Chapter 10

1. Chapter 10 KNOWLEDGE_BASED DECISION SUPPORT

2. Learning Objectives • Define knowledge and describe the different types of knowledge • Describe the characteristics of knowledge management • Describe organizational learning and its relationship to knowledge management • Describe the knowledge management cycle • Describe the technologies that can be used in a knowledge management system • Describe different approaches to knowledge management • Describe the activities of the chief knowledge officer and others involved in knowledge management • Describe the role of knowledge management in organizational activities • Describe ways of evaluating intellectual capital in an organization

3. Learning Objectives • Describe how KMS are implemented • Describe the roles of technology, people, and management in knowledge management • Describe the benefits and drawbacks of knowledge management initiatives • Describe how knowledge management can revolutionize the way an organization functions

4. Opening Vignette: Case Study • A Knowledge-based DSS in A Chinese Chemical Plant. • Dalian Dyestuff plant is one of the largest chemical plants in China. It produces about 100 different kinds of dyes and other chemical products. With the economic reform in China, manufacturing decisions were decentralized. The plant managers were suddenly faced with the problem of determining their own production plans. Because of the size of the plant and the number of products, it became very difficult to make and appropriately change production plans, which depend on market demand. The plant also had to make purchasing decisions and decisions regarding of the disposal of environmentally damaging materials. All Right Reserved, Zhong YAO, School of E&M, BHU

5. 6.1 Concepts and Definitions • Managerial Decision Makers are Knowledge Workers • Use Knowledge in Decision Making • Accessibility to Knowledge Issue • Knowledge-Based Decision Support: Applied Artificial Intelligence All Right Reserved, Zhong YAO, School of E&M, BHU

6. 6.1 Concepts and Definitions • Artificial Intelligence (AI) is a term that (in this textbook) • Encompasses many definitions • AI involves studying human thought processes (to understand what intelligence is) • Representing thought processes on machines (such as computer and robots) • One well-published definition of AI is : 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” (Rich and Knight [1991]) • Theory of how the human mind works (Mark Fox) All Right Reserved, Zhong YAO, School of E&M, BHU

7. 6.1 Concepts and Definitions • Winston and Prendergast [1984] list three Objectives of AI • Make machines smarter (primary goal) • Understand what intelligence is (Nobel Laureate purpose) • Make machines more useful (entrepreneurial purpose) • (Winston and Prendergast [1984], The AI Business. Cambridge, MA: MIT Press) • The meaning of the term Intelligent behavior as the follows: • Or on other words, signs of Intelligence: All Right Reserved, Zhong YAO, School of E&M, BHU

8. 6.1 Concepts and Definitions • Learn or understand from experience • Make sense out of ambiguous or contradictory messages • Respond quickly and successfully to new situations • Use reasoning to in solving problems and directing conduct effectively • Deal with perplexing situations • Understand and infer in ordinary, rational ways • Apply knowledge to manipulate the environment • Think and reason • Recognize the relative importance of different elements in a situation All Right Reserved, Zhong YAO, School of E&M, BHU

9. 6.1 Concepts and Definitions • However, Although AI’s ultimate goal is to build machines that mimic human intelligence, the capabilities of current commercial AI products are far from exhibiting any significant success in the abilities just listed. Nevertheless, AI programs are continually improving, and they increase productivity and quality by automating several tasks that requires some human intelligence. All Right Reserved, Zhong YAO, School of E&M, BHU

10. 6.1 Concepts and Definitions • Testing for Intelligence • An interesting test designed to determine whether a computer exhibits intelligent behavior was designed by Alan Turing and is called the Turing test. • A computer can be considered to be smart only when a human interviewer, “conversing” with both an unseen human being and an unseen computer, could not determine which is which • The following definitions and characteristics of AI focus on decision-making and problem solving. All Right Reserved, Zhong YAO, School of E&M, BHU

11. 6.1 Concepts and Definitions • Symbolic Processing • When human experts solve problems, particularly the types that are considered appropriate for AI, they do not do it by solving sets of equations or performing other laborious mathematical computations. Instead, they choose symbols to represent the problem concepts and apply various strategies and rules to manipulate these concept. The AI approach represents knowledge as sets of symbols that stands for problem concepts. In Summary, • Use Symbols to Represent Problem Concepts • Apply Various Strategies and Rules to Manipulate these Concepts All Right Reserved, Zhong YAO, School of E&M, BHU

