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CS1001

CS1001. Lecture 25. Overview. Homework 4 Artificial Intelligence Database Systems. Reading. Brookshear, 11 Brookshear, 10 Brookshear, 9. Homework 4. Check Courseworks Problems based on Handout (Smullyan, Natural Deduction) Question from Ch. 10 Question from Ch. 11.

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CS1001

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  1. CS1001 Lecture 25

  2. Overview • Homework 4 • Artificial Intelligence • Database Systems

  3. Reading • Brookshear, 11 • Brookshear, 10 • Brookshear, 9

  4. Homework 4 • Check Courseworks • Problems based on • Handout (Smullyan, Natural Deduction) • Question from Ch. 10 • Question from Ch. 11

  5. Artificial Intelligence • Reasoning (Production Systems) • Goal is to derive a solution given facts and rules • Searching • You are given facts and rules and search all possible combinations to find some desired solution (usually minimum/max) • Heuristics • Operate based on guidelines you know to be true about a problem

  6. Paradigms in AI • “Top Down” • Describe intelligent behavior as rule sets • Define behavior at the highest level and refine as need be • Examples: most production systems, expert systems • “Bottom Up” • Define very basic behavior of “agents” • Describe simple rules of how these agents interact • Simulate interactions on a grand scale

  7. A Production System

  8. Genetic Algorithms • A “Bottom Up” approach • Create simple logic and rules for interactions • Essentially, a GA is a program that writes and alters other programs. It then simulates those programs and evaluates their performance • http://math.hws.edu/xJava/GA/ • http://users.ox.ac.uk/~quee0818/ts/ts.html

  9. Expert Systems • Expert systems are a strict set of rules • These rules allow for drawing conclusions from an initial set of facts • Propositional logic could be used as the mathematical system behind an expert system • Example: If a patient has a Hematocrit reading of 8, he/she has severe Anemia

  10. Neural Networks • Neural networks are based on the biological function of brain neurons (sort of) • This is a learning model, whereby the model can be trained and updated over time

  11. Neural Networks

  12. Semantic Networks

  13. Blackboard Systems • Blackboard systems contain all sorts of subsystems that we’ve seen so far • They are a metaphorical area for different AI systems to exchange predictions and vote on a consensus as a whole

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