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Intelligent Systems MGMT 661 - Summer 2012 Night #7, Part 1 Outline for Tonight 1. Intelligent Systems 2. Decision Support Systems 3. Enterprise Knowledge Management Systems Virtual Reality Neural Networks Expert Systems DSS and DSS models GIS ESS (e.g. Balanced Scorecard) Final Homework tutorial on Pivot Charts Information Processing data = recording of events information = organized data knowledge = information + (patterns or context) wisdom = knowledge + experience Types of Information Structured Information explicitly laid out as tables, graphs, reports Unstructured Information emails presentations memos messages 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 Virtual Reality Systems Current primary uses: engineering design medical imaging Future Uses: immersive data mining 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 Neural Networks used to model complex relationships between inputs and outputs or to find patterns in data good at classification problems learns by example 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 facts (known and inferred) heuristics (rule of thumb) Inference Base Engine method for reasoning about the facts and heuristics to form a conclusion 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) 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 Group Exercise Write four expert system rules to determine which courses an MBA student should take next semester. if (fact) then (new fact)