12. 6.1 Concepts and Definitions • A symbol is a string of characters that stands for some real-world concept • Examples • Product • Defendant • 0.8 • Chocolate • These symbols can be combined to express meaningful relationships, which calls for symbol structure. All Right Reserved, Zhong YAO, School of E&M, BHU

13. 6.1 Concepts and Definitions • Symbol Structures (Relationships) • (DEFECTIVE product) • (LEASED-BY product defendant) • (EQUAL (LIABILITY defendant) 0.8) • tastes_good (chocolate). • Interpreted to “the product is defective”, “product is leased by the defendant”, “the liability of the defendant is 0.8”, and “chocolate taste good”. However, they may be interpreted differently. This is one of the problems we encounter in building AI systems. All Right Reserved, Zhong YAO, School of E&M, BHU

14. 6.1 Concepts and Definitions • To solve a problem, an AI Programs Manipulate Symbols to Solve Problems • Symbols and Symbol Structures Form Knowledge Representation • Symbolic processing (definition) is an essential characteristics of AI. As reflected by a definition of AI in a branch of computer science: Artificial Intelligence Dealings Primarily with Symbolic, Non-algorithmic Problem- Solving Methods • These definition focuses on two characteristics: All Right Reserved, Zhong YAO, School of E&M, BHU

15. 6.1 Concepts and Definitions • Numeric versus Symbolic • Computers were originally designed specifically to process numbers (numeric processing). However, people tend to think symbolically; our intelligence seems to be based, in part, on our mental ability to manipulate symbols rather than just numbers. Although symbol processing is a core in AI, this does not mean that AI does not involve math; rather, the emphasis in AI is on the manipulation of symbols. All Right Reserved, Zhong YAO, School of E&M, BHU

16. 6.1 Concepts and Definitions • Algorithmic versus Non-algorithmic • An algorithm is a step-by-step procedure that has well-defined starting and ending points and is guaranteed to reach a solution to a specific problem. Most computer architectures readily lend themselves to this step-by-step approach. Many human reasoning processes tend to be non-algorithmic; in other words, our mental activities consist of more than just following logical, step-by-step procedures. All Right Reserved, Zhong YAO, School of E&M, BHU

17. 6.1 Concepts and Definitions • Heuristics • Heuristics are included as a key element of AI in the following definition: “AI is the branch of computer science that deals with ways of representing knowledge using symbols rather than numbers and with rules-of-thumb, or heuristics, methods for processing information.(Encyclopedia Britannica) • Inferencing • AI involves an attempt by machines to exhibit reasoning capabilities. The reasoning consists of inferencing from facts and rules using heuristics or other search approaches. AI is unique in that it makes inferencing by using a pattern Matching approach. • Pattern Matching • Attempt to describe objects, events, or processes in terms of their qualitative features and logical and computational relationships. All Right Reserved, Zhong YAO, School of E&M, BHU

18. 6.2 Artificial Intelligence versus Natural Intelligence • The potential value of AI can be better understood by contrasting it with natural, or human intelligence. • More permanent. Natural intelligence is perishable from a commercial standpoint in that workers can change their place of employment or forget information. However, AI is permanent as long as the computer systems and programs remain unchanged. • Ease of duplication and dissemination. Transferring a body of knowledge from one person to another usually requires a lengthy process of apprenticeship; even so, expertise can never be duplicated completely. However, when knowledge is embodies in a computer system, it can be copies from what computer and easily moved to another computer, sometimes across the globe. All Right Reserved, Zhong YAO, School of E&M, BHU

19. 6.2 Artificial Intelligence versus Natural Intelligence • Less expensive than the natural intelligence. There are many circumstances in which buying computer service costs less than having corresponding human power carry out the same tasks. • AI, being a computer technology, is consistent and thorough: Natural intelligence is erratic because people are erratic; they do not always perform consistently. • Can be documented. Decisions made by a computer can be easily documented by tracing the activities of the system. Natural intelligence is difficult to reproduce. For example, a person may may reach a conclusion but at some later date may be unable to recreate the reasoning process that led to that conclusion or to even recall the assumptions that were a part of the decision. All Right Reserved, Zhong YAO, School of E&M, BHU

