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CSCE101 –Chapter 8 (continued). Tuesday, December 5, 2006. Information Systems. Office Information Systems Transaction Processing Systems Management Information Systems Decision Support Systems Executive Support Systems Expert Systems. Experts System Components.
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CSCE101 –Chapter 8 (continued) Tuesday, December 5, 2006
Information Systems • Office Information Systems • Transaction Processing Systems • Management Information Systems • Decision Support Systems • Executive Support Systems • Expert Systems
Experts System Components • End user, problem domain expert, knowledge engineer • Components of an expert system • Knowledge Base • Inference Engine • User Interface
Inference • Given that certain premises are true, one can deduce a conclusion that is also true. Example #1: All men are mortal Socrates is a man ------------------ Therefore Socrates is mortal. Example #2: Given the output from two queries against a personnel database: • How many women are in Department X: Result: 1 2. What is the average salary of the women in Department x? Result: $60,000 Conclusion: One now knows the exact salary of the only woman in Department X
Other Uses of AI • Natural language processing • Intelligent agents • Pattern recognition • Fuzzy logic • Virtual reality & simulation devices • Robotics
Natural Language Processing Brute force processing = Generating all possible answers + selection of best answer Ex. #1: Word substitution until a meaningful sentence occurs: English: “The spirit is willing, but the flesh is weak.” Russian: “The wine is agreeable, but the meat is spoiled.” Ex. #2: Computers that play chess Ex. #3: Decryptors
Intelligent Agents • Act autonomously on behalf of the user (ex.: bots, crawlers, spiders). • Data mining capabilities • Learning and adapting
Pattern Recognition • Recognition of some kind of pattern in multimedia data or text data. Ex. #1: Face recognition software Ex. #2: Data mining • AI winters • Pattern recognition software became an important research area after 9/11
Fuzzy Logic • Predicate logic vs. fuzzy logic • Degrees of participation in a set Ex. #1 – In which room are you standing? Ex. #2 – Programming elevators in order to optimize traffic flow
Virtual Reality and Simulation Devices • Computer-generated sensory data • Virtual reality programs create output that simulates some aspect of reality. Can be used for entertainment or training purposes. • Simulators are specifically designed to train a response into the user (ex.: surgeons, pilots)
Robots • Robots perform physical tasks that would normally be done by a human
Weak AI vs. Strong AI • Weak AI – Conventional AI • May include brute-force calculations • Finite reasoning capability • Strong AI – Computational Intelligence • Computer can “learn” • Chinese Room thought experiment • Edsgar Dijkstra – • “Debating as to whether a computer can actually think is about as relevant as debating whether a submarine is really swimming”
Weak AI vs. Strong AI (continued) • Strong AI – Computational Intelligence • Attempts at implementing Strong AI • Neural networks • Genetic algorithms • Cyborgs • Turing test • Captchas • Ethics in AI – AI can’t be value free because it is built by humans • AI run amok is standard fare for science fiction. • Many ideas for strong AI come from the discipline of epistemology.
Weak AI vs. Strong AI (continued) • A branch of philosophy known as ontology is also studied by AI researchers • General purpose AI applications vs. specific purpose AI applications
