AI Applications

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lecture series

  1. introductory lecture

  3. complexity
    lecture: complexity
    complexity models... emergence models... chaos models...

  5. legal move generation, searching & planning techniques

    • search
    • game playing
    • planning operators
    • other

    • problem based learning exercise: maze search (but do it with Clojure)
    • check Clojure resources for an operator search & implementation of a STRIPS-like solver
    • write a cost based search in NetLogo
    • an excel minimax solver which operates on a 3x3 tree (thanks to Ben Slee)
    • A Beginner's Guide to Big O Notation -- Big O notation is used to analyse the performance & complexity of algorithms. BigO (used in various areas of Computer Science) can be used as a measure for the performance of various types of search. If you have not met BigO before, here is a short guide.
    • A couple of links to texts on "game theory" (i) R.A.McCain (ii) T.S.Ferguson. These are both a few years old (then again so is game theory).

  7. rule application & expert systems

  9. constraint propagation

  11. Knowledge Representation
    we will not study knowledge representation as a stand-alone topic this year, we will introduce it as relevant in other topics. These links provide some useful information...

  13. multiagent systems (MAS)

  15. language processing

  17. machine learning




    useful links

    • pages from Tom Carter (California State University Stanislaus).
      Tom's pages have lots of useful information relevant to modelling, AI, linguistics, complexity, etc. - highly recommended: Tom's Pages
    • pages from Liron Cohen -- these have useful information about search & other aspects of A.I.: Liron's Pages
    • evolution

    • timelines for natural evolution...
      - timeline used in lectures evolution timeline#1
      - an animated timeline animated timeline
    • an "Essay" on artificial life
    • machine learning (these are also referenced in the machine learning section above)

    • Complex Adaptive Systems -- a collection of notes & links from a course at Indiana
    • Software Examples -- for Demonstrating Complex Adaptive Systems
    • for those who want to develop in Python...

    • Python samples -- for some of the examples in Russel and Norvig's A.I. Modern Approach
    • Other Python Links -- links to some AI Python libraries



    1. "A.I. A Modern Approach", Stuart Russell & Peter Norvig
      Prentice Hall, ISBN:0-13-103805-2
      essential reading (see reading lists below)
    2. "Artificial Intelligence", Elaine Rich & Kevin Knight
      McGraw-Hill, ISBN:0-07-052263-4
      recommended reading
    3. one of the recommended texts on Clojure
      a must if you want to build AI engines



    From: "A.I. A Modern Approach", Stuart Russell & Peter Norvig

    - chapter references assume the most recent version of this book;
    - some chapters are optional, others required but the whole book is relevant

    • Ch.1. (optional) general introduction to "what AI is about"
    • Ch.2. (optional) outlines the way we think about AI software
    • Ch.3. (required) an introduction to problem solving using search tecniques
    • Ch.4. (required) more advanced search techniques, using costs, etc
    • Ch.5. (required) using constraints and developing solutions based on constraints
    • Ch.6. (required) game playing: minimax, alpha-beta pruning, etc
    • Ch.7,8. (optional) an introduction to logic. Logic has a long history of use in AI and many authors express their ideas in logic (Russel and Norvig included). These chapters are optional because the AIN course is not based on logic but you will understand other chapters of the book better with an appreciation of logic and if you have already studied formal methods you should know most of the content of these chapters already
    • Ch.11. (required) planning, applying operators, means-end analysis, etc
    • Ch.12. (optional) advanced issues with planning (optional but recommended)




    review questions