20. 6.2 Artificial Intelligence versus Natural Intelligence • Can execute certain tasks much faster than a human • Can perform certain tasks better than many or even most people • Natural language does have several advantages over AI. Some are: • 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 use sensory experience directly, whereas most AI system must work with symbolic input and representations. All Right Reserved, Zhong YAO, School of E&M, BHU

21. 6.2 Artificial Intelligence versus Natural Intelligence • Perhaps most importantly, human beings reasoning is able to use at all times a wide context of experience bring that to bear on individual problems. In contrast, AI systems typically gain their power by having a Very Narrow Focus • Information Processing • Computers can collect and process information efficiently (such as a large amount of information) • People instinctively: • Recognize relationships between things • Sense qualities • Spot patterns indicating relationships • BUT, AI technologies can provide significant improvement in productivity and quality! All Right Reserved, Zhong YAO, School of E&M, BHU

22. 6.3 Knowledge in Artificial Intelligence • What is knowledge? (based on Sowa[1985]) • Knowledge encompasses the implicit and explicit restrictions placed upon objects (entities), operations and relationships along with general and specific heuristics and inference procedures involved in the situation being modeled. • Major characteristics that distinguishes AI from other CBIS is that AI’s major emphasis is knowledge processing (rather than data or information processing). Knowledge is now recognized as a major organization resource. • Data, information and knowledge can be classified by their degree of abstraction and by their quantity. Knowledge is the most abstraction and exists in the smallest quantity. All Right Reserved, Zhong YAO, School of E&M, BHU

23. 6.3 Knowledge in Artificial Intelligence High Knowledge Degree of Abstraction Information Data Low Quantity All Right Reserved, Zhong YAO, School of E&M, BHU

24. 6.3 Knowledge in Artificial Intelligence All Right Reserved, Zhong YAO, School of E&M, BHU

25. 6.3 Knowledge in Artificial Intelligence • Uses of knowledge • Although the computers can not have a diversity of experiences, or study and learn as the human mind can, it can use knowledge given to it by human experts. Such knowledge consists of facts, concepts, theories, heuristics methods, procedures and relationships. 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) used in an AI system is organized together and it is called a knowledge base. Most knowledge bases are limited in that they typically focus on some specific, usually narrow subject area or domain. All Right Reserved, Zhong YAO, School of E&M, BHU

26. 6.3 Knowledge in Artificial Intelligence • In fact, the narrow domain of knowledge and the fact that an AI system must involve some qualitative aspects of decision making are viewed as critical for AI application success. • Once a Knowledge base is built, AI techniques are used to give the computer inference capabilities based on the facts and relationships contained in the knowledge base. • Knowledge Bases • With a knowledge base and the ability to draw inferences from it, the computer can be put to practical use as a problem solver and decision maker. Figure shows a application of KB. All Right Reserved, Zhong YAO, School of E&M, BHU

27. 6.3 Knowledge in Artificial Intelligence Computer Outputs (answers, alternative, solution, etc. Inputs (questions, problem, etc. Knowledge Base Inferencing capability By searching the knowledge base for relevant facts and relationships, the computer can reach one or more alternative solutions to the given problems. All Right Reserved, Zhong YAO, School of E&M, BHU

28. 6.3 Knowledge in Artificial Intelligence • Knowledge Engineering (definition) • (Feigenbaum and McCorduck [1983]) • The art of bringing the principles and tools of AI research to bear on difficult applications problems requiring expert’s knowledge for their solutions. • The technical issues of acquiring this knowledge, representing it and using it appropriately to construct and explain lines of reasoning are important problems in the design of knowledge-based systems. • The art of constructing intelligent agents is both part of and an extension of the programming art. It is the art of building complex computer programs that represent and reason with knowledge of the world. All Right Reserved, Zhong YAO, School of E&M, BHU

29. 6.3 Knowledge in Artificial Intelligence • Knowledge engineering process: (Narrow scope) • Knowledge acquisition: acquisition of knowledge from human experts, books, documents, sensors, or computer files. Knowledge may be specific to the problem domain or to the problem-solving procedures, or it may be general knowledge, or it may be metaknowledge (knowledge about knowledge --- information about how experts use their knowledge to solve problems and problem-solving procudures) • Knowledge representation: Acquired knowledge is organized in an activity called knowledge representation. For example, preparation of knowledge map and encoding the knowledge in the knowledge base. All Right Reserved, Zhong YAO, School of E&M, BHU

