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Artificial Intelligence Methods (G52AIM)

Artificial Intelligence Methods (G52AIM). Dr Rong Qu rxq@cs.nott.ac.uk Module Introduction. G52AIM web pages http://www.cs.nott.ac.uk/~rxq/g52aim.htm http://www.cs.nott.ac.uk/~gxk/aim/2009/ All lecture slides and additional notes Coursework (available soon) Assessment Textbooks

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Artificial Intelligence Methods (G52AIM)

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  1. Artificial Intelligence Methods (G52AIM) Dr Rong Qu rxq@cs.nott.ac.uk Module Introduction

  2. G52AIM web pages http://www.cs.nott.ac.uk/~rxq/g52aim.htm http://www.cs.nott.ac.uk/~gxk/aim/2009/ All lecture slides and additional notes Coursework (available soon) Assessment Textbooks Course schedule Other resources Previous exam paper/example questions Course Information

  3. Lectures Handouts/notes Willingness to answer questions, i.e. mailing list More feedback on coursework Teaching method Lectures: approx. 20 hours Jointly taught with Professor Kendall Private study: approx. 20 hours Module mailing list: g52aim@cs.nott.ac.uk Course Information

  4. Lecture time - location Friday 3-5pm JC-BSSOUTH-A26+ Lecture schedule might be slightly adjusted (enough notice will be given) Course Information

  5. Coursework Coursework I, 5% 2/3 pages “report/essay” about basics of optimisation Deadline: 23rd March 2010, 3pm Coursework II, 20% Implementation required Some optimization algorithms on some domain Prefer Java/C++/C#, but no GUI needed! Deadline: 11th May 2010, 3pm Assessment

  6. Exam 75% Covering all materials in the lectures 6 questions from which you can choose 4 Some past papers and suggested answers available at the module’s web page Assessment

  7. Lecture 1 : Introduction & Local search (today) Lecture 2 : Simulated annealing & Constructive heuristics Lecture 3 : Tabu search & Algorithm design Lecture 4 : Genetic programming (gxk) Lecture 5 : Variable neighborhood search & coursework Schedule

  8. Lecture 6 : Genetic algorithms Lecture 7 : Case study (rxq) Coursework 1 due Lecture 8 : Ant algorithms Lecture 9 : Hyper-heuristics Easter holiday Lecture 10 : Case study Coursework 2 due Schedule

  9. Course Context G51IAI Introduction to AI G52AIM Artificial Intelligence Methods G52AIP Artificial Intelligence Programming G53IDS Individual Project

  10. Course Context • Related modules • G53KRR Knowledge representation and reasoning • G53DIA Designing Intelligent Agent • G53DSS Decision support methodologies • G51IRB Introduction to Robotics • G52ARB Advanced Robotics

  11. Textbooks • Search Methodologies – Introductory tutorials in optimization and decision support techniques*, Burke and Kendall 2005 Chap 1 : Introduction Chap 4 : Genetic Algorithms Chap 5 : Genetic Programming Chap 6 : Tabu Search Chap 7 : Simulated Annealing Chap 8 : Variable Neighborhood Search Chap 17 : Hyper-heuristics *available in the library

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