Chapter 16 Knowledge Application Systems Systems that Utilize

Chapter 16 Knowledge Application Systems: Systems that Utilize Knowledge 1

Chapter Objectives • Describe knowledge application mechanisms, which facilitate direction and routines. • Explain knowledge application technologies, which support direction and routines including: w w expert systems decision support fault diagnosis (or troubleshooting) systems help desk systems. Ch 2

Knowledge application systems • Knowledge Application Systems support the process through which some individuals utilize knowledge possessed by other individuals without actually acquiring, or learning, that knowledge. • Examples are w w Expert systems Decision support system Fault Diagnosis System Help. Desk Systems, etc Ch 3

Expert System • an expert system is a computer system that emulates the decision-making ability of a human expert. • Expert systems are designed to solve complex problems by reasoning about knowledge represented primarily as if–then rules rather than through conventional procedural code. • The first expert systems were created in the 1970 s and then proliferated in the 1980 s Ch 4

Expert systems. . . • Expert systems were among the first truly successful forms of Artificial Intelligence software. • An expert system is divided into two subsystems: the inference engine and the knowledge base. • The knowledge base represents facts and rules. • The inference engine applies the rules to the known facts to deduce new facts or predictions. Ch 5

Simple Example of ES • IF patient has pain THEN prescribe pain killers (priority 10) • IF patient has chest pain THEN treat for heart disease (priority 100) • IF patient has pain AND patient is over 60 AND patient has a history of heart conditions THEN take to emergency room Ch 6

The Architecture of Expert Systems • Expert knowledge derived from human experts • Purpose: w Diagnose illnesses w Provide recommendations w Solve other problems 7

The Architecture of Expert Systems (2) • Knowledge base: database of rules (domain knowledge). • Explanation system: explains the decisions the system makes. • User Interface: the means by which the user interacts with the expert system. • Knowledge base editor: allows the user to edit the information in the knowledge base. 8

Decision support system • A Decision Support System (DSS) is an interactive, flexible, and adaptable a computer-based information system, specially developed for supporting the solution of a non-structured management problem for improved decision making. • A properly designed DSS help decision makers compile useful information from a combination of raw data, documents, personal knowledge, or business models to identify and solve problems and make decisions. 9

Capabilities of DSS 1. 2. 3. 4. 5. 6. 7. 8. 9. Provide support in semi-structured and unstructured situations Support for various managerial levels Support to individuals and groups Support to interdependent and/or sequential decisions Support all phases of the decision-making process Support a variety of decision-making processes and styles Are adaptive Have user friendly interfaces Ch 10

Capabilities. . . 1. Goal is to improve the effectiveness of decision making types 2. The decision maker controls the decision-making process 3. End-users can build simple systems 4. Utilizes models for analysis 5. Provides access to a variety of data sources, formats, and types Decision makers can make better, more consistent decisions in a timely manner Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson Copyright 1998, Prentice Hall, Upper Saddle River, NJ Ch 11

Fault diagnosis Systems • Fault diagnosis is increasingly becoming a major emphasis for the development of knowledge applications systems • CABER at Lockheed Martin Corporation is one of the earliest successful implementations of knowledge application systems for the diagnosis and recovery of faults in large multistation milling machine tools Ch 12

Fault diagnosis. . . • The milling machines are equipped with selfdiagnostic capabilities, typically they resolved only 20 percent to 40 percent of the systems faults. • The field service engineers collected over 10, 000 records for the creation of the case library that supports CABER system, • CABER augmented the self-diagnostic capabilities of the milling machine, which provided junior fieldservice engineers with the necessary tools to resolve the fault and reduce machine downtime. Ch 13

Help Desk Systems • A system that is developed to support customers as help desk workers • For example Compaq Computer Corporation implemented a help desk support technology named SMART to assist help desk employees track calls and resolve customer service problems • SMART is an integrated call-tracking and problem-solving system, supported by hundreds of cases that help resolve diagnostic problems resulting from the use of Compaq products Ch 14

Help Desk. . • The system automatically retrieves from the case library historical cases similar to the one currently faced by the customer • The customer service representative then uses that solution to help customers solve the problem at hand. • SMART developers reported an increase from 50 percent to 87 percent of the problems that could be resolved directly by the first level of customer support. Ch 15

Technologies for Applying Knowledge • Artificial Intelligence (AI) is the area of computer science that deals with the design and development of computer systems that exhibit human-like cognitive capabilities. . • AI refers to enabling computers to perform tasks that resemble human thinking ability • Definitions for AI range from: systems that act like humans, systems that think like humans, systems that think rationally, to systems that act rationally (Russell and Norvig 2002). Ch 16

AI Defined as • The science that provides computers with the ability to represent and manipulate symbols so they can be used to solve problems not easily solved through algorithmic models. • Knowledge is associated with the cognitive symbols we manipulate, while human intelligence refers to our ability to learn and communicate in order to solve problems • Some AI systems try to imitate the problem-solving capabilities of skillful problem-solvers in a particular domain. Ch 17

