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Knowledge Acquisition, Representation, and Reasoning

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Knowledge Acquisition, Representation, and Reasoning TURBMW18_0131986600.QXD 11/9/06 8:58 PM Page W-174 CHAPTER Knowledge 18 Acquisition, Representation, and Reasoning Learning Objectives ◆ Understand the nature of knowledge ◆ Understand the knowledge-engineering process ◆ Learn different approaches to knowledge acquisition ◆ Explain the pros and cons of different knowledge acquisition approaches ◆ Illustrate methods for knowledge verification and validation ◆ Understand inference strategies in rule-based intelligent systems ◆ Explain uncertainties and uncertainty processing in expert systems (ES) n this book, we have described the concepts and structure of knowledge-based ES. IFor such systems to be useful, it is critical to have powerful knowledge in the knowl- edge base and a good inference engine that can derive valid conclusions from the knowledge base. In this chapter, we describe the process for acquiring knowledge from human experts, validating and representing the knowledge, creating and operating inference mechanisms, and dealing with uncertainty.The chapter is divided into the fol- lowing sections: W18.1 Opening Vignette: Development of a Real-Time Knowledge-Based System at Eli Lilly W18.2 Concepts of Knowledge Engineering W18.3 The Scope and Types of Knowledge W18.4 Methods of Acquiring Knowledge from Experts W18.5 Acquiring Knowledge from Multiple Experts W18.6 Automated Knowledge Acquisition from Data and Documents W18.7 Knowledge Verification and Validation W18.8 Representation of Knowledge W18.9 Reasoning in Intelligent Systems W-174 TURBMW18_0131986600.QXD 11/9/06 8:58 PM Page W-175 Opening Vignette W-175 ◆ W18.10 Explanation and Metaknowledge W18.11 Inferencing with Uncertainty W18.12 Expert Systems Development W18.13 Knowledge Acquisition and the Internet W18.1 OPENING VIGNETTE: DEVELOPMENT OF A REAL-TIME KNOWLEDGE-BASED SYSTEM AT ELI LILLY PROBLEM Eli Lilly (lilly.com) is a large U.S.-based, global pharmaceutical manufacturing company that has 42,600 employees worldwide and markets it products in about 160 countries. Production of medicines requires a special process called fermentation. A typical fermentation process involves a series of stirred vessels in which a culture of microor- ganisms is grown and transferred to progressively larger tanks. In order to have quality products, the fermentation process must be monitored carefully and controlled consis- tently. Unfortunately, some key quality parameters are difficult to control using tradi- tional statistical process control. For example, it is difficult to quantify the state of a fer- mentation seed, but without this information, predictions of the behavior of a production cycle may be imprecise. Furthermore, Lilly operates many plants that use the same process but are operated by different employees. The quality of the product may vary substantially when different operators use their experience to control the process. The turnover of experienced operators also causes a problem for some plants. As a result, both productivity and quality problems emerge. SOLUTION Eli Lilly is using ES to overcome its problems. Its motivation for adopting a new sys- tem was to make the expertise of key technical employees available to the process operators 24 hours a day, anywhere. It was felt that an ES would allow relevant por- tions of the knowledge base to be cloned and made available to all of the company’s plants. Eli Lilly installed Gensym’s G2 (gensym.com/?p=g2_platform) at its Speke site to construct an intelligent quality-alert system that would provide consistent real-time advice to the operators. DEVELOPMENT PROCESS The Eli Lilly system focuses on the seed stage of the fermentation process. Four knowl- edge engineers were involved in the system development: three for knowledge elicitation and one for coding knowledge into G2 rules and procedures. The knowledge engineers had some domain knowledge, and they were asked to record the knowledge of the experts but not to preempt or optimize it with personal attitudes toward the domain— and in particular not to rationalize the knowledge into a framework with which the inter- viewer was familiar.They were also asked not to intimidate the experts by virtue of their domain expertise. The entire development process took six months to complete, and it included the following major steps: 1. Knowledge elicitation. The knowledge engineers interviewed 10 experienced experts to acquire their knowledge. A knowledge acquisition tool, KAT, was used to facilitate the knowledge acquisition. TURBMW18_0131986600.QXD 11/9/06 8:58 PM Page W-176 ◆ W-176 CHAPTER 18 Knowledge Acquisition, Representation, and Reasoning 2. Knowledge fusion. The interviews resulted in 10 different knowledge sets, repre- sented as graphs. A joint session integrated all sets. 3. Coding of the knowledge base. The resulting knowledge graph was converted into rules acceptable to G2. Initially, a total of 60 rules were produced. 