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Transcript
CS 420 Expert Systems – 2015/2016 Handout: Lab 1 Lab Contents: - Introduction To Expert Systems - Expert Systems vs. Artificial Intelligence - Expert Systems vs. Decision Support Systems - ES Structure / Architecture - ES Features - ES Lifecycle - Advantages and disadvantages of ES Introduction to Expert Systems What’s an Expert System An interactive computer based decision tool that uses facts A computer system that emulates the decision-making ability of a human expert in a restricted domain [Giarratano & Riley 1998] Edward Feigenbaum – “An intelligent computer program that uses knowledge and inference procedures to solve problems that are difficult enough to require significant human expertise for their solutions.” [Giarratano & Riley 1998] Sometimes, we also refer to knowledge-based system WHY? to solve difficult decision making problems, based on knowledge acquired from an expert in a certain field Expert Systems vs. Artificial Intelligence Expert systems: a computer application that employs a set of rules based on human knowledge to solve problems that require human expertise – Imitates reasoning of experts on “information fit” – A non-expert simulates a dialog with an expert to solve complex problems Artificial Intelligence: practical mechanisms that enable computers to simulate the human reasoning process – Interface of compute science/cognitive psychology – “the study of how to make computers do things which humans do better” – the Turing Test? Expert Systems vs. Decision Support Systems Decision support system ( DSS ): It is an information system that Provides its user with decision oriented information whenever decision making situation arises Eng. Nareeman Sabry | Eng. Amal Ibrahim Page 1 Expert System ( ES ): It is information system collect the knowledge from an expert in specific field to an computer system Decision support systems (DSS) DSS: an IS designed to help managers select one of many alternative solutions Components of a DSS o Data management module: a database or data warehouse that holds and maintains data for the DSS; data may come from a number of sources including such systems as SCM (supply chain management) and CRM o o o (customer relationship management) Model management module: contains a model or models to be used by the DSS; the model may be fixed (static), dynamic (able to change due to changes in the data), or it may be a collection of possible models from which the DSS or the user may select Dialog module: this is the interface between the user and the DSS; this is what the user would interact with to enter data, query the system, produce reports, etc. Sensitivity analysis module: this is used to determine what effect particular parameters have on the result; for example, you may be doing an analysis for a municipality where the amount of tax revenue generated is given great weight toward the outcome Examples of DSS given in text: o Production and retailing: used to project purchasing trends and help decide how much product to stock and where to purchase it o Tax planning: used to make financial decisions to help reduce tax burden o Web site planning and adjustment: used to analyze customer behavior and suggest changes to design o o o Yield management: used to maximize overall revenue, often by using price discrimination Financial services: used to make decisions such as loan approvals Benefit analysis: used to help determine which package of benefits is best suited for someone's needs and budget Eng. Nareeman Sabry | Eng. Amal Ibrahim Page 2 Expert systems (ES) Expert systems emulate the knowledge of an expert in a narrow field/domain Ai: artificial intelligence; the name given to a broad field where computer engineers and scientists try to mimic patterns of thinking and learning using computers Knowledge base: a collection of facts and the relationships between them; much of the content may be a collection of if/then rules Inference engine: software that combines data input by the user with a knowledge base to try to suggest a solution Neural networks: hardware or software designed to mimic the way a brain works Turing test: a test proposed by alan turing to determine if computers can think. To pass the turing test, a computer must have a dialog with humans and have the humans not be able to tell if they are talking to a computer or another person. To make the test a little more achievable, the dialog is almost always carried out as a text connection, like instant messaging or chat. Intelligent agent: software designed to wait to perform particular operations when triggered by a specific event, such as automatically reordering an item when the stock level of that item falls below a certain value Examples of es given in text: o Medical diagnosis: used to recognize patterns of diseases based on test results o Medical management: used to suggest courses of action and help avoid bad interactions with medicines, procedures, etc. o Credit evaluation o Detection of insider securities trading o Detection of common metals o Irrigation and pest management o Diagnosis and prediction of mechanical failure GDDS: Group Decision Support System Eng. Nareeman Sabry | Eng. Amal Ibrahim Page 3 ES