AI. . . • One of the areas in AI that has witnessed the greatest popularity is knowledge-based systems, which we refer to here as knowledge application systems. • Other areas of research within AI include natural language understanding, classification, diagnostics, design, machine learning, planning and scheduling, robotics, and computer vision. • We are interested in Knowledge application systems Ch 18

Knowledge application systems • Two most relevant intelligent technologies or called knowledge based systems that underpin the development of knowledge application systems are w rule-based expert systems and w case-based reasoning. • Generally development of knowledge application systems requires eliciting the knowledge from the expert and representing it a form that is usable by computers Ch 19

Rule Based Systems • Traditionally, the development of knowledgebased systems had been based on the use of rules or models to represent the domain knowledge. • It requires the collaboration of a subject matter expert with a knowledge engineer, the latter being responsible for the elicitation and representation of the expert’s knowledge. . Ch 20

Rule based. . . • Knowledge elicitation process is called knowledge engineering. • Knowledge engineers typically build knowledge application systems by first interviewing in detail the domain expert and representing the knowledge more commonly in a set of heuristics, or rules-of-thumb. • In order for the computer to understand these rules-of-thumb, we represent them as production rules or IF-THEN statements. Ch 21

Rule based. . . • For example: IF the number of employees is less than 500, THEN the firm is a small business is one of the rules that the SOS Advisor checks to ensure the firm is eligible for the SBIR/STTR program • The IF portion is the condition (also premise or antecedent), which tests the truth-value of a set of assertions. If the statement is true, the THEN part of the rule (also action, conclusion, or consequence) is also inferred as a fact. Ch 22

Rule based. . . • In addition to rules, other paradigms to represent knowledge include frames, predicates, associative networks, and objects. • Rule-based systems have posed some disadvantages. • The number of rules that may be needed to properly represent the domain may be quite large. • For example, the Gen. AID system has about 10, 000 rules when it was first deployed. Ch 23

Examples of Rule Base systems • MYCIN was a joint venture between Dept. of Computer Science and the Medical School of Stanford University. • MYCIN was designed to solve the problem of diagnosing and recommending treatments for meningitis and blood infections. • Teaching Aids such as SCHOLAR which gives Geography Tutorials and • SOPHIE which teaches how to detect breakdown in electrical circuits. Ch 24

Case Based Reasoning (CBR) • Case-based reasoning is an artificial intelligence technique designed to mimic human problem solving. • Its goal is to mimic the way humans solve problems. • When faced with a new problem, humans search their memories for past problems resembling the current problem and adapt the prior solution to “fit” the current problem. Ch 25

• CBR is a method of analogical reasoning that utilizes old cases or experiences in an effort to solve problems, critique solutions, explain anomalous situations, or interpret situations Ch 26

Example of case based reasoning? • Oops the car stopped. w What could have gone wrong? • Aah. . Last time it happened, there was no petrol. w Is there petrol? § Yes. w Oh but wait I remember the tyre was punctured (ban bocor) • This is the normal thought process of a human when faced with a problem which is similar to a problem he/she had faced before. Ch 27

How do we solve problems? • By knowing the steps to apply w from symptoms/gejala to a plausible diagnosis • But not always applying causal knowledge w sebab - akibat • How does an expert solve problems? w uses same “book learning” as a novice w but quickly selects the right knowledge to apply • Heuristic knowledge (“rules of thumb”) w “I don’t know why this works but it does and so I’ll use it again!” w difficult to elicit Ch 28

CBR Cycles Ch 29

CBR System Components • Case-base w database of previous cases (experience) • Retrieval of relevant cases w index for cases in library w matching most similar case(s) w retrieving the solution(s) from these case(s) • Adaptation of solution w alter the retrieved solution(s) to reflect differences between new case and retrieved case(s) Ch 30

Example -- Technical Diagnosis of Car Faults Ch 31

CBR Knowledge Containers • Cases w lesson to be learned w context in which lesson applies • Description Language w features and values of problem/solution • Retrieval Knowledge w features used to index cases w relative importance of features used for similarity • Adaptation Knowledge w circumstances when adaptation is needed w alteration to apply Ch 32

Case acquisition/authoring • cases are acquired from real experiences • cases are created from categories of real experiences (prototypes) • cases are authored by an expert • cases are learned by data analysis • cases are searched in patterns • cases are converted (extracted) from text • cases are learned from text Ch 33

Similarity • The key to CBR success is expertise to determine what makes a case similar to another. • For example, if you have a common cold and your spouse has the flu, you will be able to recognize these two conditions are similar. • But only a physician can determine whether two infirmities are similar so that the same treatment can be applied. • It is expert knowledge that tells when a case is similar to another in the context of a CBR system. • Similarity function is a knowledge representation formalism to measure similarity between two cases Ch 34

Retrieval • Similarity functions measure similarity • All cases (or a selected portion) are compared to the target (problem) case • Cases are retrieved when their similarity is above a pre-defined threshold • This threshold determines the point from which cases are considered similar Ch 35

Adaptation • All features that describe a case and are not used for retrieval can potentially be adapted Ch 36

