Artificial Intelligence and Expert Systems PowerPoint Presentation

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Artificial Intelligence and Expert Systems

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Opening Vignette: “A Web-based Expert System for Wine Selection” Company background Problem description Proposed solution Results Answer and discuss the case questions

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Artificial intelligence (AI) A subfield of computer science, concerned with symbolic reasoning and problem solving 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)

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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 Understanding and inferring in a rational way Apply knowledge to manipulate the environment Thinking and reasoning Recognizing and judging the relative importance of different elements in a situation AI Objectives

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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

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AI … deals primarily with symbolic, non-algorithmic methods of problem solving represents knowledge as a set of symbols, and uses these symbols to represent problems, and apply various strategies and rules to manipulate symbols to solve problems A symbol is a string of characters that stands for some real-world concept (e.g., Product, consumer,…) Examples: (DEFECTIVE product) (LEASED-BY product customer) - LISP Tastes_Good (chocolate) Symbolic Processing

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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 qualitative features and logical and computational relationships Knowledge Base

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Evolution of artificial intelligence

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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 Advantages of Biological Natural Intelligence Is truly creative Can use sensory input directly and creatively Can apply experience in different situations

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Linguistics Psychology Philosophy Computer Science Electrical Engineering Mechanics Hydraulics Physics Optics Management and Organization Theory Chemistry The AI Field Chemistry Physics Statistics Mathematics Management Science Management Information Systems Computer hardware and software Commercial, Government and Military Organizations … AI is many different sciences and technologies It is a collection of concepts and ideas

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The AI Field… AI provides the scientific foundation for many commercial technologies

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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 Additional… Game Playing, Language Translation Fuzzy Logic, Genetic Algorithms Intelligent Software Agents AI Areas

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Anti-lock Braking Systems (ABS) Automatic Transmissions Video Camcorders Appliances Washers, Toasters, Stoves Help Desk Software Subway Control… AI is often transparent in many commercial products

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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 Works best with narrow problem areas/tasks 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)

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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, including extensive domain knowledge, heuristic rules that simplify and improve approaches to problem solving, meta-knowledge and meta-cognition, and compiled forms of behavior that afford great economy in a skilled performance Important Concepts in ES

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Experts Degrees or levels of expertise Nonexperts outnumber experts often by 100 to 1 Transferring Expertise From expert to computer to nonexperts via acquisition, representation, inferencing, transfer Inferencing Knowledge = Facts + Procedures (Rules) Reasoning/thinking performed by a computer Rules (IF … THEN …) Explanation Capability (Why? How?) Important Concepts in ES

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Features of ES Expertise Symbolic reasoning Deep knowledge – complex knowledge not easily found in non-experts Self-knowledge – provide explanations

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Applications of Expert Systems DENDRAL Applied knowledge (i.e., rule-based reasoning) Deduced likely molecular structure of compounds MYCIN A rule-based expert system Used for diagnosing and treating bacterial infections XCON A rule-based expert system Used to determine the optimal information systems configuration Applications: Credit analysis, Marketing, Finance, Manufacturing, Human resources, Science and Engineering, Education, …

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Companies Using Expert Systems Customer support at Logitech Many products web-based self-help China’s Freight Train System Allocate what and how much to load EnvaPower Market Forecaster Electricity market forecaster Rule-Based engine for mobile games SEI Investment’s Financial Diagnosis System Delivers “financial wellness” to clients

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Comparison of Conventional Systems and ES

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Comparison of Conventional Systems and ES

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Comparison of Human Experts and ES

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Comparison of Human Experts and ES

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Structures of Expert Systems Development Environment Consultation (Runtime) Environment

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Conceptual Architecture of a Typical Expert Systems

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Structure of ES Knowledge acquisition (KA) The extraction and formulation of knowledge derived from various sources, especially from experts (elicitation) Knowledge base A collection of facts, rules, and procedures organized into schemas. The assembly of all the information and knowledge about a specific field of interest Blackboard (working memory) An area of working memory set aside for the description of a current problem and for recording intermediate results in an expert system Explanation subsystem (justifier) The component of an expert system that can explain the system’s reasoning and justify its conclusions

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Expert Has the special knowledge, judgment, experience and methods to give advice and solve problems Knowledge Engineer Helps the expert(s) structure the problem area by interpreting and integrating human answers to questions, drawing analogies, posing counter examples, and enlightening conceptual difficulties User Others System Analyst, Builder, Support Staff, … The Human Element in ES

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Knowledge Engineering (KE) A set of intensive activities encompassing the acquisition of knowledge from human experts (and other information sources) and converting this knowledge into a repository (commonly called a knowledge base) The primary goal of KE is to help experts articulate how they do what they do, and to document this knowledge in a reusable form

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The Knowledge Engineering Process

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Declarative Knowledge Descriptive representation of knowledge that relates to a specific object. Shallow - Expressed in a factual statements Important in the initial stage of knowledge acquisition Procedural Knowledge Considers the manner in which things work under different sets of circumstances Includes step-by-step sequences and how-to types of instructions Metaknowledge Knowledge about knowledge Major Categories of Knowledge in ES