Structure / Architecture Components of an Expert System ES Features Is limited to specific domain Is capable of explaining its own line of reasoning in a comprehensive way Has knowledge that is clearly separated from its program input data Can often reason with uncertain or incomplete information. Will delivers output as advice instead of just tables of figures. It must exhibit high performance in terms of speed and reliability in order to be a useful tool. Is designed to grow incrementally. Eng. Nareeman Sabry | Eng. Amal Ibrahim Page 4 ES Lifecycle Problem Identification Phase: Identifying the problem and opportunity where the organization can obtain benefits from an ES, and establishing the ES general goals Feasibility Study Phase: Assessing the feasibility of the ES development in terms of its technical, economical, and operational feasibility. Project Planning Phase: Planning for the ES project, including development team members, working environment, project schedule, and budget. Knowledge Acquisition Phase: Extracting domain knowledge from domain experts and determining the system’s requirements. Knowledge Representation Phase: Representing key concepts from the domain, and interrelationships between these concepts, using formal representation methods. Knowledge Implementation Phase: Coding the formalized knowledge into a working prototype. Verification and Validation: Verifying and validating a working prototype against the system’s requirement, and revising it as necessary according to domain experts’ feedback. Installation/Transition/Training: Installing the final prototype in an operating environment, training the users, and developing documentation/user’s manual. Eng. Nareeman Sabry | Eng. Amal Ibrahim Page 5 Operation/Evaluation/Maintenance Running the system in an operating environment, evaluating its performance and benefits, and maintaining the system. Advantages and disadvantages of ES ES Advantages: Economical – Lower cost per user Availability – Accessible anytime, almost anywhere Response time – Often faster than human experts Reliability – Can be greater than that of human experts – No distraction, fatigue, emotional involvement, … Explanation – Reasoning steps that lead to a particular conclusion Intellectual property – Can’t walk out of the door Permanence - expert systems do not forget, but human experts may Reproducibility - many copies of an expert system can be made, but training new human experts is time-consuming and expensive If there is a maze of rules (e.g. Tax and auditing), then the expert system can "unravel" the maze Efficiency - can increase throughput and decrease personnel costs – Although expert systems are expensive to build and maintain, they are inexpensive to operate – Development and maintenance costs can be spread over many users – The overall cost can be quite reasonable when compared to expensive and scarce human experts – Cost savings: wages - (elimination of a room full of clerks) other costs - (minimize loan loss) Consistency - with expert systems similar transactions handled in the same way. The system will make comparable recommendations for like situations. Humans are influenced by – Recency effects (most recent information having a disproportionate impact on judgment) – Primacy effects (early information dominates the judgment). Documentation - an expert system can provide permanent documentation of the decision process Completeness - an expert system can review all the transactions, a human expert can only review a sample Eng. Nareeman Sabry | Eng. Amal Ibrahim Page 6 Timeliness - fraud and/or errors can be prevented. Information is available sooner for decision making Breadth - the knowledge of multiple human experts can be combined to give a system more breadth that a single person is likely to achieve Reduce risk of doing business – Consistency of decision making – Documentation – Achieve expertise Entry barriers - expert systems can help a firm create entry barriers for potential competitors Differentiation - in some cases, an expert system can differentiate a product or can be related to the focus of the firm (xcon) Computer programs are best in those situations where there is a structure that is noted as previously existing or can be elicited ES Disadvantages: Limited knowledge – “shallow” knowledge » No “deep” understanding of the concepts and their relationships – No “common-sense” knowledge – No knowledge from possibly relevant related domains – “closed world” » The ES knows only what it has been explicitly “told” » It doesn’t know what it doesn’t know Mechanical reasoning – May not have or select the most appropriate method for a particular problem – Some “easy” problems are computationally very expensive Lack of trust – Users may not want to leave critical decisions to machines How do you code common sense? Expertise is difficult to extract and encode. Expert “errors” transferred to model Another is that human experts adapt naturally but an ES must be recoded. Human experts better recognize when a problem is outside the knowledge domain, but an ES may just keep working ES’s can’t eliminate the cognitive limitations of the user An ES is functional only in a narrow domain Eng. Nareeman Sabry | Eng. Amal Ibrahim Page 7