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Artificial Intelligence and Its Relevance to Industrial Engineers

Artificial Intelligence and Its Relevance to Industrial Engineers. Assoc.Prof.Dr. Hasan H. ÖNDER. In this presentation we will review w hy industrial engineers should be interested in expert systems (AI- knowledge-based ) systems. Brief history of AI. AI research originated in the 1950’s,

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Artificial Intelligence and Its Relevance to Industrial Engineers

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  1. Artificial Intelligence and Its Relevance to Industrial Engineers Assoc.Prof.Dr. Hasan H. ÖNDER

  2. In this presentation we will review why industrial engineers should be interested in expert systems (AI-knowledge-based )systems.

  3. Brief history of AI • AI research originated in the 1950’s, • IEEE Computer Society in December, 1984.

  4. AI research deals • using computers to emulate the reasoning, • problem-solving, • creativity, • planning behaviors of human beings so that they can solve problems that are too large or too complex to be solved with traditional techniques.

  5. Branches of AI • Fuzzy Sets, • Neuron Networks, • Genetic Algorithms, • Expert Systems (Knowledge-Based Systems).

  6. Expert Systems or Knowledge-Based Systems. • expert system is considered to be the area of AI with the most promise for commercial applications. • computer programs that solve difficult problems that are traditionally by human experts.

  7. Knowledge-Based Systems. • Knowledge-based systems are concerned with applying knowledge to solve that ordinarily require human intelligence and/or expertise. • Expert systems are the knowledge-based systems that consist of computer programs design to represent and apply factual knowledge of specific areas of expertise to solve problems.

  8. They emphasize domain-specific problem solving strategies and employ self-knowledge to reason about their own interference process and provide explanation or justifications for conclusions reached

  9. Expert systems typically consist of three components: • A knowledge base: consists of all facts, rules, relations ets. Used by the experts. • inference systems: contains the lines of reasoning followed by the expert to organize and control the steps taken to solve problems in a given domain. • A dialog system: user interface which communicates with the knowledge-base through the inference system.

  10. Knowledge acquition Knowledge base User interface Inference system Components of Expert systems

  11. Expert systems are needed to supplement, complement, or replace certain human expert functions because: • Human expertise is a scarce resource whose supply is never guaranteed. • Human get tired, forget, or simply becomes indolent. • Humans are inconsistent in their day to day decisions for the same set of data. • Human gets quit, have bad days, harbor bias, or insubordinate. • Human lie, die, and hide.

  12. The system can; • diagnose, • monitor, • analyze, • interpret, • consult, • plan, • design, • instruct, • clarify, • learn, • and conceptualize specific topics in industrial engineering.

  13. Industrial Engineer: • associated with the ‘human aspects’ of job functions • concerned about the natural, physical and emotional limitations of man • decision-making and management

  14. Success or failure of may undertaking is directly dependent on the quality of decisions. The need for expert systems arises because there are certain inherent human characteristics that tend to impair the optimality of decisions.

  15. . Computers have been used extensively for traditional decision support systems (DSS). Now expert systems are extending the frontiers of computer usage for decision-making. This is in direct response to a need created by the limitations of man in decision environments.

  16. Application areas of AI and computers: • Manufacturing: • Robotics/vision • Factory Planning and design • CAD • Human factors/ organizational implications • Decision-aids • Human-machine communications and intelligent interfaces • Production planning • Repair and fault –diagnosis • Process control

  17. Potential IE applications • Computer aided manufacturing: production management, robotics, quality control, flexible manufacturing systems • Banking, financing, economics, • Business administration, accounting, human resources management. • Law, legislation, regulation, enforcement, contract management, taxation systems. • Insurance. • Office automations, • Computer aided design: electronics, architecture, engineering, and construction, structural design etc.

  18. Potential IE applications • Technical diagnosis and maintanence. • Logistics. • Computer-aided education • Computer engineering: configuration management, reliability, safety, auditing, security, capacity and network management. • Software engineering: specification, design, verification and validation, project management, quality control, maintenance. • Humanities and social science. • Decision support systems: command control, communications, and intelligence, data fusion.

  19. Potential IE applications • Complex systems control, simulation, simulation and training. • Mathematics, statistics and data analysis, numerical analysis, risk assessment. • Signal processing and pattern recognition; vision, speech processing. • Agriculture and food industry, • Information retrieval, data base management. • Project management; planning, scheduling, monitoring, controlling, heuristics formulation.

  20. Potential IE applications • With the proliferation of personal computers and the increased movement towards micro-computer-based expert systems, industrial engineers should take the lead in developing systems that can provide assistance for real-time operational decisions.

  21. Potential IE applications • A question that may be come up at the point of time is, why should Industrial engineers be concerned with AI, which is a domain of computer scientists?

  22. Potential IE applications • In addition to the need to give more emphasis to emerging technologies, (preparing us for a better role in the more automated systems of the future), AI technologies offer significant opportunities to improve performance and also alternative approaches to deal with some of our current problems like integration of CAD/CAM or CIM systems.

  23. Time has come for industrial engineers • Industrial engineers by virtue of their decision-oriented responsibilities are in a unique position to utilize the emerging technology of expert systems. Industrial engineers are will known for their involvements in decision processes for job functions ranging from facility location planning, process control, and economic analysis to automation of manufacturing systems.

  24. Artificial intelligence is being hailed as a technology that will dictate how business and industry will operate in the future. Laying the groundwork now for the marriage of expert systems and IE job functions will help assure the realization of the full potentials of this young branch of computer science. Industrial engineers can, thus, take the lead in preparing work environments for the impending ‘invasion’ of intelligent computers and software.

  25. I.E Analysis/sysnthesis DOMAIN EXPERT Knowledge/experties KNOWLEDGE ENGINEER System development WORKİNG MEMORY INFERENCE ENGINE KNOWLEDGE BASE Recommendation Consultation CLIENT IE and expert systems interfaces

  26. Concluding remarks • An industrial engineer should consider the knowledge-based as one more tool in his/her bag of tools. • Computers have changed the industrial Engineer’s way of thinking and become a part of our systems approach to problem-solving. It is the IE who integrates skills of engineering with the tools of mathematics and computer science to formulate and build models for design, analysis, evaluation and prediction purposes. Future computers are predicted to introduce staggering changes in our abilities to use computers; and AI is part of this future making it important for some of the IE’s to get involved in AI.

  27. The developments in knowledge-based systems have reached such a stage that they are now ready to be taken out of laboratories and into the real world. However, even where they are suitable for applications, they are not yet enough to displace the human link completely and it is not expected that they will replace these experts. This is because the expert systems knowledge comes from the knowledge base and the heuristic rules developed from those solve all possible decision situations.

  28. Thus an expert system, once developed to the prototype stage and found satisfactory, needs to go through continuous learning process whenever new situations are encountered. This learning comes from adding new knowledge in the knowledge base and/or adding/modifying heuristic rules.

  29. Another imported point is that AI products often are not stand-alone type of products and have to be integrated with other systems in a large context. In AI breadth of technical understanding will be more important for successful research. Need for greater understanding substantiates the concept that IE’s should take more interest in AI concepts and applications.

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