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How ES Work: Inference Mechanisms Knowledge representation and organization Expert knowledge must be represented in a computer-understandable format and organized properly in the knowledge base Different ways of representing human knowledge include: Production rules (IF THEN rules) Semantic networks Logic statements (T or F)

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Semantic Network

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IF premise, THEN conclusion IF your income is high, THEN your chance of being audited by the IRS is high Conclusion, IF premise Your chance of being audited is high, IF your income is high Inclusion of ELSE IF your income is high, OR your deductions are unusual, THEN your chance of being audited by the IRS is high, 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.” Forms of Rules

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Knowledge and Inference Rules Two types of rules are common in AI: Knowledge rules and Inference rules Knowledge rules (declarative rules), state all the facts and relationships about a problem Inference rules (procedural rules), advise on how to solve a problem, given that certain facts are known Inference rules contain rules about rules (metarules) Knowledge rules are stored in the knowledge base Inference rules become part of the inference engine Example: IF needed data is not known THEN ask the user IF more than one rule applies THEN fire the one with the highest priority value first

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How ES Work: Inference Mechanisms Inference is the process of chaining multiple rules together based on available data Forward chaining A data-driven search in a rule-based system If the premise clauses match the situation, then the process attempts to assert the conclusion Backward chaining A goal-driven search in a rule-based system It begins with the action clause of a rule and works backward through a chain of rules in an attempt to find a verifiable set of condition clauses

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

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Goal-driven: Start from a potential conclusion (hypothesis), then seek evidence that supports (or contradicts with) it Often involves formulating and testing intermediate hypotheses (or sub-hypotheses) Backward Chaining Investment Decision: Variable Definitions A = Have $10,000 B = Younger than 30 C = Education at college level D = Annual income > $40,000 E = Invest in securities F = Invest in growth stocks G = Invest in IBM stock Knowledge Base Rule 1: A & C -> E Rule 2: D & C -> F Rule 3: B & E -> F (invest in growth stocks) Rule 4: B -> C Rule 5: F -> G (invest in IBM)

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Data-driven: Start from available information as it becomes available, then try to draw conclusions Which One to Use? If all facts available up front - forward chaining Diagnostic problems - backward chaining Forward Chaining FACTS: A is TRUE B is TRUE Knowledge Base Rule 1: A & C -> E Rule 2: D & C -> F Rule 3: B & E -> F (invest in growth stocks) Rule 4: B -> C Rule 5: F -> G (invest in IBM)

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Inferencing Issues How do we choose between BC and FC Follow how a domain expert solves the problem If the expert first collect data then infer from it => Forward Chaining If the expert starts with a hypothetical solution and then attempts to find facts to prove it => Backward Chaining How to handle conflicting rules IF A & B THEN C IF X THEN C Establish a goal and stop firing rules when goal is achieved Fire the rule with the highest priority Fire the most specific rule Fire the rule that uses the data most recently entered

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Inferencing with Uncertainty Theory of Certainty (Certainty Factors) Certainty Factors and Beliefs Uncertainty is represented as a Degree of Belief Express the Measure of Belief Manipulate degrees of belief while using knowledge-based systems Certainty Factors (CF) express belief in an event based on evidence (or the expert's assessment) 1.0 or 100 = absolute truth (complete confidence) 0 = certain falsehood CFs are NOT probabilities CFs need not sum to 100

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Inferencing with Uncertainty Combining Certainty Factors Combining Several Certainty Factors in One Rule where parts are combined using AND and OR logical operators AND IF inflation is high, CF = 50 percent, (A), AND unemployment rate is above 7, CF = 70 percent, (B), AND bond prices decline, CF = 100 percent, (C) THEN stock prices decline CF(A, B, and C) = Minimum[CF(A), CF(B), CF(C)] => The CF for “stock prices to decline” = 50 percent The chain is as strong as its weakest link

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Inferencing with Uncertainty Combining Certainty Factors OR IF inflation is low, CF = 70 percent, (A), OR bond prices are high, CF = 85 percent, (B) THEN stock prices will be high CF(A, B) = Maximum[CF(A), CF(B)] => The CF for “stock prices to be high” = 85 percent Notice that in OR only one IF premise needs to be true

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Combining two or more rules Example: R1: IF the inflation rate is less than 5 percent, THEN stock market prices go up (CF = 0.7) R2: IF unemployment level is less than 7 percent, THEN stock market prices go up (CF = 0.6) Inflation rate = 4 percent and the unemployment level = 6.5 percent Combined Effect CF(R1,R2) = CF(R1) + CF(R2)[1 - CF(R1)]; or CF(R1,R2) = CF(R1) + CF(R2) - CF(R1)  CF(R2) Inferencing with Uncertainty Combining Certainty Factors

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Example continued… Given CF(R1) = 0.7 AND CF(R2) = 0.6, then: CF(R1,R2) = 0.7 + 0.6(1 - 0.7) = 0.7 + 0.6(0.3) = 0.88 Expert System tells us that there is an 88 percent chance that stock prices will increase For a third rule to be added CF(R1,R2,R3) = CF(R1,R2) + CF(R3) [1 - CF(R1,R2)] R3: IF bond price increases THEN stock prices go up (CF = 0.85) Assuming all rules are true in their IF part, the chance that stock prices will go up is CF(R1,R2,R3) = 0.88 + 0.85 (1 - 0.88) = 0.982 Inferencing with Uncertainty Combining Certainty Factors