30. 6.3 Knowledge in Artificial Intelligence • Knowledge validation: knowledge is validated and verified until its quality is acceptable. Test case results are usually shown to the experts to verify the accuracy of the ES. • Inferencing: Design of software to enable the computer to make inference based on the knowledge and specifics of a problem. Then the system can provide advice to a nonexpert user. • Explanation and justification: Design and programming of an explanation capability; e.g., programming the ability to answer question like why a specific piece of information is needed by the computer or how a certain conclusion was derived by a computer. All Right Reserved, Zhong YAO, School of E&M, BHU

31. 6.3 Knowledge in Artificial Intelligence Knowledge engineering process: A overview All Right Reserved, Zhong YAO, School of E&M, BHU

32. 6.3 Knowledge in Artificial Intelligence • Wide scope • Entire process of developing and maintaining AI • Knowledge Types • Advantaged knowledge • Base knowledge • Trivial knowledge (琐细的\一般性的) • Explicit knowledge • Objective, rational, technical • Easily documented • Easily transferred / taught / learned • Tacit knowledge • Subjective, cognitive, experiential learning • Hard to document • Hard to transfer / teach / learn • Involves a lot of human interpretation All Right Reserved, Zhong YAO, School of E&M, BHU

33. 6.3 Knowledge in Artificial Intelligence • Knowledge Management • A process of elicitation, transformation, and diffusion of knowledge throughout an enterprise so that it can be shared and thus reused • Helps organizations find, select, organize, disseminate, and transfer important information and expertise • Transforms data / information into actionable knowledge to be used effectively anywhere in the organization by anyone All Right Reserved, Zhong YAO, School of E&M, BHU

34. 6.3 Knowledge in Artificial Intelligence All Right Reserved, Zhong YAO, School of E&M, BHU

35. 6.3 Knowledge in Artificial Intelligence • KM Objectives • Create knowledge repositories • Improve knowledge access • Enhance the knowledge environment • Manage knowledge as an asset • Chief Knowledge Officer (CKO) • Set knowledge management strategic priorities • Establish a knowledge repository of best practices • Gain a commitment from senior executives to support a learning environment • Teach information seekers how to ask better and smarter questions • Establish a process for managing intellectual assets • Obtain customer satisfaction information in near real-time • Globalize knowledge management All Right Reserved, Zhong YAO, School of E&M, BHU

36. 6.4 Knowledge Acquisition • Knowledge Acquisition Difficulties • Problems in Transferring Knowledge • Expressing Knowledge • Transfer to a Machine • Number of Participants • Structuring Knowledge • Experts may lack time or not cooperate • Testing and refining knowledge is complicated • Poorly defined methods for knowledge elicitation • System builders may collect knowledge from one source, but the relevant knowledge may be scattered across several sources All Right Reserved, Zhong YAO, School of E&M, BHU

37. 6.4 Knowledge Acquisition • Knowledge Acquisition Difficulties • May collect documented knowledge rather than use experts • The knowledge collected may be incomplete • Difficult to recognize specific knowledge when mixed with irrelevant data • Experts may change their behavior when observed and/or interviewed • Problematic interpersonal communication between the knowledge engineer and the expert All Right Reserved, Zhong YAO, School of E&M, BHU

38. 6.4 Knowledge Acquisition • Knowledge Acquisition Methods • Manual • Semiautomatic • Automatic (Computer Aided) • Manual Methods - Structured Around Interviews • Process (See Figure) • Interviewing • Tracking the Reasoning Process • Observing • Manual methods: slow, expensive and sometimes inaccurate All Right Reserved, Zhong YAO, School of E&M, BHU

39. 6.4 Knowledge Acquisition • Semiautomatic Methods • Support Experts Directly (see Figure) • Help Knowledge Engineers Experts Elicitation Coding Knowledge engineer Knowledge base Documented knowledge All Right Reserved, Zhong YAO, School of E&M, BHU

40. 6.4 Knowledge Acquisition • Automatic (Computer Aided) • Expert’s and/or the knowledge engineer’s roles are minimized (or eliminated) • Induction Method (see Figure ) Computer-aided (interactive) interviewing Coding Expert Knowledge base Knowledge engineer All Right Reserved, Zhong YAO, School of E&M, BHU

