lectures.alex.balgavy.eu

Lecture notes from university.
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assessment-info.md (1496B)


      1 +++
      2 title = "Assessment info"
      3 +++
      4 # Assessment info
      5 check learning goals on canvas.
      6 
      7 everything in working groups (this means go through the sheets again)
      8 * informed search (DF, BF, DFID)
      9 * uninformed search (Hill Climbing, BF, A, A*)
     10 * adversarial search (minimax with alpha-beta)
     11 * logical representations
     12 * DPLL
     13 * uncertainty representations
     14 * Bayesian learning
     15 * NN/Deep learning
     16 
     17 research procedure
     18   * take at least 4 bots you implemented
     19   * compare performance -- play against each other, in different environments
     20   * study results: outperforming, speed
     21   * define interesting hypotheses and research questions, use analysis to verify/falsify them
     22 
     23 scientific paper structure:
     24   * title page with abstract
     25     * title and authors
     26     * abstract of 2-3 paragraphs
     27   * introduction: intro to problem, solution, some results (2 pages)
     28   * background info
     29     * describe game, challenge, IS framework, whatever else is needed (1-2 pages)
     30   * research question
     31     * describe approach
     32     * what are:
     33       * possible outcomes of setup and contribution
     34       * e.g. whether one method works, whether it works better than others
     35     * also, define "working better"
     36   * experimental setup (2 pages)
     37     * explain how experiments were set up
     38     * what you did in terms of implementation
     39     * compare different methods
     40     * define metrics
     41   * results (2 pages)
     42     * describe results in overview tables
     43     * point reader to most significant, interesting results
     44   * findings
     45   * conclusions
     46