4. Testing and evaluation. The system was tested using values that simulated anom- alies for the variables and output verification. Results Configuring the scalable automation was more intuitive and faster even when taking the learning curve into consideration. The program executes exactly as drawn making visual proof of calculations quick and easy.Two routines can be activated during simu- lation or start-up to find which gives the best response time. Such comparisons are very difficult with older automation. OLE for process control (OPC) standard augments communications between flex- ible production areas. It provides data exchange standards and constructs that allows information sharing among programs, much like Microsoft Office. OPC also provides more robust, secure, and object-oriented code and greater error checking. Sources: Adapted from A. Ranjan, J. Glassey, G. Montague, and P.Mohan, “From Process Experts to a Real-Time Knowledge-Based System,” Expert Systems, Vol. 19, No. 2, 2002, pp. 69–79; and A. Bullock, Life Sciences/Eli Lilly—Success Story, February 1988, easydeltav.com/successstories/ lilly.asp (accessed April 2006). Questions for the Opening Vignette 1. Why did Eli Lilly need to develop an intelligent system for providing advice to process operators? 2. Why were the coding knowledge engineers asked not to use their own knowledge framework but to record only knowledge from the experts? 3. Why do we need knowledge engineers to develop ES? Can we ask experts to develop the system by themselves? 4. What do you think of Eli Lilly’s approach, which developed 10 separate knowl- edge bases and then put them together through knowledge fusion? What are the pros and cons of this approach? 5. For system testing and evaluation, Eli Lilly used simulated data rather than real data. Why is this acceptable for real-time ES? Can you think of a better approach for system testing and evaluation? 6. What are the benefits of using knowledge acquisition tools? WHAT WE CAN LEARN FROM THIS VIGNETTE The opening vignette illustrates the process of acquiring knowledge and deploying it in a real-time monitoring system. It also illustrates the use of computer-aided knowledge acquisition tools to make the development job easier and to integrate knowledge from multiple experts. W18.2 CONCEPTS OF KNOWLEDGE ENGINEERING The process of acquiring knowledge from experts and building a knowledge base is called knowledge engineering. The activity of knowledge engineering is defined in the pioneering work of Feigenbaum and McCorduck (1983) as the art of bringing the TURBMW18_0131986600.QXD 11/9/06 8:58 PM Page W-177 Concepts of Knowledge Engineering W-177 ◆ principles and tools of artificial intelligence research to bear on difficult applications problems requiring the knowledge of experts for their solutions. Knowledge engineer- ing involves the cooperation of human experts in the domain working with the knowl- edge engineer to codify and make explicit the rules (or other procedures) that a human expert uses to solve real problems. The field is related to software engineering. Knowledge engineering can be viewed from two perspectives: narrow and broad. According to the narrow perspective, knowledge engineering deals with knowledge acquisition, representation, validation, inferencing, explanation, and maintenance. Alternatively; according to the broad perspective, the term describes the entire process of developing and maintaining intelligent systems. In this book, we use the narrow definition. The knowledge possessed by human experts is often unstructured and not explic- itly expressed. A major goal of knowledge engineering is to help experts articulate what they know and document the knowledge in a reusable form. For details, see en.wikipedia.org/wiki/Knowledge_engineering. THE KNOWLEDGE-ENGINEERING PROCESS The knowledge-engineering process includes five major activities: 1. Knowledge acquisition. Knowledge acquisition involves the acquisition of knowl- edge from human experts, books, documents, sensors, or computer files. The knowledge may be specific to the problem domain or to the problem-solving pro- cedures, it may be general knowledge (e.g., knowledge about business), or it may be metaknowledge (knowledge about knowledge). (By metaknowledge, we mean information about how experts use their knowledge to solve problems and about problem-solving procedures in general.) Byrd (1995) formally verified that knowl- edge acquisition is the bottleneck in ES development today; thus, much theoretical and applied research is still being conducted in this area. An analysis of more than 90 ES applications and their
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