledge base

Inferencing with Rules: Forward and Backward Chaining Firing a rule: When all of the rule's hypotheses (the “if parts”) are satisfied Can check every rule in the knowledge base in a forward or backward direction Continues until no more rules can fire, or until a goal is achieved

Forward and Backward Chaining Chaining: Linking a set of pertinent rules Search process: directed by a rule interpreter approach: Forward chaining. If the premise clauses match the situation, then the process attempts to assert the conclusion Backward chaining. If the current goal is to determine the correct conclusion, then the process attempts to determine whether the premise clauses (facts) match the situation

Backward Chaining Goal-driven - Start from a potential conclusion (hypothesis), then seek evidence that supports (or contradicts) it Often involves formulating and testing intermediate hypotheses (or subhypotheses)

Forward Chaining Data-driven - Start from available information as it becomes available, then try to draw conclusions What to use? If all facts available up front (as in auditing) - forward chaining Diagnostic problems - backward chaining

Representing Uncertainty Numeric Graphic Symbolic

Numeric Uncertainty Representation Scale (0-1, 0-100) 0 = Complete uncertainty 1 or 100 = Complete certainty Problems with Cognitive Biases People May be Inconsistent at Different Times

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Expert Systems Demo EXSYS (Corvid)

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Intelligent Systems Development (Rapid) Prototyping

Prototyping: ES Development Life Cycle (PADI) Nonlinear process Planning Analysis Design Implementation Prototype

Software Classification: Technology Levels Expert System Applications (Specific ES) Shells Hybrid Systems Support Tools, Facilities, and Construction Aids Programming Languages

Rapid Prototyping and a Demonstration Prototype Build a small prototype Test, improve, expand Demonstrate and analyze feasibility Complete design

Rapid Prototyping Crucial to ES development Small-scale system Includes knowledge representation Small number of rules For proof of concept

What We’ve Done Basic definitions/methods/ideas Advanced definitions/methods/ideas How to KBS/ES KBS/ES with a business focus Very rich area – still much potential in business

Questions Comments Opinions Coffee?