1. CS 1 – Introduction to Computer Science Introduction to the wonderful world of Dr. T Dr. Daniel Tauritz

2. Teaching Artificial Intelligence (AI), in particular: Introduction to Artificial Intelligence (CS347) – heuristic search, game theory, games (WS2002: Abalone, FS2003: Stratego), intelligent agentsWS2002 FS2003 Evolutionary Computation (CS401) – solving REALLY hard problems (FS2002 samples, FS2003 samples)FS2002 samplesFS2003 samples

3. Research Natural Computation Lab Problem domain: Computer Security Approaches: Discrete Mathematics Artificial Intelligence (Game Theory) Evolutionary Computation Neural Networks Fuzzy Logic

4. Base courses for AI (1) Mathematics Math 8/21/22 Calculus & Geometry I,II,III CS 158 Discrete Mathematics for CS Math 203/208 Matrix/Linear Algebra CS 228 Intro to Numerical Methods Optional mathematics CS 328 & 329 Object-Oriented Numerical Modeling I & II

5. Base courses for AI (2) Programming & Algorithms CS 53/54 Introduction to Programming CS 153 Data Structures I CS 253 Data Structures II Advanced theory CS 330 Automata Theory CS 355 Analysis of Algorithms

6. AI courses CS 347 Artificial Intelligence CS 378 Neural Networks & Applications CS 401 Evolutionary Computation CS 404 Data Mining & Knowledge Discovery CS 447 Advanced Topics in AI EE 338 Fuzzy Logic Control EMAN 478 Advanced Neural Networks

7. Evolutionary Computation Inspired by Darwin’s theory of natural selection and survival of the fittest and Mendel’s laws of heredity (genetics) A population of individuals in an environment becomes a set of trial solutions for a problem Fitness indicates quality of solution Genes represented by a data type

8. Example problem Given the function f(x,y) = x 2 y + 5xy -3xy 2 -5 <= x <= 5 and -5 <= y <= 5 for what integer values of x and y is f(x,y) minimal?

9. Evolutionary Algorithm (1) Trial solution: (x,y) Genes represented by integers Fitness function: -f(x,y) Population size: 4 Number of offspring: 2 Competition: remove the 2 individuals with the lowest fitness value

10. Evolutionary Algorithm (2) Selection: in first step select with 50% chance fittest individual, in second step with 50% second fittest individual, etc. If no individuals selected, select fittest. Genetic operators: 1-point crossover with 50% chance single unit increment or decrement mutation with 50% chance

11. UMR ACM SIG Security Come to the Intro Meeting 7:00pm, Wednesday, Sep. 10 th Room 216, CS Building