Adaptation methods • substitution w w w reinstantiation: replacement based on a role parameter adjustment (proportional) local search (taxonomy) query memory case-based substitution: alternatives in cases • transformation: transform by changing features either by substitution or deletion w common-sense transformation w model-guided repair Ch 37

Learning • learning by incorporating new cases to the case base • learning by adding cases that are adaptations from retrieved cases Ch 38

Advantage of CBR over Rule Base • CBR is selected when the relationship between the case attributes and the solution or outcome is not understood well enough to represent in rules. • CBR systems are advantageous when the ratio of cases that are “exceptions to the rule” is high, as rule-based systems become impractical in such applications. • In such situation, w CBR is convenient to incorporate the solution of a newly entered case. w CBR provides users with steps to combine and derive a solution from the collection of retrieved solutions. Ch 39

Other technologies • Constraint-based reasoning is an artificial intelligence technique that uses essentially “what cannot be done” to guide the process of finding a solution (Tsang 1994). • This technique is useful in naturally constrained tasks such as planning and scheduling. • For example, to schedule a meeting all the individuals that need to attend must be available at the same time, otherwise the “availability constraint” will be violated. Ch 40

Other technologies. . . • Model-based reasoning refers to an inference method used in expert systems based on a model of the physical world. • With this approach, the main focus of application development is developing the model. • Then at run time, an "engine" combines this model knowledge with observed data to derive conclusions such as a diagnosis or a prediction. • The system can help diagnose faults not previously experienced Ch 41

Example - cause/effect model of problems for vehicles: Ch 42

Other technologies. . . • Diagrammatic Reasoning w understanding of concepts and ideas through the use of diagrams and imagery, versus linguistic or algebraic representations w instrumental in developing systems such as Gelernter’s Geometry Machine Ch 43

Summary of Technologies Ch 44

Developing Knowledge Application Systems • Typical case-based knowledge application system will consist of the following processes: w w w Search the case library for similar cases. Select and retrieve the most similar case(s). Adapt the solution for the most similar case. Apply the generated solution and obtain feedback. Add the newly solved problem to the case library. Ch 45

Developing Knowledge Application Systems • The CASE-Method: w System development process § to develop a knowledge application system that will store new cases and retrieve relevant cases. w Case library development process § to develop and maintain a large-scale case library that will adequately support the domain in question. w System operation process § to define the installation, deployment, and user support of the knowledge application system. Ch 46

Developing Knowledge Application Systems • The CASE-Method Cont’d: w Database mining process § uses rule inference techniques and statistical analysis to analyze the case library. w Management process § describes how the project task force will be formed and what organizational support will be provided w Knowledge transfer process § describes the incentive systems to encourage user acceptance and support. Ch 47

Developing Knowledge Application Systems • Sub-processes of developing the case library: w Case Collection w Attribute-Value Extraction and Hierarchy Formation w Feedback • CASE Method in CBR development: w significant reduction in system development workload and costs • Knowledge application systems: w apply a solution to a similar problem w serve as a framework for creative reasoning. Ch 48

Developing Knowledge Application Systems • Knowledge application systems enabled the implementation of decision support systems w to support design tasks in diverse domains such as architecture, engineering, and lesson planning. w case-based design aids (CBDA’s) help human designers by making available a broad range of commentated designs. w Case libraries accumulate organizational experiences, considered corporate memory. Ch 49

Case Study: SOS Advisor • The SBIR/STTR Online System (SOS) Advisor w Web-based expert system w identify potential applicants to the Small Business Innovation Research (SBIR) and Small Business Technology Transfer Research (STTR) programs w optimize the time required to examine the potential eligibility of companies seeking SBIR/STTR funding. Ch 50

Case Study: SOS Advisor Ch 51

Case Study: SOS Advisor Ch 52

Case study: National Semiconductor • Knowledge application system based on the use of casebased reasoning (CBR) technology for product quality assurance (PQA). • Total Recall, can be viewed as consisting of four components and the Web client: w Application Server: Main server for the Total Recall application. Performs data manipulation and user presentation. w Total Recall Database: Maintains all the information related to the testing results of the PQA process. w Case Library: A separate database containing CBR representation of cases. w CBR Server: The final case library and CBR engine. Ch 53

Total Recall System Architecture Ch 54

Total Recall CBR Database Ch 55

Limitations of knowledge application systems • Typically developed to serve a task-specific domain problem, and not integrated with the organization’s enterprise systems. • Security: cases may include sensitive information. • Scalability: must represent a large enough number of cases • Speed: as the size of the case library grows to a more comprehensive representation of real environments, computing and searching costs will also increase. • May not be able to solve all the problems that come across, in particular, increasingly complex environments Ch 56

Conclusions In this Chapter we: • Discussed what knowledge application systems and design considerations, including the Case-Method Cycle • Described the types of knowledge application systems: w expert systems w help desk systems w fault diagnosis systems • Presented case studies describing details of implementation of knowledge application systems: w SOS Advisor w Total Recall w OFD for Shuttle Processing Ch 57