lectures.alex.balgavy.eu

Lecture notes from university.
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      1 = Assessment info =
      2 check learning goals on canvas.
      3 
      4 everything in working groups (this means go through the sheets again)
      5   * informed search (DF, BF, DFID)
      6   * uninformed search (Hill Climbing, BF, A, A*)
      7   * adversarial search (minimax with alpha-beta)
      8   * logical representations
      9   * DPLL
     10   * uncertainty representations
     11   * Bayesian learning
     12   * NN/Deep learning
     13 
     14 research procedure
     15   * take at least 4 bots you implemented
     16   * compare performance -- play against each other, in different environments
     17   * study results: outperforming, speed
     18   * define interesting hypotheses and research questions, use analysis to verify/falsify them
     19 
     20 scientific paper structure:
     21   * title page with abstract
     22     * title and authors
     23     * abstract of 2-3 paragraphs
     24   * introduction: intro to problem, solution, some results (2 pages)
     25   * background info
     26     * describe game, challenge, IS framework, whatever else is needed (1-2 pages)
     27   * research question
     28     * describe approach
     29     * what are:
     30       * possible outcomes of setup and contribution
     31       * e.g. whether one method works, whether it works better than others
     32     * also, define "working better"
     33   * experimental setup (2 pages)
     34     * explain how experiments were set up
     35     * what you did in terms of implementation
     36     * compare different methods
     37     * define metrics
     38   * results (2 pages)
     39     * describe results in overview tables
     40     * point reader to most significant, interesting results
     41   * findings
     42   * conclusions
     43