41. 6.4 Knowledge Acquisition • Knowledge Modeling • The knowledge model views knowledge acquisition as the construction of a model of problem-solving behavior-- a model in terms of knowledge instead of representations • Can reuse models across applications Case histories and examples Induction system Knowledge base All Right Reserved, Zhong YAO, School of E&M, BHU

42. 6.4 Knowledge Acquisition -----Manual methods • Interviews • Most Common Knowledge Acquisition: Face-to-face interviews • Interview Types • Unstructured (informal) • Semi-structured • Structured • Tracking Methods • Techniques that attempt to track the reasoning process of an expert • From cognitive psychology • Most common formal method: Protocol Analysis All Right Reserved, Zhong YAO, School of E&M, BHU

43. 6.4 Knowledge Acquisition • Protocol Analysis: • Protocol: a record or documentation of the expert's step-by-step information processing and decision-making behavior • The expert performs a real task and verbalizes his/her thought process (think aloud) • Observations and Other Manual Methods • Case analysis • Critical incident analysis • Discussions with the users • Commentaries All Right Reserved, Zhong YAO, School of E&M, BHU

44. 6.4 Knowledge Acquisition • Conceptual graphs and models • Brainstorming • Prototyping • Multidimensional scaling • Johnson's hierarchical clustering • Performance review • Expert-driven Methods • Knowledge Engineers Typically • Lack Knowledge About the Domain • Are Expensive • May Have Problems Communicating With Experts All Right Reserved, Zhong YAO, School of E&M, BHU

45. 6.4 Knowledge Acquisition • Knowledge Acquisition May be Slow, Expensive and Unreliable • Can Experts Be Their Own Knowledge Engineers? • Expert-driven Methods May Use • Manual---Expert's Self-reports • Computer-Aided (Semiautomatic) • REFINER+ - case-based system • Visual modeling techniques • New machine learning methods to induce decision trees and rules • Tools based on repertory grid analysis All Right Reserved, Zhong YAO, School of E&M, BHU

46. 6.4 Knowledge Acquisition • Automated Knowledge Acquisition (Machine Learning) • Rule Induction • Induction: Process of Reasoning from Specific to General • In ES: Rules Generated by a Computer Program from Cases • Interactive Induction • Case-based Reasoning • For Building ES by Accessing Problem-solving Experiences for Inferring Solutions for Solving Future Problems • Cases and Resolutions Constitute a Knowledge Base All Right Reserved, Zhong YAO, School of E&M, BHU

47. 6.4 Knowledge Acquisition • Automated Knowledge Acquisition (Machine Learning) • Neural Computing • Fairly Narrow Domains with Pattern Recognition • Requires a Large Volume of Historical Cases • Intelligent Agents • KQML (Knowledge Query and Manipulation Language) for Knowledge Sharing • KIF, Knowledge Interchange Format (Among Disparate Programs) All Right Reserved, Zhong YAO, School of E&M, BHU

48. 6.5 Knowledge Representation • Knowledge representation • Once acquired, knowledge must be organized for use. • A good knowledge representation naturally represents the problem domain • An unintelligible (难解的) knowledge representation is wrong • Most artificial intelligence systems consist of: • Knowledge Base • Forms the system's intelligence source • Inference mechanism uses to reason and draw conclusions • Inference Mechanism (Engine) Examines the knowledge base to answer questions, solve problems or make decisions within the domain All Right Reserved, Zhong YAO, School of E&M, BHU

49. 6.5 Knowledge Representation • Knowledge representation • Many knowledge representation schemes • Can be programmed and stored in memory • Are designed for use in reasoning • Major knowledge representation schemas: • Production rules • Frames • Representation in Logic and other Schemas • General form of any logical process • Inputs (Premises) • Premises used by the logical process to create the output, consisting of conclusions (inferences) All Right Reserved, Zhong YAO, School of E&M, BHU

50. 6.5 Knowledge Representation • Facts known true can be used to derive new facts that are true • Symbolic logic: System of rules and procedures that permits the drawing of inferences from various premises • Basic Forms of Computational Logic • Propositional logic (or propositional calculus) • Predicate logic (or predicate calculus) All Right Reserved, Zhong YAO, School of E&M, BHU