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Intelligent Systems

Intelligent Systems. MGMT 661 - Summer 2012 Night #7, Part 1. Outline for Tonight. Intelligent Systems Enterprise Knowledge Management Systems Virtual Reality Neural Networks Expert Systems Decision Support Systems DSS and DSS models GIS ESS (e.g. Balanced Scorecard) Final Homework

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Intelligent Systems

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  1. Intelligent Systems MGMT 661 - Summer 2012 Night #7, Part 1

  2. Outline for Tonight • Intelligent Systems • Enterprise Knowledge Management Systems • Virtual Reality • Neural Networks • Expert Systems • Decision Support Systems • DSS and DSS models • GIS • ESS (e.g. Balanced Scorecard) • Final Homework • tutorial on Pivot Charts

  3. Information Processing data = recording of events information = organized data knowledge = information + (patterns or context) wisdom = knowledge + experience

  4. Types of Information • Structured Information • explicitly laid out as tables, graphs, reports • Unstructured Information • emails • presentations • memos • messages

  5. Enterprise-wide Knowledge Management Systems • contents: • structured and unstructured information • FAQs, work blogs, white papers, special reports • directory of in-house experts • wiki pages • features: • search engines • collaboration tools (e.g. wikis and bookmarking) • automatic knowledge collection

  6. Virtual Reality Systems • Current primary uses: • engineering design • medical imaging • Future Uses: • immersive data mining

  7. Virtual Reality Today • Google Goggles • cell phone app • take a picture of a landmark and it tells you where you are • Google Glasses • hands free smart phone display • responds to voice commands

  8. Neural Networks used to model complex relationships between inputs and outputs or to find patterns in data good at classification problems learns by example

  9. Expert Systems • Elements of problem solving • facts about the problem • theories about the problem • strategies for solving these types of problems • rules for what to do • Components of an Expert System • Knowledge Base • facts (known and inferred) • heuristics (rule of thumb) • Inference Engine • method for reasoning about the facts and heuristics to form a conclusion

  10. Expert Systems Example Heuristics if (temp is cold) then (need to wear a coat) if (sky is raining) then (need an umbrella) if (need to wear a coat) and (going outside) then (put on coat) and (retract (need to wear a coat)) if (checked temp) and (checked sky) and (going outside) then (go outside)

  11. Expert Systems • well suited to mimicking trouble shooters • interpretation, diagnosis, design, repair, control • able to explain how and why a decision was reached • reasons to develop an ES: • capturing scarce expertise • consistent decisions • faster response • Problems: • expertise is hard to extract from humans

  12. Group Exercise Write four expert system rules to determine which courses an MBA student should take next semester. if (fact) then (new fact)

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