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Inferencing with Uncertainty Certainty Factors - Example Rules R1: IF blood test result is yes THEN the disease is malaria (CF 0.8) R2: IF living in malaria zone THEN the disease is malaria (CF 0.5) R3: IF bit by a flying bug THEN the disease is malaria (CF 0.3) Questions What is the CF for having malaria (as its calculated by ES), if 1. The first two rules are considered to be true ? 2. All three rules are considered to be true?

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Inferencing with Uncertainty Certainty Factors - Example Questions What is the CF for having malaria (as its calculated by ES), if 1. The first two rules are considered to be true ? 2. All three rules are considered to be true? Answer 2 1. CF(R1, R2) = CF(R1) + CF(R2) – (CF(R1) * CF(R2)) = 0.8 + 0.5 – (0.8 * 0.5) = 1.3 – 0.4 = 0.9 2. CF(R1, R2, R3) = CF(R1, R2) + CF(R3) – (CF(R1, R2) * CF(R3)) = 0.9 + 0.3 – (0.9 * 0.3) = 1.2 – 0.27 = 0.93 Answer 1 1. CF(R1, R2) = CF(R1) + CF(R2) * (1 – CF(R1) = 0.8 + 0.5 * (1 - 0.8) = 0.8 – 0.1 = 0.9 2. CF(R1, R2, R3) = CF(R1, R2) + CF(R3) * (1 - CF(R1, R2)) = 0.9 + 0.3 * (1 - 0.9) = 0.9 – 0.03 = 0.93

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Explanation Human experts justify and explain their actions … so should ES Explanation: an attempt by an ES to clarify reasoning, recommendations, other actions (asking a question) Explanation facility = Justifier Explanation Purposes… Make the system more intelligible Uncover shortcomings of the knowledge bases (debugging) Explain unanticipated situations Satisfy users’ psychological and/or social needs Clarify the assumptions underlying the system's operations Conduct sensitivity analyses Explanation as a Metaknowledge

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Two Basic Explanations Why Explanations - Why is a fact requested? How Explanations - To determine how a certain conclusion or recommendation was reached Some simple systems - only at the final conclusion Most complex systems provide the chain of rules used to reach the conclusion Explanation is essential in ES Used for training and evaluation

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How ES Work: Inference Mechanisms Development process of ES A typical process for developing ES includes: Knowledge acquisition Knowledge representation Selection of development tools System prototyping Evaluation Improvement /Maintenance

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Development of ES Defining the nature and scope of the problem Rule-based ES are appropriate when the nature of the problem is qualitative, knowledge is explicit, and experts are available to solve the problem effectively and provide their knowledge Identifying proper experts A proper expert should have a thorough understanding of: Problem-solving knowledge The role of ES and decision support technology Good communication skills

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Development of ES Acquiring knowledge Knowledge engineer An AI specialist responsible for the technical side of developing an expert system. The knowledge engineer works closely with the domain expert to capture the expert’s knowledge Knowledge engineering (KE) The engineering discipline in which knowledge is integrated into computer systems to solve complex problems normally requiring a high level of human expertise

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Development of ES Selecting the building tools General-purpose development environment Expert system shell (e.g., ExSys or Corvid)… A computer program that facilitates relatively easy implementation of a specific expert system Choosing an ES development tool Consider the cost benefits Consider the functionality and flexibility of the tool Consider the tool's compatibility with the existing information infrastructure Consider the reliability of and support from the vendor

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A Popular Expert System Shell

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Development of ES Coding (implementing) the system The major concern at this stage is whether the coding (or implementation) process is properly managed to avoid errors… Assessment of an expert system Evaluation Verification Validation

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Development of ES - Validation and Verification of the ES Evaluation Assess an expert system's overall value Analyze whether the system would be usable, efficient and cost-effective Validation Deals with the performance of the system (compared to the expert's) Was the “right” system built (acceptable level of accuracy?) Verification Was the system built "right"? Was the system correctly implemented to specifications?

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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

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Capture Scarce Expertise Increased Productivity and Quality Decreased Decision Making Time Reduced Downtime via Diagnosis Easier Equipment Operation Elimination of Expensive Equipment Ability to Solve Complex Problems Knowledge Transfer to Remote Locations Integration of Several Experts' Opinions Can Work with Uncertain Information … more … ES Benefits

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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 ES sometimes produce incorrect recommendations … more … Problems and Limitations of ES

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Most Critical Factors Having a Champion in Management User Involvement and Training Justification of the Importance of the Problem Good Project Management Plus The level of knowledge must be sufficiently high There must be (at least) one cooperative expert The problem must be mostly qualitative The problem must be sufficiently narrow in scope The ES shell must be high quality, with friendly user interface, and naturally store and manipulate the knowledge ES Success Factors

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Thank You