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Exploring Artificial Intelligence and Evolutionary Computation with Dr. T: A Unique CS Journey

Discover the captivating world of Artificial Intelligence (AI) and Evolutionary Computation with Dr. Daniel Tauritz. Through courses like Heuristic Search and Game Theory, delve into solving complex problems and creating intelligent agents. Learn the basics of Discrete Mathematics, Neural Networks, and Fuzzy Logic as foundational knowledge for AI. Engage in programming and algorithmic challenges to enhance your skills in this fascinating field. Join the ACM SIG Security Intro Meeting for more insights and free pizza!

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Exploring Artificial Intelligence and Evolutionary Computation with Dr. T: A Unique CS Journey

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  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 agents • Evolutionary Computation (CS401) – solving REALLY hard problems (FS2002 samples, FS2003 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) = x2y + 5xy -3xy2 -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. 10th Room 216, CS Building Free Pizza

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